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General General

COVID-19 and cyberbullying: deep ensemble model to identify cyberbullying from code-switched languages during the pandemic.

In Multimedia tools and applications

It has been declared by the World Health Organization (WHO) the novel coronavirus a global pandemic due to an exponential spread in COVID-19 in the past months reaching over 100 million cases and resulting in approximately 3 million deaths worldwide. Amid this pandemic, identification of cyberbullying has become a more evolving area of research over posts or comments in social media platforms. In multilingual societies like India, code-switched texts comprise the majority of the Internet. Identifying the online bullying of the code-switched user is bit challenging than monolingual cases. As a first step towards enabling the development of approaches for cyberbullying detection, we developed a new code-switched dataset, collected from Twitter utterances annotated with binary labels. To demonstrate the utility of the proposed dataset, we build different machine learning (Support Vector Machine & Logistic Regression) and deep learning (Multilayer Perceptron, Convolution Neural Network, BiLSTM, BERT) algorithms to detect cyberbullying of English-Hindi (En-Hi) code-switched text. Our proposed model integrates different hand-crafted features and is enriched by sequential and semantic patterns generated by different state-of-the-art deep neural network models. Initial experimental results of the proposed deep ensemble model on our code-switched data reveal that our approach yields state-of-the-art results, i.e., 0.93 in terms of macro-averaged F1 score. The dataset and codes of the present study will be made publicly available on the paper's companion repository [https://github.com/95sayanta/COVID-19-and-Cyberbullying].

Paul Sayanta, Saha Sriparna, Singh Jyoti Prakash

2022-Jan-08

Code-switched language, Cyberbullying, Deep ensemble, Natural language processing

General General

Predicting the impact of online news articles - is information necessary?: Application to COVID-19 articles.

In Multimedia tools and applications

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features - ranging from the semantic relations through readability measures to the article's digital content - are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

Preiss Judita

2022-Jan-08

Grammatical relations, Popularity prediction, SemRep relations, Twitter

General General

Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach.

In Neural computing & applications

In medical practice, all decisions, as for example the diagnosis based on the classification of images, must be made reliably and effectively. The possibility of having automatic tools helping doctors in performing these important decisions is highly welcome. Artificial Intelligence techniques, and in particular Deep Learning methods, have proven very effective on these tasks, with excellent performance in terms of classification accuracy. The problem with such methods is that they represent black boxes, so they do not provide users with an explanation of the reasons for their decisions. Confidence from medical experts in clinical decisions can increase if they receive from Artificial Intelligence tools interpretable output under the form of, e.g., explanations in natural language or visualized information. This way, the system outcome can be critically assessed by them, and they can evaluate the trustworthiness of the results. In this paper, we propose a new general-purpose method that relies on interpretability ideas. The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19.

De Falco Ivanoe, De Pietro Giuseppe, Sannino Giovanna

2022-Jan-08

Chest X-ray images, Classification, Covid-19 disease, Evolutionary algorithms, Interpretable machine learning

General General

Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks.

In Wireless personal communications

Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because it is a fast and low-cost way. Certain above mentioned things can be recognized by chest X-ray images that plays a significant role and also used for examining in detection of CORONA VIRUS(COVID-19). Here radiological chest X-rays are easily available with low cost only. In this survey paper, Convolutional Neural Network(CNN) based solution that will benefit in detection of the Covid-19 positive patients using radiography chest X-Ray images. To test the efficiency of the solution, using data sets of publicly available X-Ray images of Corona virus positive cases and negative cases. Images of positive Corona Virus patients and pictures of healthy person images are divided into testing images and trainable images. The solution which are providing the good results with classification accuracy within the test set-up. Then GUI based application supports for medical examination areas. This GUI application can be used on any computer and performed by any medical examiner or technician to determine Corona Virus positive patients using radiography X-ray images. The result will be precisely obtaining the Covid-19 Patient analysis through the chest X-ray images and also results may be retrieve within a few seconds.

Thirukrishna J T, Krishna Sanda Reddy Sai, Shashank Policherla, Srikanth S, Raghu V

2022-Jan-08

CNN, Convolutional neural networks, Deep CNN, Deep learning, Detection

General General

NetNMSP: Nonoverlapping maximal sequential pattern mining.

In Applied intelligence (Dordrecht, Netherlands)

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

Li Yan, Zhang Shuai, Guo Lei, Liu Jing, Wu Youxi, Wu Xindong

2022-Jan-10

Backtracking strategy, COVID-19, Gap constraint, MERS-CoV, Maximal pattern mining, Nonoverlapping pattern mining, Sequential pattern mining

General General

DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach.

In New generation computing

The new type of coronavirus disease, which has spread from Wuhan, China since the beginning of 2020 called COVID-19, has caused many deaths and cases in most countries and has reached a global pandemic scale. In addition to test kits, imaging techniques with X-rays used in lung patients have been frequently used in the detection of COVID-19 cases. In the proposed method, a novel approach based on a deep learning model named DeepCovNet was utilized to classify chest X-ray images containing COVID-19, normal (healthy), and pneumonia classes. The convolutional-autoencoder model, which had convolutional layers in encoder and decoder blocks, was trained by using the processed chest X-ray images from scratch for deep feature extraction. The distinctive features were selected with a novel and robust algorithm named SDAR from the deep feature set. In the classification stage, an SVM classifier with various kernel functions was used to evaluate the classification performance of the proposed method. Also, hyperparameters of the SVM classifier were optimized with the Bayesian algorithm for increasing classification accuracy. Specificity, sensitivity, precision, and F-score, were also used as performance metrics in addition to accuracy which was used as the main criterion. The proposed method with an accuracy of 99.75 outperformed the other approaches based on deep learning.

Demir Fatih, Demir Kürşat, Şengür Abdulkadir

2022-Jan-12

Bayesian algorithm, COVID-19, Convolutional-autoencoder model, Feature selection

General General

A Transformer-Based Model for Evaluation of Information Relevance in Online Social-Media: A Case Study of Covid-19 Media Posts.

In New generation computing

Online social media has become a major source of information gathering for a huge section of society. As the amount of information flows in online social media is enormous but on the other hand, the fact-checking sources are limited. This shortfall of fact-checking gives birth to the problem of misinformation and disinformation in the case of the truthfulness of facts on online social media which can have serious effects on the wellbeing of society. This problem of misconception becomes more rapid and critical when some events like the recent outbreak of Covid-19 happen when there is no or very little information is available anywhere. In this scenario, the identification of the content available online which is mostly propagated from person to person and not by any governing authority is very needed at the hour. To solve this problem, the information available online should be verified properly before being conceived by any individual. We propose a scheme to classify the online social media posts (Tweets) with the help of the BERT (Bidirectional Encoder Representations from Transformers)-based model. Also, we compared the performance of the proposed approach with the other machine learning techniques and other State of the art techniques available. The proposed model not only classifies the tweets as relevant or irrelevant, but also creates a set of topics by which one can identify a text as relevant or irrelevant to his/her need just by just matching the keywords of the topic. To accomplish this task, after the classification of the tweets, we apply a possible topic modelling approach based on latent semantic analysis and latent Dirichlet allocation methods to identify which of the topics are mostly propagated as false information.

Sharma Utkarsh, Pandey Prateek, Kumar Shishir

2022-Jan-10

BERT, Covid-19, Online social media, Text mining, Tweet classification

General General

Segmentation and classification on chest radiography: a systematic survey.

In The Visual computer

Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. This leads to the need for computer-aided detection diagnosis. For decades, researchers were automatically detecting pulmonary disorders using the traditional computer vision (CV) methods. Now the availability of large annotated datasets and computing hardware has made it possible for deep learning to dominate the area. It is now the modus operandi for feature extraction, segmentation, detection, and classification tasks in medical imaging analysis. This paper focuses on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets. The studies performed using the Generative Adversarial Network (GAN) models for segmentation and classification on chest X-rays are also included in this study. GAN has gained the interest of the CV community as it can help with medical data scarcity. In this study, we have also included the research conducted before the popularity of deep learning models to have a clear picture of the field. Many surveys have been published, but none of them is dedicated to chest X-rays. This study will help the readers to know about the existing techniques, approaches, and their significance.

Agrawal Tarun, Choudhary Prakash

2022-Jan-08

Computer vision, Deep convolutional neural network, GAN, Lung segmentation, Multiclass classification, Nodule, TB, COVID-19, Pneumothorax detection

Public Health Public Health

The use of cremation data for timely mortality surveillance: the example of the COVID-19 pandemic in Ontario, Canada.

In JMIR public health and surveillance

BACKGROUND : Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions, including across Canada. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating real-time mortality data that encompasses the majority of deaths within the province.

OBJECTIVE : This study aimed to validate the use of cremation data as a more timely surveillance tool for all-cause mortality during a public health emergency in a jurisdiction with delays in vital statistics data. Specifically, this study aimed to validate this surveillance tool by determining the stability, timeliness, and robustness of its real-time estimation of all-cause mortality.

METHODS : Cremation records from January 2020 until April 2021 were compared to the historical records from 2017-2019, grouped according to week, age, sex, and COVID-19 status. Cremation data were compared to Ontario's provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years (2017-2019) to determine whether there was excess mortality within various age groups and whether deaths attributed to COVID-19 account for the entirety of the excess mortality.

RESULTS : Between 2017-2019, cremations were performed for 67.4% (95% CI: 67.3-67.5%) of deaths; the proportion of cremated deaths remained stable throughout 2020, even within age and sex categories. Cremation records are 99% complete within three weeks of the data of death, which precedes the compilation of vital statistics data by several months. Consequently, during the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI: 14.6-19.3%) in all-cause mortality, a finding which was confirmed several months later with cremation data.

CONCLUSIONS : The percent of Ontarians cremated and the completion of cremation data several months before vital statistics did not change meaningfully during the COVID-19 pandemic period, establishing that the pandemic did not significantly alter cremation practices. Cremation data can be used to accurately estimate all-cause mortality in near real-time, particularly when real-time mortality estimates are needed to inform policy decisions for public health measures. The accuracy of this excess mortality estimation was confirmed by comparing it with official vital statistics data. These findings demonstrate the utility of cremation data as a complementary data source for timely mortality information during public health emergencies.

CLINICALTRIAL :

Postill Gemma, Murray Regan, Wilton Andrew S, Wells Richard A, Sirbu Renee, Daley Mark J, Rosella Laura

2022-Jan-06

General General

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.

In eLife

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

Timmons James A, Anighoro Andrew, Brogan Robert J, Stahl Jack, Wahlestedt Claes, Farquhar David Gordon, Taylor-King Jake, Volmar Claude-Henry, Kraus William E, Phillips Stuart M

2022-Jan-17

COVID-19, Diabetes, Exercise, Transcriptomics, computational biology, deep learning, drug repurposing, human, insulin biology, medicine, systems biology

Public Health Public Health

Molecular docking, molecular dynamics simulation and MM-GBSA studies of the activity of glycyrrhizin relevant substructures on SARS-CoV-2 RNA-dependent-RNA polymerase.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is the causative agent of Coronavirus Disease (COVID-19), which is a life-threatening disease. The World Health Organization has classified COVID-19 as a severe worldwide public health pandemic due to its high death rate, quick transmission, and lack of medicines. To counteract the recurrence of the severe acute respiratory syndrome, active antiviral medications are urgently required. Glycyrrhizin was documented with activity on different viral proteins, including SARS-CoV-2; in this study, the activity of glycyrrhizin and its substructures (604 molecules) were screened on SARS-CoV-2 RNA-dependent-RNA polymerase using molecular docking, molecular dynamic (MD) simulation, and MM/GBSA. Sixteen molecules exhibited docking energy higher than -7 kcal/mol; four compounds (10772603, 101088272, 154730753 and glycyrrhizin) showed the highest binding energy, and good stability during MD simulation. The glycyrrhizin compound exhibited favorable docking energy (-7.9 kcal/mol), and it was the most stable complex during MD simulation. The predicted binding free energy of the glycyrrhizin complex was -57 ± 8 kcal/mol. These findings suggest that this molecule, after more validation, could become a good candidate for developing and manufacturing an anti-SARS-CoV-2 medication.Communicated by Ramaswamy H. Sarma.

Zamzami Mazin A

2022-Jan-17

Coronavirus, RdRp, SARS-CoV-2, antiviral agent, molecular docking; natural compound

Internal Medicine Internal Medicine

The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach.

In Current medical research and opinion

BACKGROUND : The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19.

METHODS : Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on a discriminant analysis and GMM, and compare these clusters.

RESULTS : A total of 16,455 inpatients (57·4% women and 42·6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as chronic obstructive pulmonary disease; it was the cluster with the worst prognosis.

CONCLUSION : The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19.

Arévalo-Lorido José Carlos, Carretero-Gómez Juana, Casas-Rojo Jose Manuel, Antón-Santos Juan Miguel, Melero-Bermejo José Antonio, López-Carmona Maria Dolores, Palacios Lidia Cobos, Sanz-Cánovas Jaime, Pesqueira-Fontán Paula Maria, de la Peña-Fernández Andrés Alberto, de la Sierra Alcántara Navas-Maria, García-García Gema Maria, Torres Peña José David, Magallanes-Gamboa Jeffrey Oskar, Fernández-Madera-Martinez Rosa, Fernández-Fernández Javier, Rubio-Rivas Manuel, Maestro-de la Calle Guillermo, Cervilla-Muñoz Eva, Ramos-Martínez Antonio, Méndez-Bailón Manuel, Ramos-Rincón José Manuel, Gómez-Huelgas Ricardo

2022-Jan-17

COVID-19, Cluster analysis, Comorbidity, Machine learning, SARS-CoV-2

Pathology Pathology

Optimising predictive models to prioritise viral discovery in zoonotic reservoirs.

In The Lancet. Microbe

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Becker Daniel J, Albery Gregory F, Sjodin Anna R, Poisot Timothée, Bergner Laura M, Chen Binqi, Cohen Lily E, Dallas Tad A, Eskew Evan A, Fagre Anna C, Farrell Maxwell J, Guth Sarah, Han Barbara A, Simmons Nancy B, Stock Michiel, Teeling Emma C, Carlson Colin J

2022-Jan-10

Surgery Surgery

'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics.

In Biosensors & bioelectronics: X

Point of care (PoC) devices are highly demanding to control current pandemic, originated from severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Though nucleic acid-based methods such as RT-PCR are widely available, they require sample preparation and long processing time. PoC diagnostic devices provide relatively faster and stable results. However they require further investigation to provide high accuracy and be adaptable for the new variants. In this study, laser-scribed graphene (LSG) sensors are coupled with gold nanoparticles (AuNPs) as stable promising biosensing platforms. Angiotensin Converting Enzyme 2 (ACE2), an enzymatic receptor, is chosen to be the biorecognition unit due to its high binding affinity towards spike proteins as a key-lock model. The sensor was integrated to a homemade and portable potentistat device, wirelessly connected to a smartphone having a customized application for easy operation. LODs of 5.14 and 2.09 ng/mL was achieved for S1 and S2 protein in the linear range of 1.0-200 ng/mL, respectively. Clinical study has been conducted with nasopharyngeal swabs from 63 patients having alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2) variants, patients without mutation and negative patients. A machine learning model was developed with accuracy of 99.37% for the identification of the SARS-Cov-2 variants under 1 min. With the increasing need for rapid and improved disease diagnosis and monitoring, the PoC platform proved its potential for real time monitoring by providing accurate and fast variant identification without any expertise and pre sample preparation, which is exactly what societies need in this time of pandemic.

Beduk Duygu, Ilton de Oliveira Filho José, Beduk Tutku, Harmanci Duygu, Zihnioglu Figen, Cicek Candan, Sertoz Ruchan, Arda Bilgin, Goksel Tuncay, Turhan Kutsal, Salama Khaled Nabil, Timur Suna

2022-May

COVID-19, Laser-scribed graphene, Machine learning, Point-of-care, SARS-CoV-2, Sensor

General General

Hybrid PSO-SVM algorithm for Covid-19 screening and quantification.

In International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization-Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19. This paper presents a model for training the system to segregate and classify the presence of pneumonia which will in turn save around 50% of the time frame for physicians. This will be especially useful in places of outbreaks where a team of people are working together with the aid of artificial intelligence and/or medical background. The AI incorporated system was distributed in all areas of across the globe. It has been observed that challenges such as data security, testing time effectiveness of model, data discrepancy etc. were positively handled using the deployed system. Moreover, since the AI integrated system identifies the infected patients immediately physicians can confirm the infection and segregate the patients at the right period. A total of 200 training cases have been observed of which 150 were identified to be infected. The proposed work shows specificity of 0.85, a sensitivity of 0.956 and an accuracy of 95.78%.

Sheela M Sahaya, Arun C A

2022-Jan-12

Artificial Intelligence, COVID-19, Magnetic Resonance Imaging, Particle Swarm Optimization, Support Vector Machine

Public Health Public Health

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.

In PeerJ. Computer science

Background : Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread.

Methods : In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic.

Results : Statistical measures-Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)-are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

Naeem Muhammad, Yu Jian, Aamir Muhammad, Khan Sajjad Ahmad, Adeleye Olayinka, Khan Zardad

2021

ARDL, Artificial neural network, Covid-19, Forecasting, Machine learning

Surgery Surgery

Assessment of General Populations Knowledge, Attitude, and Perceptions Toward the Coronavirus Disease (COVID-19): A Cross-Sectional Study From Pakistan.

In Frontiers in medicine

Background: Coronavirus disease 2019 (COVID-19) is a global health threat and caused a universal psychosocial impact on the general population. Therefore, the knowledge, attitude, and perceptions (KAPs) of the general population are critical for the development and effective implementation of standard operating procedures (SOP) to contain the contagion and minimize the losses. Therefore, the current study was conducted to understand and evaluate the KAPs of Pakistani populations toward the COVID-19. Methods: An online cross-sectional study was carried out among participants from 1 May to 30 July 2020 in different areas of Pakistan. The respondents of the study were the general population with age ≥ 18 years. The poll URL was posted on several channels after a call for participation. Other social media platforms such as WeChat, WhatsApp, Facebook, Twitter, Instagram, Messenger, and LinkedIn were engaged to maximize general population engagement. The questionnaire included details about sociodemographic, knowledge about COVID-19, perceptions toward universal safety precautions of COVID-19, and beliefs attitude toward the COVID-19. The obtained data were exported into a Microsoft Excel spreadsheet and SPSS software version 21 for windows. The descriptive statistics values were presented in frequencies and percentages. Binary logistic regression, Chi-square test, and one-way ANOVA were applied to analyze the participants' socio-demographic characteristics and variables related to KAPs. P-value < 0.05 was recorded as significant. Results: A total of 1,000 participants were invited of which 734 participated in this study. The response rate was 73.4% (734/1,000). The gender, marital status, education, and residence showed a significant association with the knowledge score. The majority of the study participants were thinking that COVID-19 may be more dangerous in elderly individuals 94.5% (n = 700), and individuals with chronic diseases or severe complications 96.7% (n = 710) (p = 0.00). More than half of the participants 52.5% (n = 385) showed their concern that either they or their family members might get the infection. More than 98% (n = 703), (P-value = 0.00) of the participants held that COVID-19 would be successfully controlled in Pakistan by following the standard SOPs and government guidelines. Conclusion: This study showed that the general population of Pakistan has good awareness and reasonable attitudes and perceptions toward the full features of the COVID-19. The current study suggests that mass-level effective health education programs are necessary for developing countries to improve and limit the gap between KAP toward COVID-19.

Khattak Saadullah, Khan Maqbool, Usman Tahir, Ali Johar, Wu Dong-Xing, Jahangir Muhammad, Haleem Kashif, Muhammad Pir, Rauf Mohd Ahmar, Saddique Kamran, Khan Nazeer Hussain, Li Tao, Wu Dong-Dong, Ji Xin-Ying

2021

Pakistan, attitude, coronavirus disease 2019, knowledge, perceptions

General General

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy.

In Environmental health : a global access science source

BACKGROUND : Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities.

METHODS : NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose-response function on the counterfactual concentrations of 10 μg/m3.

RESULTS : The Land Use Random Forest models were able to capture 41-42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604.

CONCLUSIONS : Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

Boniardi Luca, Nobile Federica, Stafoggia Massimo, Michelozzi Paola, Ancona Carla

2022-Jan-16

Air pollution, COVID-19, Citizen science, Health Impact Assessment, Machine Learning

General General

Prediction and Evaluation of healthy and unhealthy status of COVID-19 patients using wearable device prototype data.

In MethodsX

COVID-19 pandemic seriousness is making the whole world suffer due to inefficient medication and vaccines. The article prediction analysis is carried out with the dataset downloaded from the Application peripheral interface (API) designed explicitly for COVID-19 quarantined patients. The measured data is collected from a wearable device used for quarantined healthy and unhealthy patients. The wearable device provides data of temperature, heart rate, SPO2, blood saturation, and blood pressure timely for alerting the medical authorities and providing a better diagnosis and treatment. The dataset contains 1085 patients with eight features representing 490 COVID-19 infected and 595 standard cases. The work considers different parameters, namely heart rate, temperature, SpO2, bpm parameters, and health status. Furthermore, the real-time data collected can predict the health status of patients as infected and non-infected from measured parameters. The collected dataset uses a random forest classifier with linear and polynomial regression to train and validate COVID-19 patient data. The google colab is an Integral development environment inbuilt with python and Jupyter notebook with scikit-learn version 0.22.1 virtually tested on cloud coding tools. The dataset is trained and tested in 80% and 20% ratio for accuracy evaluation and avoid overfitting in the model. This analysis could help medical authorities and governmental agencies of every country respond timely and reduce the contamination of the disease.•The measured data provide a comprehensive mapping of disease symptoms to predict the health status. They can restrict the virus transmission and take necessary steps to control, mitigate and manage the disease.•Benefits in scientific research with Artificial Intelligence (AI) to tackle the hurdles in analyzing disease diagnosis.•The diagnosis results of disease symptoms can identify the severity of the patient to monitor and manage the difficulties for the outbreak caused.

Hussain Shaik Asif, Bassam Nizar Al, Zayegh Amer, Ghawi Sana Al

2022-Jan-10

AI model, Dataset, Healthcare, Pandemic, Quarantine, Wearable electronic device

General General

COVID-19 Mandatory self-quarantine wearable device for authority monitoring with edge AI reporting & flagging system.

In Health and technology

A mandatory self-quarantine is necessary for those who return from overseas or any red zone areas. It is important that the self-quarantine is conducted without the non-adherence issue occurring and causes the self-quarantine individual to be the carrier of the COVID-19 in the community. To navigate and resolve this issue, most countries have implemented a series of COVID-19 monitoring and tracing systems. However, there are some restrictions and limitation which can lead to intentional non-adherence. The quarantined individuals can still travel within the community by removing the wristband or simply providing an incorrect contact status in the tracing application. In this paper, a novel configuration for mandatory self-quarantine system is proposed. It will enable interaction between the wearable and contact tracing technologies to ensure that the authorities have total control of the system. The hardware of the proposed system in the wearable device is low in cost, lightweight and safe to use for the next user after the quarantine is completed. The software (software and database) that linked between the quarantine user and normal user utilizes edge artificial intelligence (AI) for reporting and flagging mechanisms.

Lim Wei Jie, Abdul Ghani N M

2022-Jan-10

COVID-19, Contact tracing, Edge artificial intelligence (AI), Quarantine monitoring

General General

Performance Prediction of Listed Companies in Smart Healthcare Industry: Based on Machine Learning Algorithms.

In Journal of healthcare engineering

With the development of wireless network, communication technology, cloud platform, and Internet of Things (IOT), new technologies are gradually applied to the smart healthcare industry. The COVID-19 outbreak has brought more attention to the development of the emerging industry of smart healthcare. However, the development of this industry is restricted by factors such as long construction cycle, large investment in the early stage, and lagging return, and the listed companies also face the problem of financing difficulties. In this study, machine learning algorithm is used to predict performance, which can not only deal with a large amount of data and characteristic variables but also analyse different types of variables and predict their classification, increasing the stability and accuracy of the model and helping to solve the problem of poor performance prediction in the past. After analysing the sample data from 53 listed companies in smart healthcare industry, we argued that the conclusion of this study can not only provide reference for listed companies in smart healthcare industry to formulate their own strategies but also provide shareholders with strategies to avoid risks and help the development of this emerging industry.

Dong Baobao, Wang Xiangming, Cao Qi

2022

General General

CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection.

In Journal of healthcare engineering

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network. Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.

Lin Cong, Zheng Yongbin, Xiao Xiuchun, Lin Jialun

2022

General General

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images.

In Cognitive computation

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

Tahir Anas M, Qiblawey Yazan, Khandakar Amith, Rahman Tawsifur, Khurshid Uzair, Musharavati Farayi, Islam M T, Kiranyaz Serkan, Al-Maadeed Somaya, Chowdhury Muhammad E H

2022-Jan-11

COVID-19 pneumonia, Computer-aided diagnostic tool, Deep convolutional neural networks, MERS, SARS, Transfer learning

General General

Real time deep learning framework to monitor social distancing using improved single shot detector based on overhead position.

In Earth science informatics

The current COVID 19 halo infection has caused a severe catastrophe with its deadly spread. Despite the implementation of the vaccine, the severity of the infection has not diminished, and it has become stronger and more destructive. So, the only solution to protect ourselves from infection is social-distancing. Although social-distancing has been in practice for a long time, in most places it is not effectively followed, and it is very difficult to find out manually at all times whether people are following it or not. Therefore, we introduced a newly developed framework of deep-learning technique to automatically identify whether people maintain social-distancing or not using remote sensing top view images. Initially, we are detecting the context of image which includes information about the environment. Our detection model recognizes individuals using the boundary box. Then centroid is determined over every detected boundary box. By means of applying Euclidean distance, the pair range distances of the detected boundary box centroid are determined. To evaluate whether the distance measurement exceeds the minimum social distance limit, the violation threshold is established. We used Improved Single Shot Detector model for detecting a person over an image. Experiments are carried out on widely collected remote sensing images from various environments. Based on the object detection algorithm of deep learning, a variety of performance metrics are compared to evaluate the efficiency of the proposed model. Research outcome shows that, our proposed model outperforms well while recognize and detect a person in a well excellent way.

Gopal Bharathi, Ganesan Anandharaj

2022-Jan-11

Boundary box, Centroid, Deep learning, Euclidean distance, SSD, Threshold

General General

Blockchain-Based Digital Twins Collaboration for Smart Pandemic Alerting: Decentralized COVID-19 Pandemic Alerting Use Case.

In Computational intelligence and neuroscience

Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.

Sahal Radhya, Alsamhi Saeed H, Brown Kenneth N, O’Shea Donna, Alouffi Bader

2022

Radiology Radiology

Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles.

In Frontiers in neuroscience ; h5-index 72.0

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.

Kofler Florian, Ezhov Ivan, Fidon Lucas, Pirkl Carolin M, Paetzold Johannes C, Burian Egon, Pati Sarthak, El Husseini Malek, Navarro Fernando, Shit Suprosanna, Kirschke Jan, Bakas Spyridon, Zimmer Claus, Wiestler Benedikt, Menze Bjoern H

2021

CT, MR, OOD, anomaly detection, ensembling, failure prediction, fusion, quality estimation

General General

Achieving data privacy for decision support systems in times of massive data sharing.

In Cluster computing

The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient's data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients' data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

Fazal Rabeeha, Shah Munam Ali, Khattak Hasan Ali, Rauf Hafiz Tayyab, Al-Turjman Fadi

2022-Jan-10

Blowfish, Data masking, Data privacy, Encryption, Identity data, Non-sensitive data, Sensitive data

General General

RALF: an adaptive reinforcement learning framework for teaching dyslexic students.

In Multimedia tools and applications

Dyslexia is a learning disorder in which individuals have significant reading difficulties. Previous studies found that using machine learning techniques in content supplements is vital in adapting the course concepts to the learners' educational level. However, to the best of our knowledge, no research objectively applied machine learning methods to adaptive content generation. This study introduces an adaptive reinforcement learning framework known as RALF through Cellular Learning Automata (CLA) to generate content automatically for students with dyslexia. At first, RALF generates online alphabet models as a simplified font. CLA structure learns each rule of character generation through the reinforcement learning cycle asynchronously. Second, Persian words are generated algorithmically. This process also considers each character's state to decide the alphabet cursiveness and the cells' response to the environment. Finally, RALF can generate long texts and sentences using the embedded word-formation algorithm. The spaces between words are proceeds through the CLA neighboring states. Besides, RALF provides word pronunciation and several exams and games to improve the learning performance of people with dyslexia. The proposed reinforcement learning tool enhances students' learning rate with dyslexia by almost 27% compared to the face-to-face approach. The findings of this research show the applicability of this approach in dyslexia treatment during Lockdown of COVID-19.

Minoofam Seyyed Amir Hadi, Bastanfard Azam, Keyvanpour Mohammad Reza

2022-Jan-10

Educational multimedia, Integrated communication, Intelligent tutoring system, Orthographic knowledge, Pandemic crisis

General General

DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction.

In Computers in biology and medicine

Drug-target interaction (DTI) prediction reduces the cost and time of drug development, and plays a vital role in drug discovery. However, most of research does not fully explore the molecular structures of drug compounds in DTI prediction. To this end, we propose a deep learning model to capture the molecular structure information of drug compounds for DTI prediction. This model utilizes a transformer network incorporating multilayer graph information, which captures the features of a drug's molecular structure so that the interactions between atoms of drug compounds can be explored more deeply. At the same time, a convolutional neural network is employed to capture the local residue information in the target sequence, and effectively extract the feature information of the target. The experiments on the DrugBank dataset showed that the proposed model outperformed previous models based on the structure of target sequences. The results indicate that the improved transformer network fuses the feature information between layers in the graph convolutional neural network and extracts the interaction data for the molecular structure. The drug repositioning experiment on COVID-19 and Alzheimer's disease demonstrated the proposed model's ability to find therapeutic drugs in drug discovery. The code of our model is available at https://github.com/zhangpl109/DeepMGT-DTI.

Zhang Peiliang, Wei Ziqi, Che Chao, Jin Bo

2022-Jan-05

COVID-19, DTI, Drug repositioning, Multilayer graph information, Transformer networks

oncology Oncology

VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds.

In Computers in biology and medicine

The coronavirus disease 2019 (COVID-19) has severely stressed the sanitary systems of all countries in the world. One of the main issues that physicians are called to tackle is represented by the monitoring of pauci-symptomatic COVID-19 patients at home and, generally speaking, everyone the access to the hospital might or should be severely reduced. Indeed, the early detection of interstitial pneumonia is particularly relevant for the survival of these patients. Recent studies on rheumatoid arthritis and interstitial lung diseases have shown that pathological pulmonary sounds can be automatically detected by suitably developed algorithms. The scope of this preliminary work consists of proving that the pathological lung sounds evidenced in patients affected by COVID-19 pneumonia can be automatically detected as well by the same class of algorithms. In particular the software VECTOR, suitably devised for interstitial lung diseases, has been employed to process the lung sounds of 28 patient recorded in the emergency room at the university hospital of Modena (Italy) during December 2020. The performance of VECTOR has been compared with diagnostic techniques based on imaging, namely lung ultrasound, chest X-ray and high resolution computed tomography, which have been assumed as ground truth. The results have evidenced a surprising overall diagnostic accuracy of 75% even if the staff of the emergency room has not been suitably trained for lung auscultation and the parameters of the software have not been optimized to detect interstitial pneumonia. These results pave the way to a new approach for monitoring the pulmonary implication in pauci-symptomatic COVID-19 patients.

Pancaldi Fabrizio, Pezzuto Giuseppe Stefano, Cassone Giulia, Morelli Marianna, Manfredi Andreina, D’Arienzo Matteo, Vacchi Caterina, Savorani Fulvio, Vinci Giovanni, Barsotti Francesco, Mascia Maria Teresa, Salvarani Carlo, Sebastiani Marco

2022-Jan-06

Audio processing, COVID-19, Electronic stethoscope, Interstitial pneumonia, Lung sounds, SARS-CoV-2, VECTOR

General General

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients.

In Frontiers in psychology ; h5-index 92.0

The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.

Ćosić Krešimir, Popović Siniša, Šarlija Marko, Kesedžić Ivan, Gambiraža Mate, Dropuljić Branimir, Mijić Igor, Henigsberg Neven, Jovanovic Tanja

2021

artificial intelligence, ex-COVID-19 patients, facial/oculometric features, mental health disorders, neurophysiological features, prediction and prevention, semantic/acoustic features

General General

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.

In Computers in biology and medicine

The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) applications, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

Attallah Omneya

2022-Jan-05

COVID-19, Convolutional neural networks (CNN), Deep learning, Discrete wavelet transform (DWT), ECG trace Image

General General

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.

In Computers in biology and medicine

Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Intelligence. Recent advances in visual recognition are based on image classification and artefacts detection within these images. The purpose of this study is to classify chest X-ray images of COVID-19 artefacts in changed real-world situations. A novel Bayesian optimization-based convolutional neural network (CNN) model is proposed for the recognition of chest X-ray images. The proposed model has two main components. The first one utilizes CNN to extract and learn deep features. The second component is a Bayesian-based optimizer that is used to tune the CNN hyperparameters according to an objective function. The used large-scale and balanced dataset comprises 10,848 images (i.e., 3616 COVID-19, 3616 normal cases, and 3616 Pneumonia). In the first ablation investigation, we compared Bayesian optimization to three distinct ablation scenarios. We used convergence charts and accuracy to compare the three scenarios. We noticed that the Bayesian search-derived optimal architecture achieved 96% accuracy. To assist qualitative researchers, address their research questions in a methodologically sound manner, a comparison of research method and theme analysis methods was provided. The suggested model is shown to be more trustworthy and accurate in real world.

Loey Mohamed, El-Sappagh Shaker, Mirjalili Seyedali

2022-Jan-05

Bayesian optimization, COVID-19, Convolutional neural network, Deep learning, Image classification, Optimization

General General

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.

In PloS one ; h5-index 176.0

This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein that relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

Alkhodari Mohanad, Khandoker Ahsan H

2022

General General

Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms.

In Financial innovation

Some countries have announced national benchmark rates, while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and the COVID-19 pandemic are considered by using daily data between December 28, 2018, and December 31, 2020, by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (1) the most significant determinant is the amount of securities bought by Central Banks; (2) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (3) Random Forest is the most accurate prediction model. Taking action by considering the study's findings can help support economic growth by achieving low-level benchmark rates.

Depren Özer, Kartal Mustafa Tevfik, Kılıç Depren Serpil

2021

Benchmark rate, Determinants, Machine learning algorithms, Turkey

General General

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules.

In Chemical science

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Gentile Francesco, Fernandez Michael, Ban Fuqiang, Ton Anh-Tien, Mslati Hazem, Perez Carl F, Leblanc Eric, Yaacoub Jean Charle, Gleave James, Stern Abraham, Wong Bill, Jean François, Strynadka Natalie, Cherkasov Artem

2021-Dec-15

General General

A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept.

In Computer networks

The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial-terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach.

Nasser Nidal, Fadlullah Zubair Md, Fouda Mostafa M, Ali Asmaa, Imran Muhammad

2021-Dec-20

5G, Artificial intelligence (AI), Beyond 5G (B5G), Edge computing, Federated learning, Pandemic, Unmanned aerial vehicle (UAV)

General General

PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients.

In Aging ; h5-index 49.0

Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for corticosteroid therapy. In this study, we aimed to propose a machine learning model to forecast the response to corticosteroid therapy in COVID-19 patients. We retrospectively collected the clinical data about 666 COVID-19 patients receiving corticosteroid therapy between January 27, 2020, and March 30, 2020, from two hospitals in China. The response to corticosteroid therapy was evaluated by hospitalization time, oxygen supply duration, and the outcomes of patients. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five prediction models were applied in the training cohort and assessed in an internal and an external validation dataset, respectively. Finally, two (C reactive protein, lymphocyte percent) of 36 candidate immune/inflammatory features were finally used for model development. All five models displayed promising predictive performance. Notably, the ensemble model, PRCTC (prediction of response to corticosteroid therapy in COVID-19 patients), derived from three prediction models including Gradient Boosted Decision Tree (GBDT), Neural Network (NN), and logistic regression (LR), achieved the best performance with an area under the curve (AUC) of 0.810 (95% confidence interval [CI] 0.760-0.861) in internal validation cohort and 0.845 (95% CI 0.779-0.911) in external validation cohort to predict patients' response to corticosteroid therapy. In conclusion, PRCTC proposed with universality and scalability is hopeful to provide tangible and prompt clinical decision support in management of COVID-19 patients and potentially extends to other medication predictions.

Gao Yue, Xiong Xiaoming, Jiao Xiaofei, Yu Yang, Chi Jianhua, Zhang Wei, Chen Lingxi, Li Shuaicheng, Gao Qinglei

2022-Jan-12

C reactive protein, COVID-19, corticosteroid, lymphocyte percent, machine learning

General General

COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model.

In Physical and engineering sciences in medicine

Clinical reports show that COVID-19 disease has impacts on the cardiovascular system in addition to the respiratory system. Available COVID-19 diagnostic methods have been shown to have limitations. In addition to current diagnostic methods such as low-sensitivity standard RT-PCR tests and expensive medical imaging devices, the development of alternative methods for the diagnosis of COVID-19 disease would be beneficial for control of the COVID-19 pandemic. Further, it is important to quickly and accurately detect abnormalities caused by COVID-19 on the cardiovascular system via ECG. In this study, the diagnosis of COVID-19 disease is proposed using a novel deep Convolutional Neural Network model by using only ECG trace images created from ECG signals of COVID-19 infected patients based on the abnormalities caused by the COVID-19 virus on the cardiovascular system. An overall classification accuracy of 98.57%, 93.20%, 96.74% and AUC value of 0.9966, 0.9771, 0.9905 is achieved for COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, COVID-19 vs. Myocardial Infarction binary classification tasks, respectively. In addition, an overall classification accuracy of 86.55% and 83.05% is achieved for COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction and Normal vs. COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction multi-classification tasks. This study is believed to have great potential to speed up the diagnosis and treatment of COVID-19 patients, saving clinicians time and facilitating the control of the pandemic.

Irmak Emrah

2022-Jan-12

COVID-19 diagnosis, Cardiovascular diseases diagnosis, Convolutional neural networks, Deep learning, Electrocardiography, Machine learning

General General

Influence of mobility restrictions on air quality in the historic center of Cuenca city and its inference on the Covid-19 rate infections.

In Materials today. Proceedings

At the end of 2019 in Wuhan China city, the outbreak of the virus called SARS-CoV 2 was originated, which later became a pandemic. In Ecuador, patient zero arrived on February 14, 2020 and the first mobility restriction imposed by the Government occurred on Tuesday, March 17 of the same year. Throughout the confinement, vehicle mobility restrictions have been modified by government entities depending on the number of infected people. This article presents an air quality study in the historic center of Cuenca city as consequence of mobility changes caused by Covid-19, where a comparison of concentration levels of polluting gases of the first semester of 2018, 2019 and 2020 is made, that allow differentiating and identifying the influence of vehicular flow on air quality. It can also be verified how the decrease in vehicle mobility restrictions influenced the increase in the rate of daily infections. For the study, air quality data published by the public mobility company of the city of Cuenca (EMOV EP) and the communications issued by the Emergency Operations Committee (COE), before and during the confinement, were collected. The acquisition, classification, analysis and interpretation of the data obtained through machine learning techniques was carried out. It can be concluded that while mobility restrictions were more severe, air quality improved and infections rate of decrease. Obtaining that polluting gases such as NO2 and CO produced by vehicular traffic show correlations of 61% and 60% respectively, which means that after 15 days of lifting the restrictive measures, the pollutants increased as well as the number of infected.

Rivera Campoverde Néstor Diego, Molina Campoverde Paúl Andrés, Novillo Quirola Gina Pamela, Ortiz Valverde William Fernando, Serrano Ortiz Bryan Michael

2022

Air monitoring network, Air quality, Covid-19, Emission, Statistical techniques, Vehicular flow Type

General General

A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images.

In Multimedia systems

The COVID-19 pandemic has opened numerous challenges for scientists to use massive data to develop an automatic diagnostic tool for COVID-19. Since the outbreak in January 2020, COVID-19 has caused a substantial destructive impact on society and human life. Numerous studies have been conducted in search of a suitable solution to test COVID-19. Artificial intelligence (AI) based research is not behind in this race, and many AI-based models have been proposed. This paper proposes a lightweight convolutional neural network (CNN) model to classify COVID and Non_COVID patients by analyzing the hidden features in the X-Ray images. The model has been evaluated with different standard metrics to prove the reliability of the model. The model obtained 98.78%, 93.22%, and 92.7% accuracy in the training, validation, and testing phases. In addition, the model achieved 0.964 scores in the Area Under Curve (AUC) metric. We compared the model with four state-of-art pre-trained models (VGG16, InceptionV3, DenseNet121, and EfficientNetB6). The evaluation results demonstrate that the proposed CNN model is a candidate for an automatic diagnostic tool for the classification of COVID-19 patients using chest X-ray images. This research proposes a technique to classify COVID-19 patients and does not claim any medical diagnosis accuracy.

Masud Mehedi

2022-Jan-07

General General

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms.

In Neural computing & applications

A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart classrooms to enhance faculty efficiency based on accumulated learning outcomes and interests. Smart classroom boards, audio-visual aids, and multimedia are directly related to the Smart classroom environment. Along with these facilities, more effort is required to monitor and analyze students' outcomes, teachers' performance, attendance records, and contents delivery in on-campus classrooms. One can achieve more improvement in quality teaching and learning outcomes by developing digital twins in on-campus classrooms. In this article, we have proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan. DeepClassRooms is cost-effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes.

Razzaq Saad, Shah Babar, Iqbal Farkhund, Ilyas Muhammad, Maqbool Fahad, Rocha Alvaro

2022-Jan-07

CNN, Covid-19, Digital class room, Fog computing, Internet of things

General General

Microestimates of wealth for all low- and middle-income countries.

In Proceedings of the National Academy of Sciences of the United States of America

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.

Chi Guanghua, Fang Han, Chatterjee Sourav, Blumenstock Joshua E

2022-Jan-18

low- and middle-income countries, machine learning, poverty, poverty maps, sustainable development

General General

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review.

In Journal of pharmaceutical analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, has affected more than 250 million people worldwide. With the recent rise of a new Delta variant, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this review paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, RADAR, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

Saeed Umer, Shah Syed Yaseen, Ahmad Jawad, Imran Muhammad Ali, Abbasi Qammer H, Shah Syed Aziz

2022-Jan-04

Artificial intelligence, COVID-19, Machine learning, Non-contact sensing, Non-invasive healthcare

General General

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach.

In Journal of complex networks

This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)-quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

Renjini A, Swapna M S, Raj Vimal, Sankararaman S

2021-Dec

complex network, dry cough, neural net, quadratic SVM, wet cough

Public Health Public Health

Signaling Potential Therapeutic Herbal Medicine Prescription for Treating COVID-19 by Collaborative Filtering.

In Frontiers in pharmacology

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has aggressed in more than 200 countries and territories since Dec 2019, and 30 million cases of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 have been reported, including 950,000 deaths. Supportive treatment remains the mainstay of therapy for COVID-19. There are no small-molecule-specific antiviral drugs available to prevent and treat COVID-19 until recently. Herbal medicine can facilitate syndrome differentiation and treatment according to the clinical manifestations of patients and has demonstrated effectiveness in epidemic prevention and control. The National Health Commission (NHC) of China has recommended "three TCM prescriptions and three medicines," as a group of six effective herbal formulas against COVID-19 in the released official file "Diagnosis and Treatment Protocol for COVID-19 Patients: Herbal Medicine for the Priority Treatment of COVID-19." This study aimed to develop a collaborative filtering approach to signaling drug combinations that are similar to the six herbal formulas as potential therapeutic treatments for treating COVID-19. The results have been evaluated by herbal medicine experts' domain knowledge.

Yang Fan, Zhang Qi, Yuan Zhongshang, Teng Saisai, Cui Lizhen, Xue Fuzhong, Wei Leyi

2021

COVID-19, SARS-CoV-2 proteins, collaborative filtering, small molecule docking, traditional herbal medicine

General General

Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems.

In Multimedia tools and applications

Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments.

Peyvandi Amirhossein, Majidi Babak, Peyvandi Soodeh, Patra Jagdish C, Moshiri Behzad

2022-Jan-03

COVID-19 pandemic, Edge surveillance, Emergency response, Smart city, Waste management

General General

COVID-19 detection from CT scans using a two-stage framework.

In Expert systems with applications

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by FS for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive  β -Hill Climbing (A β HC) for better performance. We evaluate the proposed approach on the SARS-COV-2 CT-Scan Dataset consisting of 2482 CT scan images and an updated version of the previous dataset containing 2926 CT scan images. For comparison, we use a few state-of-the-art optimization algorithms. The best accuracy scores obtained by the present approach are 97.30% and 98.87% respectively on the said datasets, which are better than many of the algorithms used for comparison. The performances are also at par with some recent works which use the same datasets. The codes for the FS algorithms are available at: https://github.com/khalid0007/Metaheuristic-Algorithms.

Basu Arpan, Sheikh Khalid Hassan, Cuevas Erik, Sarkar Ram

2022-Jan-01

Adaptive β-Hill Climbing, COVID-19 detection, Convolutional Neural Network, Harmony Search

General General

Classifier Fusion for Detection of COVID-19 from CT Scans.

In Circuits, systems, and signal processing

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%.

Kaur Taranjit, Gandhi Tapan Kumar

2022-Jan-03

Activations, COVID-19, CT images, Classifier fusion, Diagnosis, Transfer learning

General General

Using artificial intelligence technology to fight COVID-19: a review.

In Artificial intelligence review

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

Peng Yong, Liu Enbin, Peng Shanbi, Chen Qikun, Li Dangjian, Lian Dianpeng

2022-Jan-03

Artificial intelligence, Blockchain, COVID-19, Cloud computing, Epidemic prevention and control, Internet of Things

General General

The COVID-19 impact on air condition usage: a shift towards residential energy saving.

In Environmental science and pollution research international

The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios.

Aliero Muhammad Saidu, Pasha Muhammad Fermi, Toosi Adel N, Ghani Imran

2022-Jan-10

A training dataset, Environmental sensing, Indoor comfort, Occupancy detection and estimation

General General

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study.

In Molecular diversity

Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an attractive target for drug design, due to its importance in virus replication. The analysis of the radial distribution function of 159 3CLpro structures reveals a high similarity index. A study of the catalytic pocket of 3CLpro with bound inhibitors reveals that the influence of the inhibitors is local, perturbing dominantly only residues in the active pocket. A machine learning based model with high predictive ability against SARS-CoV-2 3CLpro is designed and validated. The model is used to perform a drug-repurposing study, with the main aim to identify existing drugs with the highest 3CLpro inhibition power. Among antiviral agents, lopinavir, idoxuridine, paritaprevir, and favipiravir showed the highest inhibition potential. Enzyme - ligand interactions as a key ingredient for successful drug design.

Novak Jurica, Potemkin Vladimir A

2022-Jan-10

3CLpro, COVID-19, Drug repurposing, Paritaprevir, QSAR, Radial distribution function

General General

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study.

In Journal of the American College of Emergency Physicians open

Objectives : Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge.

Methods : We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches.

Results : Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models.

Conclusions : A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.

Beiser David G, Jarou Zachary J, Kassir Alaa A, Puskarich Michael A, Vrablik Marie C, Rosenman Elizabeth D, McDonald Samuel A, Meltzer Andrew C, Courtney D Mark, Kabrhel Christopher, Kline Jeffrey A

2021-Dec

COVID‐19, SARS‐CoV‐2, clinical prediction model, discharge planning, emergency department, machine learning, prognosis, readmissions

Radiology Radiology

COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics.

In Frontiers in digital health

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

Al-Hindawi Ahmed, Abdulaal Ahmed, Rawson Timothy M, Alqahtani Saleh A, Mughal Nabeela, Moore Luke S P

2021

COVID-19, Coronavirus, artificial intelligence, linear regression, machine learning

Cardiology Cardiology

Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence-Powered Analysis of 12-Lead Intake Electrocardiogram.

In Cardiovascular digital health journal

Background : Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that AI can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications.

Objective : Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE).

Methods : We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.4±16.9 years). Records were labeled by mortality (death vs. discharge) or MACE (no events vs. arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data.

Results : 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one MACE (119 arrhythmic; 107 HF; 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60±0.05 and 0.55±0.07, respectively; these were comparable to AUC values for conventional models (0.73±0.07 and 0.65±0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance.

Conclusion : Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

Sridhar Arun R, Chen Zih-Hua, Mayfield Jacob J, Fohner Alison E, Arvanitis Panagiotis, Atkinson Sarah, Braunschweig Frieder, Chatterjee Neal A, Zamponi Alessio Falasca, Johnson Gregory, Joshi Sanika A, Lassen Mats C H, Poole Jeanne E, Rumer Christopher, Skaarup Kristoffer G, Biering-Sørensen Tor, Blomstrom-Lundqvist Carina, Linde Cecilia M, Maleckar Mary M, Boyle Patrick M

2021-Dec-31

12-lead ECG, Artificial Intelligence, COVID-19, Deep Learning Arrhythmia, Heart Failure Prognosis, Mortality, Risk Factors

Radiology Radiology

Evaluation of multiple open-source deep learning models for detecting and grading COVID-19 on chest radiographs.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: Chest x-rays are complex to report accurately. Viral pneumonia is often subtle in its radiological appearance. In the context of the COVID-19 pandemic, rapid triage of cases and exclusion of other pathologies with artificial intelligence (AI) can assist over-stretched radiology departments. We aim to validate three open-source AI models on an external test set. Approach: We tested three open-source deep learning models, COVID-Net, COVIDNet-S-GEO, and CheXNet for their ability to detect COVID-19 pneumonia and to determine its severity using 129 chest x-rays from two different vendors Phillips and Agfa. Results: All three models detected COVID-19 pneumonia (AUCs from 0.666 to 0.778). Only the COVID Net-S-GEO and CheXNet models performed well on severity scoring (Pearson's r 0.927 and 0.833, respectively); COVID-Net only performed well at either task on images taken with a Philips machine (AUC 0.735) and not an Agfa machine (AUC 0.598). Conclusions: Chest x-ray triage using existing machine learning models for COVID-19 pneumonia can be successfully implemented using open-source AI models. Evaluation of the model using local x-ray machines and protocols is highly recommended before implementation to avoid vendor or protocol dependent bias.

Risman Alexander, Trelles Miguel, Denning David W

2021-Nov

COVID-19, artificial intelligence, x-ray

Public Health Public Health

Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study.

In Frontiers in medicine

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC. Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC. Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.

Bian Yi, Le Yue, Du Han, Chen Junfang, Zhang Ping, He Zhigang, Wang Ye, Yu Shanshan, Fang Yu, Yu Gang, Ling Jianmin, Feng Yikuan, Wei Sheng, Huang Jiao, Xiao Liuniu, Zheng Yingfang, Yu Zhen, Li Shusheng

2021

COVID-19, anticoagulation, bleeding events, mortality, outcomes, unsupervised machine learning

General General

A hover view over effectual approaches on pandemic management for sustainable cities - The endowment of prospective technologies with revitalization strategies.

In Sustainable cities and society

The COVID-19 pandemic affects all of society and hinders day-to-day activities from a straightforward perspective. The pandemic has an influential impact on almost everything and the characteristics of the pandemic remain unclear. This ultimately leads to ineffective strategic planning to manage the pandemic. This study aims to elucidate the typical pandemic characteristics in line with various temporal phases and its associated measures that proved effective in controlling the pandemic. Besides, an insight into diverse country's approaches towards pandemic and their consequences is provided in brief. Understanding the role of technologies in supporting humanity gives new perspectives to effectively manage the pandemic. Such role of technologies is expressed from the viewpoint of seamless connectivity, rapid communication, mobility, technological influence in healthcare, digitalization influence, surveillance and security, Artificial Intelligence (AI), and Internet of Things (IoT). Furthermore, some insightful scenarios are framed where the full-fledged implementation of technologies is assumed, and the reflected pandemic impacts in such scenarios are analyzed. The framed scenarios revolve around the digitalized energy sector, an enhanced supply chain system with effective customer-retailer relationships to support the city during the pandemic scenario, and an advanced tracking system for containing virus spread. The study is further extended to frame revitalization strategies to highlight the expertise where significant attention needs to be provided in the post-pandemic period as well as to nurture sustainable development. Finally, the current pandemic scenario is analyzed in terms of occurred changes and is mapped into SWOT factors. Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution based Multi-Criteria Decision Analysis, these SWOT factors are analyzed to determine where prioritized efforts are needed to focus so as to traverse towards sustainable cities. The results indicate that the enhanced crisis management ability and situational need to restructure the economic model emerges to be the most-significant SWOT factor that can ultimately support humanity for making the cities sustainable.

Elavarasan Rajvikram Madurai, Pugazhendhi Rishi, Shafiullah G M, Irfan Muhammad, Anvari-Moghaddam Amjad

2021-May

COVID-19 Pandemic, Pandemic characteristics, Supply chain management, Sustainability, Technology, Tracing

General General

BEMD-3DCNN-based method for COVID-19 detection.

In Computers in biology and medicine

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the most effective medicine to help and protect patients. Importantly, a rapid diagnostic and detection system is a priority and should be developed to stop COVID-19 from spreading. Medical imaging techniques have been used for this purpose. Current research is focused on exploiting different backbones like VGG, ResNet, DenseNet, or combining them to detect COVID-19. By using these backbones many aspects cannot be analyzed like the spatial and contextual information in the images, although this information can be useful for more robust detection performance. In this paper, we used 3D representation of the data as input for the proposed 3DCNN-based deep learning model. The process includes using the Bi-dimensional Empirical Mode Decomposition (BEMD) technique to decompose the original image into IMFs, and then building a video of these IMF images. The formed video is used as input for the 3DCNN model to classify and detect the COVID-19 virus. The 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) module, and then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows learning from different feature maps. In our experiments, we used 6484 X-ray images, of which 1802 were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed technique achieved the desired results on the selected dataset. Additionally, the use of the 3DCNN model with contextual information processing exploited CAA networks to achieve better performance.

Riahi Ali, Elharrouss Omar, Al-Maadeed Somaya

2021-Dec-30

3DCNN, BEMD, COVID-19, Context-aware attention

General General

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning.

In Computers in biology and medicine

BACKGROUND : We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals.

METHODS : We recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay.

RESULTS : Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87).

CONCLUSIONS : Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.

Boussen Salah, Cordier Pierre-Yves, Malet Arthur, Simeone Pierre, Cataldi Sophie, Vaisse Camille, Roche Xavier, Castelli Alexandre, Assal Mehdi, Pepin Guillaume, Cot Kevin, Denis Jean-Baptiste, Morales Timothée, Velly Lionel, Bruder Nicolas

2021-Dec-31

Artificial intelligence, COVID-19, Intubation, Monitoring, Prediction

General General

Using AI and passive medical radiometry for diagnostics (MWR) of venous diseases.

In Computer methods and programs in biomedicine

We studied the possibility of using artificial intelligence (AI) passive microwave radiometry (MWR) for the diagnostics of venous diseases. MWR measures non-invasive microwave emission (internal temperature) from human body 4 cm deep. The method has been used for early diagnostics in cancer, back pain, brain, COVID-19 pneumonia, and other diseases. In this paper, an AI model based on MWR data is proposed. The model was used to predict the disease state of phlebology patients. We have used MWR and infrared (skin temperature) data of the lower extremities to design a feature space and construct a classification algorithm. Our method has a sensitivity above 0.8 and a specificity above 0.7. At the same time, our method provides an advisory outcome in terms which are understandable for clinicians.

Levshinskii V, Galazis C, Losev A, Zamechnik T, Kharybina T, Vesnin S, Goryanin I

2021-Dec-29

General General

Atorvastatin versus placebo in patients with covid-19 in intensive care: randomized controlled trial.

In BMJ (Clinical research ed.)

OBJECTIVE : To assess the effect of statin treatment versus placebo on clinical outcomes in patients with covid-19 admitted to the intensive care unit (ICU).

DESIGN : INSPIRATION/INSPIRATION-S was a multicenter, randomized controlled trial with a 2×2 factorial design. Results for the anticoagulation randomization have been reported previously. Results for the double blind randomization to atorvastatin versus placebo are reported here.

SETTING : 11 hospitals in Iran.

PARTICIPANTS : Adults aged ≥18 years with covid-19 admitted to the ICU.

INTERVENTION : Atorvastatin 20 mg orally once daily versus placebo, to be continued for 30 days from randomization irrespective of hospital discharge status.

MAIN OUTCOME MEASURES : The primary efficacy outcome was a composite of venous or arterial thrombosis, treatment with extracorporeal membrane oxygenation, or all cause mortality within 30 days from randomization. Prespecified safety outcomes included increase in liver enzyme levels more than three times the upper limit of normal and clinically diagnosed myopathy. A clinical events committee blinded to treatment assignment adjudicated the efficacy and safety outcomes.

RESULTS : Of 605 patients randomized between 29 July 2020 and 4 April 2021 for statin randomization in the INSPIRATION-S trial, 343 were co-randomized to intermediate dose versus standard dose prophylactic anticoagulation with heparin based regimens, whereas 262 were randomized after completion of the anticoagulation study. 587 of the 605 participants were included in the primary analysis of INSPIRATION-S, reported here: 290 were assigned to atorvastatin and 297 to placebo (median age 57 years (interquartile range 45-68 years); 256 (44%) women). The primary outcome occurred in 95 (33%) patients assigned to atorvastatin and 108 (36%) assigned to placebo (odds ratio 0.84, 95% confidence interval 0.58 to 1.21). Death occurred in 90 (31%) patients in the atorvastatin group and 103 (35%) in the placebo group (odds ratio 0.84, 95% confidence interval 0.58 to 1.22). Rates for venous thromboembolism were 2% (n=6) in the atorvastatin group and 3% (n=9) in the placebo group (odds ratio 0.71, 95% confidence interval 0.24 to 2.06). Myopathy was not clinically diagnosed in either group. Liver enzyme levels were increased in five (2%) patients assigned to atorvastatin and six (2%) assigned to placebo (odds ratio 0.85, 95% confidence interval 0.25 to 2.81).

CONCLUSIONS : In adults with covid-19 admitted to the ICU, atorvastatin was not associated with a significant reduction in the composite of venous or arterial thrombosis, treatment with extracorporeal membrane oxygenation, or all cause mortality compared with placebo. Treatment was, however, found to be safe. As the overall event rates were lower than expected, a clinically important treatment effect cannot be excluded.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04486508.

**

2022-Jan-07

Ophthalmology Ophthalmology

Application of artificial intelligence in cataract management: current and future directions.

In Eye and vision (London, England)

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

Gutierrez Laura, Lim Jane Sujuan, Foo Li Lian, Ng Wei Yan Yan, Yip Michelle, Lim Gilbert Yong San, Wong Melissa Hsing Yi, Fong Allan, Rosman Mohamad, Mehta Jodhbir Singth, Lin Haotian, Ting Darren Shu Jeng, Ting Daniel Shu Wei

2022-Jan-07

Artificial intelligence, Biometry, Cataract, Cataract screening, Cataract surgery, IOL calculations, Machine learning, Telemedicine

General General

Privacy-aware Early Detection of COVID-19 through Adversarial Training

ArXiv Preprint

Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect coronavirus using routine clinical data (blood tests, and vital signs). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this work, we examine two machine learning approaches, intended to predict a patient's COVID-19 status using routinely collected and readily available clinical data. We employ adversarial training to explore robust deep learning architectures that protect attributes related to demographic information about the patients. The two models we examine in this work are intended to preserve sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets from the Oxford University Hospitals, Bedfordshire Hospitals NHS Foundation Trust, University Hospitals Birmingham NHS Foundation Trust, and Portsmouth Hospitals University NHS Trust we train and test two neural networks that predict PCR test results using information from basic laboratory blood tests, and vital signs performed on a patients' arrival to hospital. We assess the level of privacy each one of the models can provide and show the efficacy and robustness of our proposed architectures against a comparable baseline. One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.

Omid Rohanian, Samaneh Kouchaki, Andrew Soltan, Jenny Yang, Morteza Rohanian, Yang Yang, David Clifton

2022-01-09

General General

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution.

In Medical & biological engineering & computing ; h5-index 32.0

COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients were enrolled in our observational study. Anagraphical and anamnestic data, COVID-19 signs and symptoms, COVID-19 therapy, hematochemical test results, and prior therapies administered to patients are used as predictors. A set of 55 features, all of which can be taken in the first hours of the patient's hospitalization, was considered. Different solutions were compared achieving the best performance with a sequential convolutional neural network-based model merged in an ensemble with two different meta-learners linked in cascade. We obtained a median absolute error of 2.7 days (IQR = 3.0) in predicting the duration of the infection; the error was equally distributed in the infection duration range. This tool could preemptively give an outlook of the COVID-19 patients' expected path and the associated hospitalization effort. The proposed solution could be viable in tackling the huge burden and the logistics complexity of hospitals or rehabilitation centers during the pandemic waves. With data taken ad admission, entering a PCA-based feature selection, a k-fold cross-validated CNN-based model was implemented. After external texting, a median absolute error of 2.7 days [IQR = 3 days].

Liuzzi Piergiuseppe, Campagnini Silvia, Fanciullacci Chiara, Arienti Chiara, Patrini Michele, Carrozza Maria Chiara, Mannini Andrea

2022-Jan-07

Artificial intelligence, COVID-19, Convolutional neural network, Duration of infection, Prognostic models, Rehabilitation

General General

Brain Networks Associated With COVID-19 Risk: Data From 3662 Participants.

In Chronic stress (Thousand Oaks, Calif.)

Background : Our behavioral traits, and subsequent actions, could affect the risk of exposure to the coronavirus disease of 2019 (COVID-19). The current study aimed to determine whether unique brain networks are associated with the COVID-19 infection risk.

Methods : This research was conducted using the UK Biobank Resource. Functional magnetic resonance imaging scans in a cohort of general population (n = 3662) were used to compute the whole-brain functional connectomes. A network-informed machine learning approach was used to identify connectome and nodal fingerprints that are associated with positive COVID-19 status during the pandemic up to February fourth, 2021.

Results : The predictive models successfully identified 6 fingerprints that were associated with COVID-19 positive, compared to negative status (all p values < 0.005). Overall, lower integration across the brain modules and increased segregation, as reflected by internal within module connectivity, were associated with higher infection rates. More specifically, COVID-19 positive status was associated with 1) reduced connectivity between the central executive and ventral salience, as well as between the dorsal salience and default mode networks; 2) increased internal connectivity within the default mode, ventral salience, subcortical and sensorimotor networks; and 3) increased connectivity between the ventral salience, subcortical and sensorimotor networks.

Conclusion : Individuals are at increased risk of COVID-19 infections if their brain connectome is consistent with reduced connectivity in the top-down attention and executive networks, along with increased internal connectivity in the introspective and instinctive networks. These identified risk networks could be investigated as target for treatment of illnesses with impulse control deficits.

Abdallah Chadi G

COVID-19, brain imaging, functional connectivity, impulse control, risk networks

Surgery Surgery

Personal identification with artificial intelligence under COVID-19 crisis: a scoping review.

In Systematic reviews

BACKGROUND : Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence.

METHODS : This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study.

RESULTS : By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods.

CONCLUSIONS : This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.

Matsuda Shinpei, Yoshimura Hitoshi

2022-Jan-06

Artificial intelligence, COVID-19 pandemic, Contactless methods, Convolutional neural network, Personal identification, Scoping review

General General

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review.

In International journal of medical informatics ; h5-index 49.0

PURPOSE : The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice.

METHODS : Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered.

RESULTS : Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice.

CONCLUSIONS : ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.

Nwanosike Ezekwesiri Michael, Conway Barbara R, Merchant Hamid A, Hasan Syed Shahzad

2021-Dec-31

AUROC, COVID-19, Clinical practice, Clinical studies, Electronic health records (EHRs), Machine learning, Model deployment, Prediction

General General

Omicron Variant (B.1.1.529): Infectivity, Vaccine Breakthrough, and Antibody Resistance.

In Journal of chemical information and modeling

The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron's infectivity, vaccine breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data and extensively validated by experimental results on SARS-CoV-2, reveals that Omicron may be over 10 times more contagious than the original virus or about 2.8 times as infectious as the Delta variant. On the basis of 185 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may have an 88% likelihood to escape current vaccines. The U.S. Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from AstraZeneca, Regeneron mAb cocktail, Celltrion, and Rockefeller University. However, its impacts on GlaxoSmithKline's sotrovimab appear to be mild. Our work calls for new strategies to develop the next generation mutation-proof SARS-CoV-2 vaccines and antibodies.

Chen Jiahui, Wang Rui, Gilby Nancy Benovich, Wei Guo-Wei

2022-Jan-06

Radiology Radiology

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

In European radiology ; h5-index 62.0

BACKGROUND : Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course.

METHODS : CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models.

RESULTS : A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available.

INTERPRETATION : The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization.

KEY POINTS : • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Liang Hengrui, Guo Yuchen, Chen Xiangru, Ang Keng-Leong, He Yuwei, Jiang Na, Du Qiang, Zeng Qingsi, Lu Ligong, Gao Zebin, Li Linduo, Li Quanzheng, Nie Fangxing, Ding Guiguang, Huang Gao, Chen Ailan, Li Yimin, Guan Weijie, Sang Ling, Xu Yuanda, Chen Huai, Chen Zisheng, Li Shiyue, Zhang Nuofu, Chen Ying, Huang Danxia, Li Run, Li Jianfu, Cheng Bo, Zhao Yi, Li Caichen, Xiong Shan, Wang Runchen, Liu Jun, Wang Wei, Huang Jun, Cui Fei, Xu Tao, Lure Fleming Y M, Zhan Meixiao, Huang Yuanyi, Yang Qiang, Dai Qionghai, Liang Wenhua, He Jianxing, Zhong Nanshan

2022-Jan-06

AI (artificial intelligence), Computer-assisted diagnosis, Coronavirus disease 2019

oncology Oncology

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.

In Artificial intelligence in the life sciences

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

Linden Thomas, Hanses Frank, Domingo-Fernández Daniel, DeLong Lauren Nicole, Kodamullil Alpha Tom, Schneider Jochen, Vehreschild Maria J G T, Lanznaster Julia, Ruethrich Maria Madeleine, Borgmann Stefan, Hower Martin, Wille Kai, Feldt Torsten, Rieg Siegbert, Hertenstein Bernd, Wyen Christoph, Roemmele Christoph, Vehreschild Jörg Janne, Jakob Carolin E M, Stecher Melanie, Kuzikov Maria, Zaliani Andrea, Fröhlich Holger

2021-Dec

Covid19, Drug repositioning, Explainable ai, Machine learning, Precision medicine

General General

Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm.

In BMC public health ; h5-index 82.0

BACKGROUND : Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms.

METHODS : The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability.

RESULTS : The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients.

CONCLUSION : The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.

Ebrahimi Vahid, Sharifi Mehrdad, Mousavi-Roknabadi Razieh Sadat, Sadegh Robab, Khademian Mohammad Hossein, Moghadami Mohsen, Dehbozorgi Afsaneh

2022-Jan-05

COVID-19, Elastic-net, Machine-learning, Re-infection, Survival

Surgery Surgery

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19.

In PloS one ; h5-index 176.0

OBJECTIVE : To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED).

METHODS : We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance.

RESULTS : The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05).

CONCLUSION : A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Lupei Monica I, Li Danni, Ingraham Nicholas E, Baum Karyn D, Benson Bradley, Puskarich Michael, Milbrandt David, Melton Genevieve B, Scheppmann Daren, Usher Michael G, Tignanelli Christopher J

2022

General General

Identification of SARS-CoV-2 main protease inhibitors from FDA-approved drugs by artificial intelligence-supported activity prediction system.

In Journal of biomolecular structure & dynamics

Although a certain level of efficacy and safety of several vaccine products against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have been established, unmet medical needs for orally active small molecule therapeutic drugs are still very high. As a key drug target molecule, SARS-CoV-2 main protease (Mpro) is focused and large number of in-silico screenings, a part of which were supported by artificial intelligence (AI), have been conducted to identify Mpro inhibitors both through drug repurposing and drug discovery approaches. In the many drug-repurposing studies, docking simulation-based technologies have been mainly employed and contributed to the identification of several Mpro binders. On the other hand, because AI-guided INTerprotein's Engine for New Drug Design (AI-guided INTENDD), an AI-supported activity prediction system for small molecules, enables to propose the potential binders by proprietary AI scores but not docking scores, it was expected to identify novel potential Mpro binders from FDA-approved drugs. As a result, we selected 20 potential Mpro binders using AI-guided INTENDD, of which 13 drugs showed Mpro-binding signal by surface plasmon resonance (SPR) method. Six (6) compounds among the 13 positive drugs were identified for the first time by the present study. Furthermore, it was verified that vorapaxar bound to Mpro with a Kd value of 27 µM by SPR method and inhibited virus replication in SARS-CoV-2 infected cells with an EC50 value of 11 µM.Communicated by Ramaswamy H. Sarma.

Komatsu Hirotsugu, Tanaka Takeshi, Ye Zhengmao, Ikeda Ken, Matsuzaki Takao, Yasugi Mayo, Hosoda Masato

2022-Jan-05

COVID-19, artificial intelligence, drug repurposing, main protease, small molecule inhibitor

General General

Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines.

In Journal of primary care & community health

BACKGROUND : The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines.

VACCINE 4.0 FRAMEWORK : The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements.

DISCUSSION : Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities.

CONCLUSION : Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic.

Sarfraz Azza, Sarfraz Zouina, Sarfraz Muzna, Abdul Razzack Aminah, Bano Shehar, Singh Makkar Sarabjot, Thevuthasan Sindhu, Paul Trissa, Khawar Sana Muhammad, Azeem Nishwa, Felix Miguel, Cherrez-Ojeda Ivan

COVID-19, community health, global health, policy, populations, vaccination

General General

Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.

In The AAPS journal

Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.

Kolluri Sheela, Lin Jianchang, Liu Rachael, Zhang Yanwei, Zhang Wenwen

2022-Jan-04

Artificial intelligence, Clinical trial design, Drug development, Machine learning, Precision medicine, Predictive modeling, Probability of success, Risk-based monitoring

General General

Comparing machine learning algorithms for predicting COVID-19 mortality.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making.

METHODS : In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated.

RESULTS : The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively.

CONCLUSION : It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.

Moulaei Khadijeh, Shanbehzadeh Mostafa, Mohammadi-Taghiabad Zahra, Kazemi-Arpanahi Hadi

2022-Jan-04

Artificial intelligence, COVID-19, Coronavirus, Machine learning, Prediction hospital mortality

General General

Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization.

In bioRxiv : the preprint server for biology

A potential therapeutic candidate for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding of the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant ACE2 variant, named ACE2 2 .v.2.4, that exhibits nanomolar affinity binding to the RBD domain of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identified other ACE2 variants, namely double mutants, that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified several ACE2 double mutants that bind to RBD with tighter affinity as compared to the wild type, most notably, L79V;N90D that binds RBD with similar affinity to ACE2 2 .v.2.4. The successful experimental validation of the double mutants demonstrated the use transfer and supervised learning approaches for engineering protein-protein interactions and identifying high affinity ACE2 peptides for targeting SARS-CoV-2.

Chan Matthew C, Chan Kui K, Procko Erik, Shukla Diwakar

2021-Dec-23

General General

COVIDXception-Net: A Bayesian Optimization-Based Deep Learning Approach to Diagnose COVID-19 from X-Ray Images.

In SN computer science

COVID-19 is spreading around the world like wildfire. Chest X-rays are used as one of the primary tools for diagnosing COVID-19. However, about two-thirds of the world population do not have access to sufficient radiological services. In this work, we propose a deep learning-driven automated system, COVIDXception-Net, for diagnosing COVID-19 from chest X-rays. A primary challenge in any data-driven COVID-19 detection is the scarcity of COVID-19 data, which heavily deteriorates a deep learning model's performance. To address this issue, we incorporate a weighted-loss function that ensures the COVID-19 cases are given more importance during the training process. We also propose using Bayesian Optimization to find the best architecture for detecting COVID-19. Extensive experimentation on four publicly available COVID-19 datasets shows that our proposed model achieves an accuracy of 0.94, precision 0.95, recall 0.94, specificity 0.997, F1-score 0.94, and Matthews correlation coefficient 0.992 outperforming three widely used architectures-VGG16, MobileNetV2, and InceptionV3. It also surpasses the performance of several state-of-the-art COVID-19 detection methods. We also performed two ablation studies that show our model's accuracy degrades from 0.994 to 0.950 when a random search is used and to 0.983 when a regular loss function is employed instead of the Bayesian and weighted loss, respectively.

Arman Shifat E, Rahman Sejuti, Deowan Shamim Ahmed

2022

Bayesian optimization, COVID-19, Chest X-ray, Deep learning, SARS-CoV-2

General General

Using bi-dimensional representations to understand patterns on COVID-19 blood exam data.

In Informatics in medicine unlocked

Blood tests have an essential part in everyday medicine and are used by doctors in several diagnostic procedures. Still, this data is multivariate - and often some diseases, like COVID-19, could have different symptom manifestation and outcomes. This study proposes a method of extracting useful information from blood tests using UMAP technique - Uniform Manifold Approximation and Projection for Dimension Reduction combined with DBSCAN clustering and statistical approaches. The analysis performed here indicates several clusters of infection prevalence varying between 2%-37%, meaning that our procedure is indeed capable of finding different patterns. A possible explanation is that COVID-19 is not just a respiratory infection but a systemic disease with critical hematological implications, primarily on white-cell fractions, as indicated by relevant statistical tests p-values in the range of 0.03-0.1. The novel analysis procedure proposed could be adopted in other data-sets of different illnesses to help researchers to discover new patterns of data that could be used in various diseases and contexts.

Bezzan Vitor P, Rocco Cleber D

2021-Dec-30

Applied AI, Blood exam, COVID-19, Dimensionality reduction, Machine learning, Unsupervised learning

General General

My video game console is so cool! A coolness theory-based model for intention to use video game consoles.

In Technological forecasting and social change

With the outbreak of COVID-19, the video game console market is thriving again. In this study, we attempted to explore users' intention to use video game consoles by developing a causal model mainly based on coolness theory and the technology acceptance model. To better illustrate user experience for video game consoles, we added several concepts to the causal model, including hedonic motivation, system and service quality, perceived cost, and game variety. Through examining survey-based data from 360 Koreans, we discovered that the model had a high explanatory power for users' intention to use video game consoles. The key findings were as follows: First, among the components of coolness theory, individuals' attitude toward consoles was significantly related to subcultural appeal and originality, but not to attractiveness. Second, originality positively influenced subcultural appeal significantly. Overall, this study implied that the novel coolness theory is effective for exploring user experience regarding of specific devices and services.

Nan Dongyan, Lee Haein, Kim Yerin, Kim Jang Hyun

2022-Mar

Attractiveness, Coolness, Originality, Subcultural appeal, Video game console

General General

[Drug repositioning to combat COVID-19 using artificial intelligence system].

In Nihon yakurigaku zasshi. Folia pharmacologica Japonica

Although months have passed since WHO declared COVID-19 a global pandemic, only a limited number of clinically effective drugs are available, and the development of drugs to treat COVID-19 has become an urgent issue worldwide. The pace of new research on COVID-19 is extremely high and it is impossible to read every report. In order to tackle these problems, we leveraged our artificial intelligence (AI) system, Concept Encoder, to accelerate the process of drug repositioning. Concept Encoder is a patented AI system based on natural language processing technology and by deeply learning papers on COVID-19, the system identified a large group of genes implicated in COVID-19 pathogenesis. The AI system then generated a molecular linkage map for COVID-19, connecting the genes by learning the molecular relationship comprehensively. By thoroughly reviewing the resulting map and list of the genes with rankings, we found potential key players for disease progression and existing drugs that might improve COVID-19 survival. Here, we focus on potential targets and discuss the perspective of our approach.

Shindo Norihisa, Toyoshiba Hiroyoshi

2022

General General

Data Quality and Network Considerations for Mobile Contact Tracing and Health Monitoring.

In Frontiers in digital health

Machine Learning (ML) has been a useful tool for scientific advancement during the COVID-19 pandemic. Contact tracing apps are just one area reaping the benefits, as ML can use location and health data from these apps to forecast virus spread, predict "hotspots," and identify vulnerable groups. However, to do so, it is first important to ensure that the dataset these apps yield is accurate, free of biases, and reliable, as any flaw can directly influence ML predictions. Given the lack of criteria to help ensure this, we present two requirements for those exploring using ML to follow. The requirements we presented work to uphold international data quality standards put forth for ML. We then identify where our requirements can be met, as countries have varying contact tracing apps and smartphone usages. Lastly, the advantages, limitations, and ethical considerations of our approach are discussed.

Dave Riya, Gupta Rashmi

2021

AI, COVID-19, contact tracing, digital health, mobile applications

General General

COV-ADSX: An Automated Detection System using X-ray images, deep learning, and XGBoost for COVID-19.

In Software impacts

Following the COVID-19 pandemic, scientists have been looking for different ways to diagnose COVID-19, and these efforts have led to a variety of solutions. One of the common methods of detecting infected people is chest radiography. In this paper, an Automated Detection System using X-ray images (COV-ADSX) is proposed, which employs a deep neural network and XGBoost to detect COVID-19. COV-ADSX was implemented using the Django web framework, which allows the user to upload an X-ray image and view the results of the COVID-19 detection and image's heatmap, which helps the expert to evaluate the chest area more accurately.

Hasani Sharif, Nasiri Hamid

2021-Dec-29

COVID-19, Chest X-ray images, Deep neural networks, DenseNet169, XGBoost

General General

Indirect supervision applied to COVID-19 and pneumonia classification.

In Informatics in medicine unlocked

The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.

Danilov Viacheslav V, Proutski Alex, Karpovsky Alex, Kirpich Alexander, Litmanovich Diana, Nefaridze Dato, Talalov Oleg, Semyonov Semyon, Koniukhovskii Vladimir, Shvartc Vladimir, Gankin Yuriy

2021-Dec-28

COVID-19, Classification, Deep learning, Indirect supervision, Pneumonia, Transfer learning

General General

A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method.

In Informatics in medicine unlocked

Background : Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients.

Methods : In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified.

Results : Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients.

Conclusions : The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and identifying high risk patients.

Varzaneh Zahra Asghari, Orooji Azam, Erfannia Leila, Shanbehzadeh Mostafa

2021-Dec-28

Artificial intelligent, COVID-19, Coronavirus, Data mining, Intubation, Machine learning, Mechanical ventilator

Public Health Public Health

The Epidemic of Sexually Transmitted Diseases Under the Influence of COVID-19 in China.

In Frontiers in public health

Background: Prevention and control of HIV/AIDS and other sexually transmitted diseases (STDs) are major public health priorities in China, but are influenced by the COVID-19 epidemic. In this study, we aimed to quantitatively explore the impact of the COVID-19 epidemic and its control measures on five major STD epidemics in China. Methods: A monthly number of newly reported cases of HIV/AIDS, hepatitis B and C, gonorrhea, and syphilis from January 2010 to December 2020 were extracted to establish autoregressive integrated moving average (ARIMA) models. Each month's absolute percentage error (APE) between the actual value and model-predicted value of each STD in 2020 was calculated to evaluate the influence of the COVID-19 epidemic on the STDs. Pearson correlation analysis was conducted to explore the confirmed COVID-19 case numbers and the COVID-19 control measures' correlations with the case numbers and the APEs of five STDs in 2020. Results: The actual number of five STDs in China was more than 50% lower than the predicted number in the early days of the COVID-19 epidemic, especially in February. Among them, the actual number of cases of hepatitis C, gonorrhea, and syphilis in February 2020 was more than 100% lower than the predicted number (APE was -102.3, -109.0, and -100.4%, respectively). After the sharply declines of STDs' reported cases in early 2020, the case numbers recovered quickly after March. The epidemic of STDs was negatively associated with the COVID-19 epidemic and its control measures, especially for restrictions on gathering size, close public transport, and stay-at-home requirements (p < 0.05). Conclusion: COVID-19 had a significant but temporary influence on the STD epidemic in China. The effective control of COVID-19 is vital for STD prevention. STD services need to be improved to prevent STDs from becoming a secluded corner in the shadow of COVID-19.

Yan Xiangyu, Wang Xuechun, Zhang Xiangyu, Wang Lei, Zhang Bo, Jia Zhongwei

2021

COVID-19, HIV/AIDS, epidemic, gonorrhea, hepatitis, sexually transmitted diseases, syphilis

General General

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence.

In IEEE access : practical innovations, open solutions

Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.

Ahsan Md Manjurul, Ahad Md Tanvir, Soma Farzana Akter, Paul Shuva, Chowdhury Ananna, Luna Shahana Akter, Yazdan Munshi Md Shafwat, Rahman Akhlaqur, Siddique Zahed, Huebner Pedro

2021

Artificial intelligence, COVID-19, SARS-CoV-2, chest X-ray, coronavirus, deep learning, imbalanced data, small data

General General

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19).

In IEEE access : practical innovations, open solutions

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

Islam Md Milon, Karray Fakhri, Alhajj Reda, Zeng Jia

2021

COVID-19, Coronavirus, computer tomography, deep learning, deep transfer learning, diagnosis, x-ray

General General

COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples.

In Journal of Shanghai Jiaotong University (science)

The COVID-19 medical diagnosis method based on individual's chest X-ray (CXR) is achieved difficultly in the initial research, owing to difficulties in identifying CXR data of COVID-19 individuals. At the beginning of the study, infected individuals' CXRs were scarce. The combination of artificial intelligence and medical diagnosis has been advanced and popular. To solve the difficulties, the interpretability analysis of AI model was used to explore the pathological characteristics of CXR samples infected with COVID-19 and assist medical diagnosis. The dataset was expanded by data augmentation to avoid overfitting. Transfer learning was used to test different pre-trained models and the unique output layers were designed to complete the model training with few samples. In this study, the output results of four pre-trained models were compared in three different output layers, and the results after data augmentation were compared with the results of the original dataset. The control variable method was used to conduct independent tests of 24 groups. Finally, 99.23% accuracy and 98% recall rate were obtained, and the visual results of CXR interpretability analysis were displayed. The network of COVID-19 interpretable diagnosis algorithm has the characteristics of high generalization and lightweight. It can be quickly applied to other urgent tasks with insufficient experimental data. At the same time, interpretability analysis brings new possibilities for medical diagnosis.

Bu Ran, Xiang Wei, Cao Shitong

2021-Dec-26

COVID-19, chest X-ray (CXR), convolutional neural network, data augmentation, interpretability, transfer learning

General General

Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images.

In Journal of Shanghai Jiaotong University (science)

Deep learning based analyses of computed tomography (CT) images contribute to automated diagnosis of COVID-19, and ensemble learning may commonly provide a better solution. Here, we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19. Two ensemble strategies are considered: the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation; voting strategy. A database containing 8 347 CT slices of COVID-19, common pneumonia and normal subjects was used as training and testing sets. Results show that the novel method can reach a high accuracy of 99.37% (recall: 0.9981, precision: 0.989 3), with an increase of about 7% in comparison to single-component models. And the average test accuracy is 95.62% (recall: 0.958 7, precision: 0.955 9), with a corresponding increase of 5.2%. Compared with several latest deep learning models on the identical test set, our method made an accuracy improvement up to 10.88%. The proposed method may be a promising solution for the diagnosis of COVID-19.

Wang Zhiming, Dong Jingjing, Zhang Junpeng

2021-Dec-26

COVID-19, computed tomography (CT) images, convolutional neural network, deep learning, ensemble model

Radiology Radiology

Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.

In Computers in biology and medicine

BACKGROUND : Chest computed tomography (CT) is crucial in the diagnosis of coronavirus disease 2019 (COVID-19). However, the persistent pandemic and similar CT manifestations between COVID-19 and community-acquired pneumonia (CAP) raise methodological requirements.

METHODS : A fully automatic pipeline of deep learning is proposed for distinguishing COVID-19 from CAP using CT images. Inspired by the diagnostic process of radiologists, the pipeline comprises four connected modules for lung segmentation, selection of slices with lesions, slice-level prediction, and patient-level prediction. The roles of the first and second modules and the effectiveness of the capsule network for slice-level prediction were investigated. A dataset of 326 CT scans was collected to train and test the pipeline. Another public dataset of 110 patients was used to evaluate the generalization capability.

RESULTS : LinkNet exhibited the largest intersection over union (0.967) and Dice coefficient (0.983) for lung segmentation. For the selection of slices with lesions, the capsule network with the ResNet50 block achieved an accuracy of 92.5% and an area under the curve (AUC) of 0.933. The capsule network using the DenseNet121 block demonstrated better performance for slice-level prediction, with an accuracy of 97.1% and AUC of 0.992. For both datasets, the prediction accuracy of our pipeline was 100% at the patient level.

CONCLUSIONS : The proposed fully automatic deep learning pipeline of deep learning can distinguish COVID-19 from CAP via CT images rapidly and accurately, thereby accelerating diagnosis and augmenting the performance of radiologists. This pipeline is convenient for use by radiologists and provides explainable predictions.

Qi Qianqian, Qi Shouliang, Wu Yanan, Li Chen, Tian Bin, Xia Shuyue, Ren Jigang, Yang Liming, Wang Hanlin, Yu Hui

2021-Dec-29

Capsule network, Community-acquired pneumonia, Coronavirus disease 2019, Deep learning, Lung computed tomography image

General General

An interactome landscape of SARS-CoV-2 virus-human protein-protein interactions by protein sequence-based multi-label classifiers

bioRxiv Preprint

The new coronavirus species, SARS-CoV-2, caused an unprecedented global pandemic of COVID-19 disease since late December 2019. A comprehensive characterization of protein-protein interactions (PPIs) between SARS-CoV-2 and human cells is a key to understanding the infection and preventing the disease. Here we present a novel approach to predict virus-host PPIs by multi-label machine learning classifiers of random forests and XGBoost using amino acid composition profiles of virus and human proteins. Our models harness a large-scale database of Viruses.STRING with >80,000 virus-host PPIs along with evidence scores for multi-level evidence prediction, which is distinct from predicting binary interactions in previous studies. Our multi-label classifiers are based on 5 evidence levels binned from evidence scores. Our best model of XGBoost achieves 74% AUC and 68% accuracy on average in 10-fold cross validation. The most important amino acids are cysteine and histidine. In addition, our model predicts experimental PPIs with higher evidence level than text mining-based PPIs. We then predict evidence levels of ~2,000 SARS-CoV-2 virus-human PPIs from public experimental proteomics data. Interactions with SARS-CoV-2 Nsp7b show high evidence. We also predict evidence levels of all pairwise PPIs of ~550,000 between the SARS-CoV-2 and human proteomes to provide a draft virus-host interactome landscape for SARS-CoV-2 infection in humans in a comprehensive and unbiased way in silico. Most human proteins from 140 highest evidence predictions interact with SARS-CoV-2 Nsp7, Nsp1, and ORF14, with significant enrichment in the top 2 pathways of vascular smooth muscle contraction (CALD1, NPR2, CALML3) and Myc targets (CBX3, PES1). Our prediction also suggests that histone H2A components are targeted by multiple SARS-CoV-2 proteins.

Lee, H.-J.

2022-01-03

Pathology Pathology

Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing.

In Clinical chemistry ; h5-index 61.0

BACKGROUND : Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available.

CONTENT : In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications.

SUMMARY : The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.

Tran Nam K, Albahra Samer, May Larissa, Waldman Sarah, Crabtree Scott, Bainbridge Scott, Rashidi Hooman

2021-Dec-30

COVID-19, Lyme disease, data stream, electronic medical records, immunoassays, meningitis, point-of-care testing, predictive analytics, sensor fusion, sepsis, tuberculosis

General General

Efficacy of virtual and asynchronous teaching of computer-assisted diagnosis of genetic diseases seen in clinics.

In American journal of medical genetics. Part A

We studied if clinicians could gain sufficient working knowledge of a computer-assisted diagnostic decision support system (DDSS) (SimulConsult), to make differential diagnoses (DDx) of genetic disorders. We hypothesized that virtual training could be convenient, asynchronous, and effective in teaching clinicians how to use a DDSS. We determined the efficacy of virtual, asynchronous teaching for clinicians to gain working knowledge to make computer-assisted DDx. Our study consisted of three surveys (Baseline, Training, and After Use) and a series of case problems sent to clinicians at Vanderbilt University Medical Center. All participants were able to generate computer-assisted DDx that achieved passing scores of the case problems. Between 75% and 92% agreed/completely agreed the DDSS was useful to their work and for clinical decision support and was easy to use. Participants' use of the DDSS resulted in statistically significant time savings in key tasks and in total time spent on clinical tasks. Our results indicate that virtual, asynchronous teaching can be an effective format to gain a working knowledge of a DDSS, and its clinical use could result in significant time savings across multiple tasks as well as facilitate synergistic interaction between clinicians and lab specialists. This approach is especially pertinent and offers value amid the COVID-19 pandemic.

Hash Mary Grace, Walker Philip D, Laferriere Heather E, Melton Leeanna, Heller Lauren S, Phillips John A

2021-Dec-30

clinical decision support, education, genetics education, machine learning, teaching, virtual

General General

Embeddings from protein language models predict conservation and variant effects.

In Human genetics

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.

Marquet Céline, Heinzinger Michael, Olenyi Tobias, Dallago Christian, Erckert Kyra, Bernhofer Michael, Nechaev Dmitrii, Rost Burkhard

2021-Dec-30

General General

Tiled Sparse Coding in Eigenspaces for Image Classification.

In International journal of neural systems

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. blackThese alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are blackfirst partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. blackThen, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. blackOur system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

Arco Juan E, Ortiz Andrés, Ramírez Javier, Zhang Yu-Dong, Górriz Juan M

2021-Dec-30

COVID-[Formula: see text], Computer-aided diagnosis, deep learning, dictionary, machine learning, medical imaging, pneumonia, sparse coding

Radiology Radiology

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020.

In Current medical imaging

OBJECTIVES : Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020, analyzing the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping, along with a bibliometric analysis for publications.

METHODS : We performed a search on Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, which represents major topics discussed. Of the resulting topics, literature map created, and linkage analysis done.

RESULTS : A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords, only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while "computed tomography" has the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by "radiotherapy, and "deep and machine learning". The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included "COVID-19" or "coronavirus" in their title, including the top cited paper.

CONCLUSION : While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represent the topic of top cited articles in 2020 radiology publications.

Al-Ryalat Nosaiba, Malkawi Lna, Abu Salhiyeh Ala’a, Abualteen Faisal, Abdallah Ghaida, Al Omari Bayan, AlRyalat Saif Aldeen

2021-Dec-30

COVID-19, computer tomography, coronavirus, literature mapping, magnetic resonance imaging, radiology

General General

Membrane-Based In-Gel Loop-Mediated Isothermal Amplification (mgLAMP) System for SARS-CoV-2 Quantification in Environmental Waters.

In Environmental science & technology ; h5-index 132.0

Since the COVID-19 pandemic is expected to become endemic, quantification of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in ambient waters is critical for environmental surveillance and for early detection of outbreaks. Herein, we report the development of a membrane-based in-gel loop-mediated isothermal amplification (mgLAMP) system that is designed for the rapid point-of-use quantification of SARS-CoV-2 particles in environmental waters. The mgLAMP system integrates the viral concentration, in-assay viral lysis, and on-membrane hydrogel-based RT-LAMP quantification using enhanced fluorescence detection with a target-specific probe. With a sample-to-result time of less than 1 h, mgLAMP successfully detected SARS-CoV-2 below 0.96 copies/mL in Milli-Q water. In surface water, the lowest detected SARS-CoV-2 concentration was 93 copies/mL for mgLAMP, while the reverse transcription quantitative polymerase chain reaction (RT-qPCR) with optimal pretreatment was inhibited at 930 copies/mL. A 3D-printed portable device is designed to integrate heated incubation and fluorescence illumination for the simultaneous analysis of nine mgLAMP assays. Smartphone-based imaging and machine learning-based image processing are used for the interpretation of results. In this report, we demonstrate that mgLAMP is a promising method for large-scale environmental surveillance of SARS-CoV-2 without the need for specialized equipment, highly trained personnel, and labor-intensive procedures.

Zhu Yanzhe, Wu Xunyi, Gu Alan, Dobelle Leopold, Cid Clément A, Li Jing, Hoffmann Michael R

2021-Dec-30

RT-LAMP, SARS-CoV-2, environmental quantification of SARS-CoV-2 in milliliters of environmental water samples, hydrogel, membrane

General General

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images.

In Behavioural neurology

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).

Syed Hassaan Haider, Khan Muhammad Attique, Tariq Usman, Armghan Ammar, Alenezi Fayadh, Khan Junaid Ali, Rho Seungmin, Kadry Seifedine, Rajinikanth Venkatesan

2021

Public Health Public Health

How Academic Health Systems Can Be Ready for the Next Pandemic.

In Academic medicine : journal of the Association of American Medical Colleges

The COVID-19 pandemic created significant challenges for academic health systems (AHSs) across their tripartite mission of providing clinical care, conducting research, and educating learners. Despite these challenges, AHSs played an invaluable role in responding to the pandemic. Clinicians worked tirelessly to care for patients, and institutions quickly reoriented their care delivery systems. Furthermore, AHSs played an important role in advancing science, launching studies and clinical trials to examine new vaccines and treatments for COVID-19. However, there is room for improvement; AHSs can use lessons learned from the COVID-19 pandemic to reshape their operations for the future. To prepare for the next pandemic, AHSs must modernize, adapt, and transform their clinical operations, research infrastructure, and education programs to include public health and to build surveillance capacity for detecting, monitoring, and managing emerging outbreaks. In this commentary, the authors describe the opportunities AHSs have to build on their experiences during the COVID-19 pandemic and the ways they can take advantage of their unique strengths in each of their 3 mission areas. Within clinical care, AHSs can reach patients outside traditional clinical settings, build national and regional networks, advance data-driven insights, engage with the community, and support and protect the workforce. Within research, they can leverage data science and artificial intelligence, perform pandemic forecasting, leverage the social and behavioral sciences, conduct clinical trials, and build a research and development preparedness and operational plan. Within education, AHSs can promote remote learning, make interprofessional learning the norm, and build a system of continuing education. [end of abstract].

Dzau Victor J, Ellaissi William F, Krishnan K Ranga Rama, Balatbat Celynne A

2021-Dec-28

Surgery Surgery

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients.

In Journal of voice : official journal of the Voice Foundation

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

Robotti Carlo, Costantini Giovanni, Saggio Giovanni, Cesarini Valerio, Calastri Anna, Maiorano Eugenia, Piloni Davide, Perrone Tiziano, Sabatini Umberto, Ferretti Virginia Valeria, Cassaniti Irene, Baldanti Fausto, Gravina Andrea, Sakib Ahmed, Alessi Elena, Pascucci Matteo, Casali Daniele, Zarezadeh Zakarya, Zoppo Vincenzo Del, Pisani Antonio, Benazzo Marco

2021-Nov-26

Accuracy, Cough, SARS-CoV-2, Screening test, Sensitivity, Surveillance

General General

Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading.

In Computational intelligence and neuroscience

Risk management and stock investment decision-making is an essential topic for investors and fund managers, especially in the context of the COVID-19 pandemic. The problem becomes easier if the market is efficient, where stock prices fully reflect potential risk. Nevertheless, if the market is not efficient, investors may have an opportunity to find an effective investment method. Vietnam is one of the emerging markets; the efficiency is still weak. Thus, there will be an opportunity for astute investors. This study aims to test the weak-form efficient market and provide a modern approach to investors' decision-making. To achieve that aim, this study uses historical data of stocks in the VN-Index and VN30 portfolio to buy and sell within a one-day period under the rolling window approach to test the Ho Chi Minh City Stock Exchange (HoSE) through a runs test and to perform stock trading using the support vector machine (SVM) and logistic regression. The buying/selling of stocks is guided by the forecasted outcomes (increase/decrease) of logistic regression and SVM. This study adjusted the return rate in proportion to the risks and compared it with index investments of VN-Index and VN30 to evaluate investment efficiency. The test results dismissed the weak-form efficient-market hypothesis, which opens up many opportunities for short-term traders. This study's primary contribution is to provide a stock trading strategy for short-term investors to maximize trading profits. Because logistic regression and SVM have proven effective trading methods, investors can use them to achieve abnormal returns.

Khoa Bui Thanh, Huynh Tran Trong

2021

General General

The relationship between text message sentiment and self-reported depression.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Personal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers.

METHODS : We correlated privacy-preserving sentiment analysis of text messages with self-reported depression symptom severity. We enrolled 219 U.S. adults in a 16 week longitudinal observational study. Participants installed a personal sensing app on their phones, which administered self-report PHQ-8 assessments of their depression severity, collected phone sensor data, and computed anonymized language sentiment scores from their text messages. We also trained machine learning models for predicting end-of-study self-reported depression status using on blocks of phone sensor and text features.

RESULTS : In correlation analyses, we find that degrees of depression, emotional, and personal pronoun language categories correlate most strongly with self-reported depression, validating prior literature. Our classification models which predict binary depression status achieve a leave-one-out AUC of 0.72 when only considering text features and 0.76 when combining text with other networked smartphone sensors.

LIMITATIONS : Participants were recruited from a panel that over-represented women, caucasians, and individuals with self-reported depression at baseline. As language use differs across demographic factors, generalizability beyond this population may be limited. The study period also coincided with the initial COVID-19 outbreak in the United States, which may have affected smartphone sensor data quality.

CONCLUSIONS : Effective depression prediction through text message sentiment, especially when combined with other personal sensors, could enable comprehensive mental health monitoring and intervention.

Liu Tony, Meyerhoff Jonah, Eichstaedt Johannes C, Karr Chris J, Kaiser Susan M, Kording Konrad P, Mohr David C, Ungar Lyle H

2021-Dec-25

Depression, digital phenotyping, language sentiment analysis, machine learning, personal sensing

General General

All about that trait: Examining extraversion and state anxiety during the SARS-CoV-2 pandemic using a machine learning approach.

In Personality and individual differences ; h5-index 70.0

We examine the longitudinal relation between extraversion and state anxiety in a large cohort of New York City (NYC) residents using a linguistic analytical machine learning approach. Anxiety, both state and trait, and Big Five personality traits were predicted using micro-blog data on the Twitter platform. In total, we examined 1336 individuals and a total of 200,289 observations across 246 days. We find that before the onset of SARS-CoV-2 in NYC (before 1st March 2020), extraverts experienced lower state anxiety compared to introverted individuals, while this difference shrinks after the onset of the pandemic, which provides evidence that SARS-COV-2 is affecting all individuals regardless of their extraversion trait disposition. Secondly, a longitudinal examination of the presented data shows that extraversion seems to matter more greatly in the early days of the crisis and towards the end of our examined time range. We interpret results within the unique SARS-CoV-2 context and discuss the relationship between SARS-COV-2 and individual differences, namely personality traits. Finally, we discuss results and outline the limitations of our approach.

Gruda Dritjon, Ojo Adegboyega

2022-Apr

Anxiety, COVID-19, Extraversion, Longitudinal, Machine learning

General General

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

In Critical care (London, England)

INTRODUCTION : Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.

METHODS : We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots.

RESULTS : A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure.

CONCLUSION : The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

Fleuren Lucas M, Dam Tariq A, Tonutti Michele, de Bruin Daan P, Lalisang Robbert C A, Gommers Diederik, Cremer Olaf L, Bosman Rob J, Rigter Sander, Wils Evert-Jan, Frenzel Tim, Dongelmans Dave A, de Jong Remko, Peters Marco, Kamps Marlijn J A, Ramnarain Dharmanand, Nowitzky Ralph, Nooteboom Fleur G C A, de Ruijter Wouter, Urlings-Strop Louise C, Smit Ellen G M, Mehagnoul-Schipper D Jannet, Dormans Tom, de Jager Cornelis P C, Hendriks Stefaan H A, Achterberg Sefanja, Oostdijk Evelien, Reidinga Auke C, Festen-Spanjer Barbara, Brunnekreef Gert B, Cornet Alexander D, van den Tempel Walter, Boelens Age D, Koetsier Peter, Lens Judith, Faber Harald J, Karakus A, Entjes Robert, de Jong Paul, Rettig Thijs C D, Arbous Sesmu, Vonk Sebastiaan J J, Fornasa Mattia, Machado Tomas, Houwert Taco, Hovenkamp Hidde, Noorduijn Londono Roberto, Quintarelli Davide, Scholtemeijer Martijn G, de Beer Aletta A, Cinà Giovanni, Kantorik Adam, de Ruijter Tom, Herter Willem E, Beudel Martijn, Girbes Armand R J, Hoogendoorn Mark, Thoral Patrick J, Elbers Paul W G

2021-Dec-27

Extubation, Extubation failure, Prediction, Risk factors

Public Health Public Health

Optimising COVID-19 Vaccination Policy to Mitigate SARS-CoV-2 Transmission within Schools in Zimbabwe.

In Vaccines

The COVID-19 pandemic has disrupted the learning of millions of children across the world. Since March 2020 when the first cases of COVID-19 were reported in Zimbabwe, the country, like many others, has gone through periods of closing and re-opening of schools as part of the national COVID-19 control and mitigation measures. Schools promote the social, mental, physical, and moral development of children. With this viewpoint, the authors argue that schools should not be closed to provide a measured and efficient response to the threats posed by the COVID-19 epidemic. Rather, infection prevention and control strategies, including vaccination of learners and teachers, and surveillance in schools should be heightened. The use of multiple prevention strategies discussed in this viewpoint has shown that when outbreaks in school settings are adequately managed, the transmission usually is low. The information presented here suggests that schools should remain open due to the preponderance of evidence indicating the overriding positive impacts of this policy on the health, development, and wellbeing of children.

Murewanhema Grant, Mukwenha Solomon, Dzinamarira Tafadzwa, Mukandavire Zindoga, Cuadros Diego, Madziva Roda, Chingombe Innocent, Mapingure Munyaradzi, Herrera Helena, Musuka Godfrey

2021-Dec-15

COVID-19, Zimbabwe, schools, vaccination

General General

Major Complex Trait for Early De Novo Programming 'CoV-MAC-TED' Detected in Human Nasal Epithelial Cells Infected by Two SARS-CoV-2 Variants Is Promising to Help in Designing Therapeutic Strategies.

In Vaccines

BACKGROUND : Early metabolic reorganization was only recently recognized as an essentially integrated part of immunology. In this context, unbalanced ROS/RNS levels connected to increased aerobic fermentation, which is linked to alpha-tubulin-based cell restructuring and control of cell cycle progression, were identified as a major complex trait for early de novo programming ('CoV-MAC-TED') during SARS-CoV-2 infection. This trait was highlighted as a critical target for developing early anti-viral/anti-SARS-CoV-2 strategies. To obtain this result, analyses had been performed on transcriptome data from diverse experimental cell systems. A call was released for wide data collection of the defined set of genes for transcriptome analyses, named 'ReprogVirus', which should be based on strictly standardized protocols and data entry from diverse virus types and variants into the 'ReprogVirus Platform'. This platform is currently under development. However, so far, an in vitro cell system from primary target cells for virus attacks that could ideally serve for standardizing the data collection of early SARS-CoV-2 infection responses has not been defined.

RESULTS : Here, we demonstrate transcriptome-level profiles of the most critical 'ReprogVirus' gene sets for identifying 'CoV-MAC-TED' in cultured human nasal epithelial cells infected by two SARS-CoV-2 variants differing in disease severity. Our results (a) validate 'Cov-MAC-TED' as a crucial trait for early SARS-CoV-2 reprogramming for the tested virus variants and (b) demonstrate its relevance in cultured human nasal epithelial cells.

CONCLUSION : In vitro-cultured human nasal epithelial cells proved to be appropriate for standardized transcriptome data collection in the 'ReprogVirus Platform'. Thus, this cell system is highly promising to advance integrative data analyses with the help of artificial intelligence methodologies for designing anti-SARS-CoV-2 strategies.

Costa José Hélio, Aziz Shahid, Noceda Carlos, Arnholdt-Schmitt Birgit

2021-Nov-26

ADH5, E2F1, SARS-CoV-2 RdRp, SARS-CoV-2 helicase, SARS-CoV-2 Δ382, anti-viral diagnosis and strategies, cell cycle, immunology paradigm shift, melatonin, microbiota, repurposing drugs

Radiology Radiology

Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography.

In Tuberkuloz ve toraks

Introduction : Computed tomography (CT) is an auxiliary modality in the diagnosis of the novel Coronavirus (COVID-19) disease and can guide physicians in the presence of lung involvement. In this study, we aimed to investigate the contribution of deep learning to diagnosis in patients with typical COVID-19 pneumonia findings on CT.

Materials and Methods : This study retrospectively evaluated 690 lesions obtained from 35 patients diagnosed with COVID-19 pneumonia based on typical findings on non-contrast high-resolution CT (HRCT) in our hospital. The diagnoses of the patients were also confirmed by other necessary tests. HRCT images were assessed in the parenchymal window. In the images obtained, COVID-19 lesions were detected. For the deep Convolutional Neural Network (CNN) algorithm, the Confusion matrix was used based on a Tensorflow Framework in Python.

Result : A total of 596 labeled lesions obtained from 224 sections of the images were used for the training of the algorithm, 89 labeled lesions from 27 sections were used in validation, and 67 labeled lesions from 25 images in testing. Fifty-six of the 67 lesions used in the testing stage were accurately detected by the algorithm while the remaining 11 were not recognized. There was no false positive. The Recall, Precision and F1 score values in the test group were 83.58, 1, and 91.06, respectively.

Conclusions : We successfully detected the COVID-19 pneumonia lesions on CT images using the algorithms created with artificial intelligence. The integration of deep learning into the diagnostic stage in medicine is an important step for the diagnosis of diseases that can cause lung involvement in possible future pandemics.

Aydın Nevin, Çelik Özer

2021-Dec

Public Health Public Health

Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction.

In Frontiers in cardiovascular medicine

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF. Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771-0.895) and validation set (AUC 0.798, 95% CI: 0.719-0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05). Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

Gao Yan, Bai Xueke, Lu Jiapeng, Zhang Lihua, Yan Xiaofang, Huang Xinghe, Dai Hao, Wang Yanping, Hou Libo, Wang Siming, Tian Aoxi, Li Jing

2021

biomarkers, heart failure, preserved ejection fraction, prognostic, risk of death

General General

Generating novel molecule for target protein (SARS-CoV-2) using drug-target interaction based on graph neural network.

In Network modeling and analysis in health informatics and bioinformatics

The transmittable spread of viral coronavirus (SARS-CoV-2) has resulted in a significant rise in global mortality. Due to lack of effective treatment, our aim is to generate a highly potent active molecule that can bind with the protein structure of SARS-CoV-2. Different machine learning and deep learning approaches have been proposed for molecule generation; however, most of these approaches represent the drug molecule and protein structure in 1D sequence, ignoring the fact that molecules are by nature in 3D structure, and because of this many critical properties are lost. In this work, a framework is proposed that takes account of both tertiary and sequential representations of molecules and proteins using Gated Graph Neural Network (GGNN), Knowledge graph, and Early Fusion approach. The generated molecules from GGNN are screened using Knowledge Graph to reduce the search space by discarding the non-binding molecules before being fed into the Early Fusion model. Further, the binding affinity score of the generated molecule is predicted using the early fusion approach. Experimental result shows that our framework generates valid and unique molecules with high accuracy while preserving the chemical properties. The use of a knowledge graph claims that the entire generated dataset of molecules was reduced by roughly 96% while retaining more than 85% of good binding desirable molecules and the rejection of more than 99% of fruitless molecules. Additionally, the framework was tested with two of the SARS-CoV-2 viral proteins: RNA-dependent-RNA polymerase (RdRp) and 3C-like protease (3CLpro).

Ranjan Amit, Shukla Shivansh, Datta Deepanjan, Misra Rajiv

2022

Deep learning, Drug-target affinity prediction, Graph neural network, Molecule generation

General General

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.

In IEEE journal of translational engineering in health and medicine

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

Mondal Arnab Kumar, Bhattacharjee Arnab, Singla Parag, Prathosh A P

2022

AI for COVID-19 detection, CT scan and CXR, deep learning, vision transformer

Public Health Public Health

High Fasting Blood Glucose Level With Unknown Prior History of Diabetes Is Associated With High Risk of Severe Adverse COVID-19 Outcome.

In Frontiers in endocrinology ; h5-index 55.0

Background : We aimed to understand how glycaemic levels among COVID-19 patients impact their disease progression and clinical complications.

Methods : We enrolled 2,366 COVID-19 patients from Huoshenshan hospital in Wuhan. We stratified the COVID-19 patients into four subgroups by current fasting blood glucose (FBG) levels and their awareness of prior diabetic status, including patients with FBG<6.1mmol/L with no history of diabetes (group 1), patients with FBG<6.1mmol/L with a history of diabetes diagnosed (group 2), patients with FBG≥6.1mmol/L with no history of diabetes (group 3) and patients with FBG≥6.1mmol/L with a history of diabetes diagnosed (group 4). A multivariate cause-specific Cox proportional hazard model was used to assess the associations between FBG levels or prior diabetic status and clinical adversities in COVID-19 patients.

Results : COVID-19 patients with higher FBG and unknown diabetes in the past (group 3) are more likely to progress to the severe or critical stage than patients in other groups (severe: 38.46% vs 23.46%-30.70%; critical 7.69% vs 0.61%-3.96%). These patients also have the highest abnormal level of inflammatory parameters, complications, and clinical adversities among all four groups (all p<0.05). On day 21 of hospitalisation, group 3 had a significantly higher risk of ICU admission [14.1% (9.6%-18.6%)] than group 4 [7.0% (3.7%-10.3%)], group 2 [4.0% (0.2%-7.8%)] and group 1 [2.1% (1.4%-2.8%)], (P<0.001). Compared with group 1 who had low FBG, group 3 demonstrated 5 times higher risk of ICU admission events during hospitalisation (HR=5.38, 3.46-8.35, P<0.001), while group 4, where the patients had high FBG and prior diabetes diagnosed, also showed a significantly higher risk (HR=1.99, 1.12-3.52, P=0.019), but to a much lesser extent than in group 3.

Conclusion : Our study shows that COVID-19 patients with current high FBG levels but unaware of pre-existing diabetes, or possibly new onset diabetes as a result of COVID-19 infection, have a higher risk of more severe adverse outcomes than those aware of prior diagnosis of diabetes and those with low current FBG levels.

Wang Wenjun, Chai Zhonglin, Cooper Mark E, Zimmet Paul Z, Guo Hua, Ding Junyu, Yang Feifei, Chen Xu, Lin Xixiang, Zhang Kai, Zhong Qin, Li Zongren, Zhang Peifang, Wu Zhenzhou, Guan Xizhou, Zhang Lei, He Kunlun

2021

COVID-19, FBG levels, complications, diabetes, glycaemia control and treatment

Public Health Public Health

Pre-pandemic Predictors of Loneliness in Adult Men During COVID-19.

In Frontiers in psychiatry

Loneliness is a major public health issue, with its prevalence rising during COVID-19 pandemic lockdowns and mandated "social distancing" practices. A 2020 global study (n = 46,054) found that, in comparison to women, men experienced the greatest levels of loneliness. Although research on predictors of loneliness during COVID-19 is increasing, little is known about the characteristics of men who may be particularly vulnerable. Studies using prospective data are needed to inform preventative measures to support men at risk of loneliness. The current study draws on rare longitudinal data from an Australian cohort of men in young to mid-adulthood (n = 283; aged M = 34.6, SD = 1.38 years) to examine 25 pre-pandemic psychosocial predictors of loneliness during COVID-19 social restrictions (March-September 2020). Adjusted linear regressions identified 22 pre-pandemic predictors of loneliness across a range of trait-based, relational, career/home and mental health variables. Given the extensive set of predictors, we then conducted penalized regression models (LASSO), a machine learning approach, allowing us to identify the best fitting multivariable set of predictors of loneliness during the pandemic. In these models, men's sense of pre-pandemic environmental mastery emerged as the strongest predictor of loneliness. Depression, neuroticism and social support also remained key predictors of pandemic loneliness (R 2 = 26, including covariates). Our findings suggest that men's loneliness can be detected prospectively and under varying levels of social restriction, presenting possible targets for prevention efforts for those most vulnerable.

Mansour Kayla A, Greenwood Christopher J, Biden Ebony J, Francis Lauren M, Olsson Craig A, Macdonald Jacqui A

2021

COVID-19, loneliness, longitudinal, male, pandemic

Public Health Public Health

COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland.

In Applied soft computing

Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localized environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources.

Sousa José, Barata João, Woerden Hugo C van, Kee Frank

2021-Dec-20

COVID-19, Location analytics, Mobile app, SARS-COV-2, Semantic networks, Strong structuration theory

General General

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis.

In Multimedia tools and applications

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed "Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)". The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

Das Anupam

2021-Dec-22

Adaptive Activation Function-based U-Net, Autoencoder, COVID-19 Detection, Convolutional Neural Network, Ensemble Learning with CNN-based Deep Features, Naive Bayes, Self Adaptive- Tunicate Swarm Algorithm, Support Vector Machine

General General

A multi-method analytical approach to predicting young adults' intention to invest in mHealth during the COVID-19 pandemic.

In Telematics and informatics

Mobile-based health (mHealth) systems are proving to be a popular alternative to the traditional visits to healthcare providers. They can also be useful and effective in fighting the spread of infectious diseases, such as the COVID-19 pandemic. Even though young adults are the most prevalent mHealth user group, the relevant literature has overlooked their intention to invest in and use mHealth services. This study aims to investigate the predictors that influence young adults' intention to invest in mHealth (IINmH), particularly during the COVID-19 crisis, by designing a research methodology that incorporates both the health belief model (HBM) and the expectation-confirmation model (ECM). As an expansion of the integrated HBM-ECM model, this study proposes two additional predictors: mobile Internet speed and mobile Internet cost. A multi-method analytical approach, including partial least squares structural equation modelling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA), and machine learning (ML), was utilised together with a sample dataset of 558 respondents. The dataset-about young adults in Bangladesh with an experience of using mHealth-was obtained through a structured questionnaire to examine the complex causal relationships of the integrated model. The findings from PLS-SEM indicate that value-for-money, mobile Internet cost, health motivation, and confirmation of services all have a substantial impact on young adults' IINmH during the COVID-19 pandemic. At the same time, the fsQCA results indicate that a combination of predictors, instead of any individual predictor, had a significant impact on predicting IINmH. Among ML methods, the XGBoost classifier outperformed other classifiers in predicting the IINmH, which was then used to perform sensitivity analysis to determine the relevance of features. We expect this multi-method analytical approach to make a significant contribution to the mHealth domain as well as the broad information systems literature.

Hasan Najmul, Bao Yukun, Chiong Raymond

2021-Dec-22

SEM-fsQCA-ML, integrated information systems model, mHealth, multi-method analytical approach, young adults

General General

Potential next-generation medications for self-administered platforms.

In Journal of controlled release : official journal of the Controlled Release Society

The Coronavirus Disease (COVID-19) pandemic has reshaped clinical chronic disease management. Patients reduced the number of physical clinic visits for regular follow-up care because of the pandemic. However, in developing countries, the scattered healthcare system hindered accessibility to clinical consultation, and poorly controlled chronic diseases resulted in numerous complications. Furthermore, the longer patients suffered from the chronic disease being treated, the more physical and psychological stress they experienced. "Diabetes Burnout," as an example, is a term to describe the phenomenon of psychological reluctance in long-term glycemic control. A comprehensive, patient-centered, and automatic drug administration and delivery model may reduce patient stress and increase compliance. Potential next-generation medication platforms, consisting of internal regulation and external interaction, may conduct autonomous dose adjustment and continuous selfmonitoring with the assistance of artificial intelligence, telemedicine, and wireless technologies. Internal regulation forms a closed-loop system in which drug administration is optimized in an implanted drug-releasing device according to a patient's physiopathological response. The other feature, external interaction, creates an ecosystem among patients, healthcare providers, and pharmaceutical researchers to monitor and adjust post-market therapeutic efficacy and safety. These platforms may provide a solution for self-medication and self-care for a wide variety of patients but may be life-changing for patients who live in developing countries where the healthcare system is scattered, as they could effectively remove healthcare barriers. As the technology matures, these self-administrated platforms may become more available and increasingly affordable, offering considerable impact to health and wellness efforts worldwide.

Chen Cheng-Han, Cheng Chao-Min

2021-Dec-24

General General

Anomaly Detection using Capsule Networks for High-dimensional Datasets

ArXiv Preprint

Anomaly detection is an essential problem in machine learning. Application areas include network security, health care, fraud detection, etc., involving high-dimensional datasets. A typical anomaly detection system always faces the class-imbalance problem in the form of a vast difference in the sample sizes of different classes. They usually have class overlap problems. This study used a capsule network for the anomaly detection task. To the best of our knowledge, this is the first instance where a capsule network is analyzed for the anomaly detection task in a high-dimensional complex data setting. We also handle the related novelty and outlier detection problems. The architecture of the capsule network was suitably modified for a binary classification task. Capsule networks offer a good option for detecting anomalies due to the effect of viewpoint invariance captured in its predictions and viewpoint equivariance captured in internal capsule architecture. We used six-layered under-complete autoencoder architecture with second and third layers containing capsules. The capsules were trained using the dynamic routing algorithm. We created $10$-imbalanced datasets from the original MNIST dataset and compared the performance of the capsule network with $5$ baseline models. Our leading test set measures are F1-score for minority class and area under the ROC curve. We found that the capsule network outperformed every other baseline model on the anomaly detection task by using only ten epochs for training and without using any other data level and algorithm level approach. Thus, we conclude that capsule networks are excellent in modeling complex high-dimensional imbalanced datasets for the anomaly detection task.

Inderjeet Singh, Nandyala Hemachandra

2021-12-27

Pathology Pathology

Evolution of enhanced innate immune evasion by SARS-CoV-2.

In Nature ; h5-index 368.0

Emergence of SARS-CoV-2 variants of concern (VOCs) suggests viral adaptation to enhance human-to-human transmission1,2. Although much effort has focused on characterisation of spike changes in VOCs, mutations outside spike likely contribute to adaptation. Here we used unbiased abundance proteomics, phosphoproteomics, RNAseq and viral replication assays to show that isolates of the Alpha (B.1.1.7) variant3 more effectively suppress innate immune responses in airway epithelial cells, compared to first wave isolates. We found that Alpha has dramatically increased subgenomic RNA and protein levels of N, Orf9b and Orf6, all known innate immune antagonists. Expression of Orf9b alone suppressed the innate immune response through interaction with TOM70, a mitochondrial protein required for RNA sensing adaptor MAVS activation. Moreover, the activity of Orf9b and its association with TOM70 was regulated by phosphorylation. We propose that more effective innate immune suppression, through enhanced expression of specific viral antagonist proteins, increases the likelihood of successful Alpha transmission, and may increase in vivo replication and duration of infection4. The importance of mutations outside Spike in adaptation of SARS-CoV-2 to humans is underscored by the observation that similar mutations exist in the Delta and Omicron N/Orf9b regulatory regions.

Thorne Lucy G, Bouhaddou Mehdi, Reuschl Ann-Kathrin, Zuliani-Alvarez Lorena, Polacco Ben, Pelin Adrian, Batra Jyoti, Whelan Matthew V X, Hosmillo Myra, Fossati Andrea, Ragazzini Roberta, Jungreis Irwin, Ummadi Manisha, Rojc Ajda, Turner Jane, Bischof Marie L, Obernier Kirsten, Braberg Hannes, Soucheray Margaret, Richards Alicia, Chen Kuei-Ho, Harjai Bhavya, Memon Danish, Hiatt Joseph, Rosales Romel, McGovern Briana L, Jahun Aminu, Fabius Jacqueline M, White Kris, Goodfellow Ian G, Takeuchi Yasu, Bonfanti Paola, Shokat Kevan, Jura Natalia, Verba Klim, Noursadeghi Mahdad, Beltrao Pedro, Kellis Manolis, Swaney Danielle L, García-Sastre Adolfo, Jolly Clare, Towers Greg J, Krogan Nevan J

2021-Dec-23

General General

AND-rPPG: A novel denoising-rPPG network for improving remote heart rate estimation.

In Computers in biology and medicine

Heart rate (HR) estimation is an essential physiological parameter in the field of biomedical imaging. Remote Photoplethysmography (r-PPG) is a pathbreaking development in this field wherein the PPG signal is extracted from non-contact face videos. In the COVID-19 pandemic, rPPG plays a vital role for doctors and patients to perform telehealthcare. Existing rPPG methods provide incorrect HR estimation when face video contains facial deformations induced by facial expression. These methods process the entire face and utilize the same knowledge to mitigate different noises. It limits the performance of these methods because different facial expressions induce different noise characteristics depending on the facial region. Another limitation is that these methods neglect the facial expression for denoising even though it is the prominent noise source in temporal signals. These issues are mitigated in this paper by proposing a novel HR estimation method AND-rPPG, that is, A Novel Denoising-rPPG. We initiate the utilization of Action Units (AUs) for denoising temporal signals. Our denoising network models the temporal signals better than sequential architectures and mitigate the AUs-based (or face expression-based) noises effectively. The experiments performed on publicly available datasets reveal that our proposed method outperforms state-of-the-art HR estimation methods, and our denoising model can be easily integrated with existing methods to improve their HR estimation.

Lokendra Birla, Puneet Gupta

2021-Dec-17

COVID-19, Deep learning, Heart rate estimation, Remote-photoplethysmography, Temporal convolutional networks

Radiology Radiology

Abdominal Computed Tomography Imaging Findings in Hospitalized COVID-19 Patients: A Year-Long Experience and Associations Revealed by Explainable Artificial Intelligence.

In Journal of imaging

The aim of this retrospective study is to assess any association between abdominal CT findings and the radiological stage of COVID-19 pneumonia, pulmonary embolism and patient outcomes. We included 158 adult hospitalized COVID-19 patients between 1 March 2020 and 1 March 2021 who underwent 206 abdominal CTs. Two radiologists reviewed all CT images. Pathological findings were classified as acute or not. A subset of patients with inflammatory pathology in ACE2 organs (bowel, biliary tract, pancreas, urinary system) was identified. The radiological stage of COVID pneumonia, pulmonary embolism, overall days of hospitalization, ICU admission and outcome were registered. Univariate statistical analysis coupled with explainable artificial intelligence (AI) techniques were used to discover associations between variables. The most frequent acute findings were bowel abnormalities (n = 58), abdominal fluid (n = 42), hematomas (n = 28) and acute urologic conditions (n = 8). According to univariate statistical analysis, pneumonia stage > 2 was significantly associated with increased frequency of hematomas, active bleeding and fluid-filled colon. The presence of at least one hepatobiliary finding was associated with all the COVID-19 stages > 0. Free abdominal fluid, acute pathologies in ACE2 organs and fluid-filled colon were associated with ICU admission; free fluid also presented poor patient outcomes. Hematomas and active bleeding with at least a progressive stage of COVID pneumonia. The explainable AI techniques find no strong relationship between variables.

Scarabelli Alice, Zilocchi Massimo, Casiraghi Elena, Fasani Pierangelo, Plensich Guido Giovanni, Esposito Andrea Alessandro, Stellato Elvira, Petrini Alessandro, Reese Justin, Robinson Peter, Valentini Giorgio, Carrafiello Gianpaolo

2021-Dec-01

COVID-19, SARS-CoV-2, abdominal imaging findings, abdominal symptoms

Public Health Public Health

Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19.

In Machine learning with applications

The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has been recently found responsible for the pandemic outbreak of a novel coronavirus disease (COVID-19). In this work, a novel approach based on deep learning is proposed for identifying precursors of small active RNA molecules named microRNA (miRNA) in the genome of the novel coronavirus. Viral miRNA-like molecules have shown to modulate the host transcriptome during the infection progression, thus their identification is crucial for helping the diagnosis or medical treatment of the disease. The existence of the mature miRNAs derived from computationally predicted miRNA precursors (pre-miRNAs) in the novel coronavirus was validated with small RNA-seq data from SARS-CoV-2-infected human cells. The results demonstrate that computational models can provide accurate and useful predictions of pre-miRNAs in the SARS-CoV-2 genome, underscoring the relevance of machine learning in the response to a global sanitary emergency. Moreover, the interpretability of our model shed light on the molecular mechanisms underlying the viral infection, thus contributing to the fight against the COVID-19 pandemic and the fast development of new treatments. Our study shows how recent advances in machine learning can be used, effectively, in response to public health emergencies. The approach developed in this work could be of great help in future similar emergencies to accelerate the understanding of the singularities of any viral agent and for the development of novel therapies. Data and source code available at: https://sourceforge.net/projects/sourcesinc/files/aicovid/.

Bugnon L A, Raad J, Merino G A, Yones C, Ariel F, Milone D H, Stegmayer G

2021-Dec-15

COVID-19, Computational prediction, Deep learning, Pre-miRNAs

General General

COVID-19 detection in X-ray images using convolutional neural networks.

In Machine learning with applications

COVID-19 global pandemic affects health care and lifestyle worldwide, and its early detection is critical to control cases' spreading and mortality. The actual leader diagnosis test is the Reverse transcription Polymerase chain reaction (RT-PCR), result times and cost of these tests are high, so other fast and accessible diagnostic tools are needed. Inspired by recent research that correlates the presence of COVID-19 to findings in Chest X-ray images, this papers' approach uses existing deep learning models (VGG19 and U-Net) to process these images and classify them as positive or negative for COVID-19. The proposed system involves a preprocessing stage with lung segmentation, removing the surroundings which does not offer relevant information for the task and may produce biased results; after this initial stage comes the classification model trained under the transfer learning scheme; and finally, results analysis and interpretation via heat maps visualization. The best models achieved a detection accuracy of COVID-19 around 97%.

Arias-Garzón Daniel, Alzate-Grisales Jesús Alejandro, Orozco-Arias Simon, Arteaga-Arteaga Harold Brayan, Bravo-Ortiz Mario Alejandro, Mora-Rubio Alejandro, Saborit-Torres Jose Manuel, Serrano Joaquim Ángel Montell, de la Iglesia Vayá Maria, Cardona-Morales Oscar, Tabares-Soto Reinel

2021-Dec-15

COVID-19, Deep learning, Segmentation, Transfer learning, X-ray

General General

Evolutionary clustering and community detection algorithms for social media health surveillance.

In Machine learning with applications

The prominent rise of social networks within the past decade have become a gold mine for data mining operations seeking to model the real world through these virtual worlds. One of the most important applications that has been proposed is utilizing information generated from social networks as a supplemental health surveillance system to monitor disease epidemics. At the time this research was conducted in 2020, the COVID-19 virus had evolved into a global pandemic, forcing many countries to implement preventative measures to halt its expanse. Health surveillance has been a powerful tool in placing further preventative measures, however it is not a perfect system, and slowly collected, misidentified information can prove detrimental to these efforts. This research proposes a new potential surveillance avenue through unsupervised machine learning using dynamic, evolutionary variants of clustering algorithms DBSCAN and the Louvain method to allow for community detection in temporal networks. This technique is paired with geographical data collected directly from the social media Twitter, to create an effective and accurate health surveillance system that grows as time passes. The experimental results show that the proposed system is promising and has the potential to be an advancement on current machine learning health surveillance techniques.

Elgazzar Heba, Spurlock Kyle, Bogart Tanner

2021-Dec-15

COVID-19, Community detection, Evolutionary clustering, Health surveillance, Social networks, Unsupervised machine learning

General General

Detecting hand washing activity among activities of daily living and classification of WHO hand washing techniques using wearable devices and machine learning algorithms.

In Healthcare technology letters

During COVID-19, awareness of proper hand washing has increased significantly. It is critical that people learn the correct hand washing techniques and adopt good hand washing habits. Hence, this study proposes using wearable devices to detect hand washing activity among other daily living activities (ADLs) and classify steps proposed by the World Health Organization (WHO). Two experiments were conducted with 16 participants, aged from 20 to 31. The first experiment was hand washing following WHO regulation (ten participants), and the second experiment was performing eight ADLs (eight participants). All participants wore two wearable devices equipped with accelerometers and gyroscopes; one on each wrist. Four machine learning classifiers were compared in classifying hand washing steps in the leave-one-subject-out (LOSO) mode. The SVM model with Gaussian kernel achieved the best performance in classifying 11 washing hands steps, with an average F1-score of 0.8501. When detected among the other ADLs, hand washing following WHO regulation obtained the F1-score of 0.9871. The study demonstrates that wearable devices are feasible to detect hand washing activity and the hand washing techniques as well. The classification results of getting the soap and rubbing thumbs are low, which will be the main focus in the future study.

Zhang Yiyuan, Xue Tianwei, Liu Zhenjie, Chen Wei, Vanrumste Bart

2021-Dec

Public Health Public Health

Twitter sentiment analysis from Iran about COVID 19 vaccine.

In Diabetes & metabolic syndrome

BACKGROUND AND AIMS : The development of vaccines against COVID-19 has been a global purpose since the World Health Organization declared the pandemic. People usually use social media, especially Twitter, to transfer knowledge and beliefs on global concerns like COVID-19-vaccination, hence, Twitter is a good source for investigating public opinions. The present study aimed to assess Persian tweets to (1) analyze Iranian people's view toward COVID-19 vaccination. (2) Compare Iranian views toward a homegrown and imported COVID-19-vaccines.

METHODS : First, a total of 803278 Persian tweets were retrieved from Twitter, mentioning COVIran Barekat (the homegrown vaccine), Pfizer/BioNTech, AstraZeneca/Oxford, Moderna, and Sinopharm (imported vaccines) between April 1, 2021 and September 30, 2021. Then, we identified sentiments of retrieved tweets using a deep learning sentiment analysis model based on CNN-LSTM architecture. Finally, we investigated Iranian views toward COVID-19-vaccination.

RESULTS : (1) We found a subtle difference in the number of positive sentiments toward the homegrown and foreign vaccines, and the latter had the dominant positive polarity. (2) The negative sentiment regarding homegrown and imported vaccines seems to be increasing in some months. (3) We also observed no significant differences between the percentage of overall positive and negative opinions toward vaccination amongst Iranian people.

CONCLUSIONS : It is worrisome that the negative sentiment toward homegrown and imported vaccines increases in Iran in some months. Since public healthcare agencies aim to increase the uptake of COVID-19 vaccines to end the pandemic, they can focus on social media such as Twitter to promote positive messaging and decrease opposing views.

Bokaee Nezhad Zahra, Deihimi Mohammad Ali

2021-Dec-13

COVID-19, Public health, SARS-CoV-2, Sentiment analysis, Vaccination

General General

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction.

In PLoS computational biology

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Annapragada Akshaya V, Greenstein Joseph L, Bose Sanjukta N, Winters Bradford D, Sarma Sridevi V, Winslow Raimond L

2021-Dec-21

General General

An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty.

In Cognitive computation

The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.

Ahmed Tawsin Uddin, Jamil Mohammad Newaj, Hossain Mohammad Shahadat, Islam Raihan Ul, Andersson Karl

2021-Dec-16

Belief Rule Base, COVID-19, Transfer Learning, VGG Net, Validation Accuracy

General General

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine.

In Applied soft computing

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi's method (CHI), Ishibuchi's method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations.

Ozger Zeynep Banu, Cihan Pınar

2022-Feb

B-cell, Epitope, Fuzzy learning, SARS-CoV, SARS-CoV-2, Spike protein

General General

Finding of the factors affecting the severity of COVID-19 based on mathematical models.

In Scientific reports ; h5-index 158.0

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

Qu Jiahao, Sumali Brian, Lee Ho, Terai Hideki, Ishii Makoto, Fukunaga Koichi, Mitsukura Yasue, Nishimura Toshihiko

2021-Dec-20

General General

3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography.

In eLife

** :

** : We have used phase-contrast X-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopathological examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross section of 3:5mm were scanned at a laboratory setup as well as at a parallel beam setup at a synchrotron radiation facility. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by high resolution cone beam X-ray tomography and scanning electron microscopy. Furthermore, we implemented a fully automated analysis of the tissue structure in form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis.

Reichardt Marius, Moller Jensen Patrick, Andersen Dahl Vedrana, Bjorholm Dahl Anders, Ackermann Maximilian, Shah Harshit, Länger Florian, Werlein Christopher, Kuehnel Mark P, Jonigk Danny, Salditt Tim

2021-Dec-21

epidemiology, global health, human

General General

pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science.

In BMC bioinformatics

BACKGROUND : Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines.

RESULTS : pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook.

CONCLUSIONS : We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Guerra João Victor da Silva, Ribeiro-Filho Helder Veras, Jara Gabriel Ernesto, Bortot Leandro Oliveira, Pereira José Geraldo de Carvalho, Lopes-de-Oliveira Paulo Sérgio

2021-Dec-20

Automated pipelines, Cavity characterization, Cavity detection, Data science, Data structure, Molecular dynamics, NumPy, Python

General General

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques.

In F1000Research

Background: The Malaysian government reacted to the pandemic's economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled "Effects of COVID-19 on the Economy and Individual - Round 2," collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques-Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes-were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance.  Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

Kannan Rathimala, Wang Ivan Zhi Wei, Ong Hway Boon, Ramakrishnan Kannan, Alamsyah Andry

2021

COVID-19, Gradient Boosted Tree, customisation, data analytics, economic stimulus package, low-income households, machine learning

General General

Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT.

In Frontiers in oncology

Objective : To develop an accurate and rapid computed tomography (CT)-based interpretable AI system for the diagnosis of lung diseases.

Background : Most existing AI systems only focus on viral pneumonia (e.g., COVID-19), specifically, ignoring other similar lung diseases: e.g., bacterial pneumonia (BP), which should also be detected during CT screening. In this paper, we propose a unified sequence-based pneumonia classification network, called SLP-Net, which utilizes consecutiveness information for the differential diagnosis of viral pneumonia (VP), BP, and normal control cases from chest CT volumes.

Methods : Considering consecutive images of a CT volume as a time sequence input, compared with previous 2D slice-based or 3D volume-based methods, our SLP-Net can effectively use the spatial information and does not need a large amount of training data to avoid overfitting. Specifically, sequential convolutional neural networks (CNNs) with multi-scale receptive fields are first utilized to extract a set of higher-level representations, which are then fed into a convolutional long short-term memory (ConvLSTM) module to construct axial dimensional feature maps. A novel adaptive-weighted cross-entropy loss (ACE) is introduced to optimize the output of the SLP-Net with a view to ensuring that as many valid features from the previous images as possible are encoded into the later CT image. In addition, we employ sequence attention maps for auxiliary classification to enhance the confidence level of the results and produce a case-level prediction.

Results : For evaluation, we constructed a dataset of 258 chest CT volumes with 153 VP, 42 BP, and 63 normal control cases, for a total of 43,421 slices. We implemented a comprehensive comparison between our SLP-Net and several state-of-the-art methods across the dataset. Our proposed method obtained significant performance without a large amount of data, outperformed other slice-based and volume-based approaches. The superior evaluation performance achieved in the classification experiments demonstrated the ability of our model in the differential diagnosis of VP, BP and normal cases.

Hao Jinkui, Xie Jianyang, Liu Ri, Hao Huaying, Ma Yuhui, Yan Kun, Liu Ruirui, Zheng Yalin, Zheng Jianjun, Liu Jiang, Zhang Jingfeng, Zhao Yitian

2021

CNN, CT, ConvLSTM, deep learning, lung diseases

General General

Effect of image transformation on EfficientNet model for COVID- 19 CT image classification.

In Materials today. Proceedings

The Novel Corona Virus 2019 has drastically affected millions of people all around the world and was a huge threat to the human race since its evolution in 2019. Chest CT images are considered to be one of the indicative sources for diagnosis of COVID-19 by most of the researchers in the research community. Several researchers have proposed various models for the prediction of Covid- 19 using CT images using Artificial Intelligence based algorithms [19], [20], [21]. EfficientNet is one of the powerful Convolutional Neural Network models proposed by Mingxing Tan and Quoc [18]. The objective of this study is to explore the effect of image enhancement algorithms such as Laplace transform, Wavelet transforms, Adaptive gamma correction and Contrast limited adaptive histogram equalization (CLAHE) on Chest CT images for the classification of Covid-19 using the EfficientNet algorithm. SARS- COV-2[14] dataset is used in this study. The images were preprocessed and brightness augmented. The EfficientNet algorithm is implemented and the performance is evaluated by adding the four image enhancement algorithms. The CLAHE based EfficientNet model yielded an accuracy of 94.56%, precision of 95%, recall of 91%, and F1 of 93%. This study shows that adding a CLAHE image enhancement to the EfficientNet model improves the performance of the powerful Convolutional Neural Network model in classifying the CT images for Covid-19.

Shamila Ebenezer A, Deepa Kanmani S, Sivakumar Mahima, Jeba Priya S

2021-Dec-13

CLAHE, COVID-19, Deep learning, EfficientNet algorithm, Image Enhancement

General General

COVID-19 prediction through X-ray images using transfer learning-based hybrid deep learning approach.

In Materials today. Proceedings

Over the past few months, the campaign against COVID-19 has developed into one of the world's most sought anti-toxin treatment scheme. It is fundamental to distinguish cases of COVID-19 precisely and quickly to help avoid this pandemic from taking a wrong turn with a proper medical reasoning and solution. While Reverse-Transcription Polymerase Chain Reaction (RT-PCR) has been useful in detection of corona virus, chest X-Ray techniques has proven to be more successful and beneficial at detection of the effects of virus. With the increase in COVID patients and the X-Rays done, it is currently possible to classify the X-Ray reports with transfer learning. This paper presents a novel approach, i.e., Hybrid Convolutional Neural Network (HDCNN), which integrates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture for the finding of COVID-19 using the chest X-Ray. The transfer learning approach, namely slope weighted activation class planning (Grad-CAMs), is used with HDCNN to display images responsible for taking decisions. In this study, HDCNN is compared with other CNNs such as Inception-v3, ShuffleNet, SqueezeNet, VGG-19 and DenseNet. As a result, HDCNN has achieved an accuracy of 98.20%, precision of 97.31%, recall of 97.1% and F1 score of 0.97. Compared to other current deep learning models, the HDCNN has achieved better results, and this can be used for diagnosis purpose after proper approvals.

Kumar Mohit, Shakya Dhairyata, Kurup Vinod, Suksatan Wanich

2021-Dec-13

Convolutional Neural Network, CovidGAN, DarkCovidNet, Grad-CAMs, Recurrent Neural Network

General General

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques.

In F1000Research

Background: The Malaysian government reacted to the pandemic's economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled "Effects of COVID-19 on the Economy and Individual - Round 2," collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques-Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes-were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance.  Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

Kannan Rathimala, Wang Ivan Zhi Wei, Ong Hway Boon, Ramakrishnan Kannan, Alamsyah Andry

2021

COVID-19, Gradient Boosted Tree, customisation, data analytics, economic stimulus package, low-income households, machine learning

General General

D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images.

In Computational intelligence and neuroscience

Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual-path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2-CovidNet, is designed to assist experts in diagnosing COVID-19 by identifying chest X-ray images. D2-CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and F1-score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID-19 are 98.97%, 94.12%, and 99.84%, respectively. D2-CovidNet has fewer computation number and parameter number. Compared with other methods, D2-CovidNet can help diagnose COVID-19 more quickly and accurately.

Wang Xin, Hu Yiyang, Luo Yanhong, Wang Wei

2021

Public Health Public Health

Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People.

In Computational intelligence and neuroscience

Background : From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level.

Objective : We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages.

Methods : We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China.

Results : Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases' prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94).

Conclusion : Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.

Xie Wenxiu, Ji Meng, Zhao Mengdan, Lam Kam-Yiu, Chow Chi-Yin, Hao Tianyong

2021

General General

Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications.

In New generation computing

The recent outbreak of novel coronavirus disease (COVID-19) has resulted in healthcare crises across the globe. Moreover, the persistent and prolonged complications of post-COVID-19 or long COVID are also putting extreme pressure on hospital authorities due to the constrained healthcare resources. Out of many long-lasting post-COVID-19 complications, heart disease has been realized as the most common among COVID-19 survivors. The motivation behind this research is the limited availability of the post-COVID-19 dataset. In the current research, data related to post-COVID complications are collected by personally contacting the previously infected COVID-19 patients. The dataset is preprocessed to deal with missing values followed by oversampling to generate numerous instances, and model training. A binary classifier based on a stacking ensemble is modeled with deep neural networks for the prediction of heart diseases, post-COVID-19 infection. The proposed model is validated against other baseline techniques, such as decision trees, random forest, support vector machines, and artificial neural networks. Results show that the proposed technique outperforms other baseline techniques and achieves the highest accuracy of 93.23%. Moreover, the results of specificity (95.74%), precision (95.24%), and recall (92.05%) also prove the utility of the adopted approach in comparison to other techniques for the prediction of heart diseases.

Gupta Aditya, Jain Vibha, Singh Amritpal

2021-Dec-14

K-fold cross-validation, Machine learning, Post-COVID-19, Stacking ensemble

Radiology Radiology

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for COVID-19 Diagnosis and Severity Assessment.

In Pattern recognition

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.

Bao Guoqing, Chen Huai, Liu Tongliang, Gong Guanzhong, Yin Yong, Wang Lisheng, Wang Xiuying

2021-Dec-12

3D CNNs, COVID-19, computer tomography, deep learning, diagnosis, multitask learning, severity assessment

General General

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features.

In Journal of X-ray science and technology

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of >  92% with SVM-RBF classifier and combining deep and machine features achieves >  96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

Kannan S Rajesh, Sivakumar J, Ezhilarasi P

2021-Dec-11

Detection of COVID-19, chest X-ray, combining deep, feature selection, firefly algorithm

General General

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.

In Computers in biology and medicine

Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

Heidari Arash, Jafari Navimipour Nima, Unal Mehmet, Toumaj Shiva

2021-Dec-14

Artificial intelligence, COVID-19, Deep learning, Neural networks, Pandemic

General General

Stress prediction using micro-EMA and machine learning during COVID-19 social isolation.

In Smart health (Amsterdam, Netherlands)

Accurately predicting users' perceived stress is beneficial to aid early intervention and prevent both mental illness and physical disease during the COVID-19 pandemic. However, the existing perceived stress predicting system needs to collect a large amount of previous data for training but has a limited prediction range (i.e., next 1-2 days). Therefore, we propose a perceived stress prediction system based on the history data of micro-EMA for identifying risks 7 days earlier. Specifically, we first select and deliver an optimal set of micro-EMA questions to users every Monday, Wednesday, and Friday for reducing the burden. Then, we extract time-series features from the past micro-EMA responses and apply an Elastic net regularization model to discard redundant features. After that, selected features are fed to an ensemble prediction model for forecasting fine-grained perceived stress in the next 7 days. Experiment results show that our proposed prediction system can achieve around 4.26 (10.65% of the scale) mean absolute error for predicting the next 7 day's PSS scores, and higher than 81% accuracy for predicting the next 7 day's stress labels.

Li Huining, Zheng Enhao, Zhong Zijian, Xu Chenhan, Roma Nicole, Lamkin Steven, Von Visger Tania T, Chang Yu-Ping, Xu Wenyao

2022-Mar

Micro-EMA, Perceived stress, Prediction model

Radiology Radiology

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

In Frontiers in medicine

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Zhang Yu-Han, Hu Xiao-Fei, Ma Jie-Chao, Wang Xian-Qi, Luo Hao-Ran, Wu Zi-Feng, Zhang Shu, Shi De-Jun, Yu Yi-Zhou, Qiu Xiao-Ming, Zeng Wen-Bing, Chen Wei, Wang Jian

2021

COVID-19, computed tomography, deep learning, pneumonia, pulmonary infectious disease

Public Health Public Health

Evaluating Rumor Debunking Effectiveness During the COVID-19 Pandemic Crisis: Utilizing User Stance in Comments on Sina Weibo.

In Frontiers in public health

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media. Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods. Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method. Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

Wang Xin, Chao Fan, Yu Guang

2021

COVID-19, debunking, effectiveness, false information, rumor, social media, stance detection

General General

An interactome landscape of SARS-CoV-2 virus-human protein-protein interactions by protein sequence-based multi-label classifiers

bioRxiv Preprint

The new coronavirus species, SARS-CoV-2, caused an unprecedented global pandemic of COVID-19 disease since late December 2019. A comprehensive characterization of protein-protein interactions (PPIs) between SARS-CoV-2 and human cells is a key to understanding the infection and preventing the disease. Here we present a novel approach to predict virus-host PPIs by multi-label machine learning classifiers of random forests and XGBoost using amino acid composition profiles of virus and human proteins. Our models harness a large-scale database of Viruses.STRING with >80,000 virus-host PPIs along with evidence scores for multi-level evidence prediction, which is distinct from predicting binary interactions in previous studies. Our multi-label classifiers are based on 5 evidence levels binned from evidence scores. Our best model of XGBoost achieves 74% AUC and 68% accuracy on average in 10-fold cross validation. The most important amino acids are cysteine and histidine. In addition, our model predicts experimental PPIs with higher evidence level than text mining-based PPIs. We then predict evidence levels of ~2,000 SARS-CoV-2 virus-human PPIs from public experimental proteomics data. Interactions with SARS-CoV-2 Nsp7b show high evidence. We also predict evidence levels of all pairwise PPIs of ~550,000 between the SARS-CoV-2 and human proteomes to provide a draft virus-host interactome landscape for SARS-CoV-2 infection in humans in a comprehensive and unbiased way in silico. Most human proteins from 140 highest evidence predictions interact with SARS-CoV-2 Nsp7, Nsp1, and ORF14, with significant enrichment in the top 2 pathways of vascular smooth muscle contraction (CALD1, NPR2, CALML3) and Myc targets (CBX3, PES1). Our prediction also suggests that histone H2A components are targeted by multiple SARS-CoV-2 proteins.

Lee, H.-J.

2021-12-20

General General

A Deep Learning Based Workflow for Detection of Lung Nodules With Chest Radiograph

ArXiv Preprint

PURPOSE: This study aimed to develop a deep learning-based tool to detect and localize lung nodules with chest radiographs(CXRs). We expected it to enhance the efficiency of interpreting CXRs and reduce the possibilities of delayed diagnosis of lung cancer. MATERIALS AND METHODS: We collected CXRs from NCKUH database and VBD, an open-source medical image dataset, as our training and validation data. A number of CXRs from the Ministry of Health and Welfare(MOHW) database served as our test data. We built a segmentation model to identify lung areas from CXRs, and sliced them into 16 patches. Physicians labeled the CXRs by clicking the patches. These labeled patches were then used to train and fine-tune a deep neural network(DNN) model, classifying the patches as positive or negative. Finally, we test the DNN model with the lung patches of CXRs from MOHW. RESULTS: Our segmentation model identified the lung regions well from the whole CXR. The Intersection over Union(IoU) between the ground truth and the segmentation result was 0.9228. In addition, our DNN model achieved a sensitivity of 0.81, specificity of 0.82, and AUROC of 0.869 in 98 of 125 cases. For the other 27 difficult cases, the sensitivity was 0.54, specificity 0.494, and AUROC 0.682. Overall, we obtained a sensitivity of 0.78, specificity of 0.79, and AUROC 0.837. CONCLUSIONS: Our two-step workflow is comparable to state-of-the-art algorithms in the sensitivity and specificity of localizing lung nodules from CXRs. Notably, our workflow provides an efficient way for specialists to label the data, which is valuable for relevant researches because of the relative rarity of labeled medical image data.

Yang Tai

2021-12-19

General General

Data Augmentation for Mental Health Classification on Social Media

ArXiv Preprint

The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user generated text on social media platforms. Academic re searchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification. Among the existing well established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experiments mental results show significant improvements in classifiers performance when trained on the augmented data.

Gunjan Ansari, Muskan Garg, Chandni Saxena

2021-12-19

General General

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans.

In Computers in biology and medicine

Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.

Shaik Nagur Shareef, Cherukuri Teja Krishna

2021-Dec-11

Computerized tomography (CT), Coronavirus disease 2019, Ensemble classifier, Pre-trained models, Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), Transfer learning

Public Health Public Health

User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Online health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs.

OBJECTIVE : This study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors.

METHODS : We concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings.

RESULTS : Our analysis showed that the number of reads per thread on each forum followed gamma distribution (HL=0, HB=0, and HD=0); the number of replies on each forum followed exponential distribution (adjusted RL2=0.946, adjusted RB2=0.958, and adjusted RD2=0.971); and the number of threads a user is involved with (adjusted RL2=0.978, adjusted RB2=0.964, and adjusted RD2=0.970), the number of followers of a user (adjusted RL2=0.989, adjusted RB2=0.962, and adjusted RD2=0.990), and a user's degrees (adjusted RL2=0.997, adjusted RB2=0.994, and adjusted RD2=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity (ρ=0.927; P<.001) in terms of the relative thread posting frequencies during each hour of the day. Besides, the study showed that all 3 forums exhibited the small-world effect (mean σL=517.15, mean σB=275.23, and mean σD=525.18) and scale-free characteristics, while the global clustering coefficients were lower than those of counterpart international OHCs. The study also discovered several hot topics commonly shared among the 3 disease forums, such as disease treatment, disease examination, and diagnosis. In particular, the study found that after the outbreak of COVID-19, users on the LCF and BCF were much more likely to bring up COVID-19-related issues while discussing their medical issues.

CONCLUSIONS : UGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers.

Lei Yuqi, Xu Songhua, Zhou Linyun

2021-Dec-15

online health community, social network analysis, user behaviors, user-generated content, weighted knowledge network

Public Health Public Health

Artificial Intelligence in Vaccine and Drug Design.

In Methods in molecular biology (Clifton, N.J.)

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.

Thomas Sunil, Abraham Ann, Baldwin Jeremy, Piplani Sakshi, Petrovsky Nikolai

2022

Artificial intelligence, AI, Artificial neural networks, Deep learning, Machine learning, Vaccine design

Pathology Pathology

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.

In Frontiers in genetics ; h5-index 62.0

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Harigua-Souiai Emna, Heinhane Mohamed Mahmoud, Abdelkrim Yosser Zina, Souiai Oussama, Abdeljaoued-Tej Ines, Guizani Ikram

2021

SARS-CoV-2, artificial neural network, deep learning, drug discovery and repurposing, graph convoluational networks, machine learning

Surgery Surgery

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19.

In Frontiers in physiology

Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models-one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased-sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased-sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

Chung Heewon, Park Chul, Kang Wu Seong, Lee Jinseok

2021

COVID-19, artificial intelligence bias, feature importance, gender dependent bias, severity prediction

General General

Food service industry in the era of COVID-19: trends and research implications.

In Nutrition research and practice

Coronavirus disease 2019 (COVID-19) is a new type of respiratory disease that has been announced as a pandemic. The COVID-19 outbreak has changed the way we live. It has also changed the food service industry. This study aimed to identify trends in the food and food service industry after the COVID-19 outbreak and suggest research themes induced by industry trends. This study investigated the industry and academic information on the food and food service industry and societal trends resulting from the COVID-19 outbreak. The most noticeable changes in the food industry include the explosive increase in home meal replacement, meal-kit consumption, online orders, take-out, and drive-through. The adoption of technologies, including robots and artificial intelligence, has also been noted. Such industry trends are discussed in this paper from a research perspective, including consumer, employee, and organizational strategy perspectives. This study reviews the changes in the food service industry after COVID-19 and the implications that these changes have rendered to academia. The paper concludes with future expectations that would come in the era of COVID-19.

Lee Seoki, Ham Sunny

2021-Dec

COVID-19, Food services, Pandemics, Technology

General General

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network.

In Biomedical signal processing and control

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

Wong Pak Kin, Yan Tao, Wang Huaqiao, Chan In Neng, Wang Jiangtao, Li Yang, Ren Hao, Wong Chi Hong

2022-Mar

Attention mechanism, COVID-19, Chest computed tomography, Multi-scale convolution neural network, Pneumonia identification

General General

Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network.

In Biocybernetics and biomedical engineering

Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent ion of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN's sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images.

Gour Mahesh, Jain Sweta

2021-Dec-09

Automatic screening, CT scan images, Chest X-ray images, Deep learning, Keyword: COVID-19, Softmax classifier, Stacked ensemble

Public Health Public Health

Prediction of asymptomatic COVID-19 infections based on complex network.

In Optimal control applications & methods

Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.

Chen Yili, He Haoming, Liu Dakang, Zhang Xie, Wang Jingpei, Yang Yixiao

2021-Oct-21

COVID‐19, Trustrank algorithm, complex network, machine learning

General General

A Covid-19's integrated herd immunity (CIHI) based on classifying people vulnerability.

In Computers in biology and medicine

Unfortunately, Covid-19 has infected millions of people very quickly, and it continues to infect people and spreads rapidly. Although there are some common symptoms of Covid-19, its effect varies from one individual to another. Estimating the severity of the infection has become a critical need as it can guide the decision makers to take an accurate and timely response. It will be valuable to provide early warning before infection takes place about susceptibility to the disease, especially since the lack of symptoms is a feature of the Covid-19 pandemic. Asymptomatic patients are considered as "silent diffusers" of the virus; hence, detecting people who will be asymptomatic before actual infection takes place will certainly safe the society from the uncontrolled and unseen spread of the virus. People can be classified based on their vulnerability to Covid-19 even before they are infected. Accordingly, precautionary measures can be taken individually based on the persons' Covid-19 susceptibility. This paper introduces a Covid-19's Integrated Herd Immunity (CIHI) strategy. The aim of CIHI is to keep the society safe with the minimal losses even with the existence of Covid-19. This can be accomplished by two basic factors; the first is an accurate prediction of the cases who will be asymptomatic if they were infected by the virus, while the second is to take suitable precautions for those who are predicted to be badly affected by the virus even before the actual infection takes place. CIHI is realized through a new classification strategy called Distance Based Classification Strategy (DBCS) which classifies people based on their vulnerability to Covid-19 infection. The proposed DBCS classifies individuals into six different types, then suitable precautionary measures can be taken for every type. DBCS can also identify future symptomatic and asymptomatic cases. In fact, DBCS consists of three sequential phases, which are; (i) Outlier Rejection Phase (ORP) using Hybrid Outlier Rejection (HOR) method, (ii) Feature Selection Phase (FSP) using Hybrid Feature Selection (HFS) method, and (iii) Classification Phase (CP) using Accumulative K-Nearest Neighbors (AKNN). DBCS has been compared with recent Covid-19 diagnosing techniques based on "NileDS" dataset. Experimental results have proven the efficiency and applicability of the proposed strategy as it provides the best classification accuracy.

Rabie Asmaa H, Saleh Ahmed I, Mansour Nehal A

2021-Dec-07

Classification, Covid-19, Feature selection, KNN

General General

Weakly-supervised lesion analysis with a CNN-based framework for COVID-19.

In Physics in medicine and biology

Lesions of COVID-19 can be visualized clearly by chest CT images, therefore, providing valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task, requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions. Specifically, this framework employs a deep learning-based diagnosis branch for the classification of the CT image and then leverages a lesion identification branch to capture multiple types of lesions. We verify our framework on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

Wu Kaichao, Jelfs Beth, Ma Xiangyuan, Ke Ruitian, Tan Xuerui, Fang Qiang

2021-Dec-14

COVID-19, Chest CT image, GGO, Lesion identification, Weakly-supervised

Radiology Radiology

Multi-COVID-Net: Multi-objective optimized network for COVID-19 diagnosis from chest X-ray images.

In Applied soft computing

Coronavirus Disease 2019 (COVID-19) had already spread worldwide, and healthcare services have become limited in many countries. Efficient screening of hospitalized individuals is vital in the struggle toward COVID-19 through chest radiography, which is one of the important assessment strategies. This allows researchers to understand medical information in terms of chest X-ray (CXR) images and evaluate relevant irregularities, which may result in a fully automated identification of the disease. Due to the rapid growth of cases every day, a relatively small number of COVID-19 testing kits are readily accessible in health care facilities. Thus it is imperative to define a fully automated detection method as an instant alternate treatment possibility to limit the occurrence of COVID-19 among individuals. In this paper, a two-step Deep learning (DL) architecture has been proposed for COVID-19 diagnosis using CXR. The proposed DL architecture consists of two stages, "feature extraction and classification". The "Multi-Objective Grasshopper Optimization Algorithm (MOGOA)" is presented to optimize the DL network layers; hence, these networks have named as "Multi-COVID-Net". This model classifies the Non-COVID-19, COVID-19, and pneumonia patient images automatically. The Multi-COVID-Net has been tested by utilizing the publicly available datasets, and this model provides the best performance results than other state-of-the-art methods.

Goel Tripti, Murugan R, Mirjalili Seyedali, Chakrabartty Deba Kumar

2021-Dec-09

CNN, COVID-19, Chest X-ray images, Deep learning, MOGOA, Multi-objective optimization

General General

Face mask detection and classification via deep transfer learning.

In Multimedia tools and applications

Wearing a mask is an important way of preventing COVID-19 transmission and infection. German researchers found that wearing masks can effectively reduce the infection rate of COVID-19 by 40%. However, the detection of face mask-wearing in the real world is affected by factors such as light, occlusion, and multi-object. The detection effect is poor, and the wearing of cotton masks, sponge masks, scarves and other items greatly reduces the personal protection effect. Therefore, this paper proposes a new algorithm for mask detection and classification that fuses transfer learning and deep learning. Firstly, this paper proposes a new algorithm for face mask detection that integrates transfer learning and Efficient-Yolov3, using EfficientNet as the backbone feature extraction network, and choosing CIoU as the loss function to reduce the number of network parameters and improve the accuracy of mask detection. Secondly, this paper divides the mask into two categories of qualified masks (N95 masks, disposable medical masks) and unqualified masks (cotton masks, sponge masks, scarves, etc.), creates a mask classification data set, and proposes a new mask classification algorithm that the combines transfer learning and MobileNet, enhances the generalization of the model and solves the problem of small data size and easy overfitting. Experiments on the public face mask detection data set show that the proposed algorithm has a better performance than existing algorithms. In addition, experiments are performed on the created mask classification data set. The mask classification accuracy of the proposed algorithm is 97.84%, which is better than other algorithms.

Su Xueping, Gao Meng, Ren Jie, Li Yunhong, Dong Mian, Liu Xi

2021-Dec-09

COVID-19, Mask classification, Masked face dataset, Masked face detection

General General

Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.

In Scientific reports ; h5-index 158.0

Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer's output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text], [Formula: see text], and [Formula: see text]. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively).

Sitaula Chiranjibi, Shahi Tej Bahadur, Aryal Sunil, Marzbanrad Faezeh

2021-Dec-13

Public Health Public Health

Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base.

In Proceedings of the National Academy of Sciences of the United States of America

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Astley Christina M, Tuli Gaurav, Mc Cord Kimberly A, Cohn Emily L, Rader Benjamin, Varrelman Tanner J, Chiu Samantha L, Deng Xiaoyi, Stewart Kathleen, Farag Tamer H, Barkume Kristina M, LaRocca Sarah, Morris Katherine A, Kreuter Frauke, Brownstein John S

2021-Dec-21

COVID-19 surveillance, SARS-CoV-2 testing, global health, human social sensing

Public Health Public Health

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination.

In Proceedings of the National Academy of Sciences of the United States of America

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Salomon Joshua A, Reinhart Alex, Bilinski Alyssa, Chua Eu Jing, La Motte-Kerr Wichada, Rönn Minttu M, Reitsma Marissa B, Morris Katherine A, LaRocca Sarah, Farag Tamer H, Kreuter Frauke, Rosenfeld Roni, Tibshirani Ryan J

2021-Dec-21

COVID-19, SARS-CoV2, survey

Public Health Public Health

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

In Proceedings of the National Academy of Sciences of the United States of America

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

McDonald Daniel J, Bien Jacob, Green Alden, Hu Addison J, DeFries Nat, Hyun Sangwon, Oliveira Natalia L, Sharpnack James, Tang Jingjing, Tibshirani Robert, Ventura Valérie, Wasserman Larry, Tibshirani Ryan J

2021-Dec-21

COVID-19, digital surveillance, forecasting, hotspot prediction, time series

Public Health Public Health

An open repository of real-time COVID-19 indicators.

In Proceedings of the National Academy of Sciences of the United States of America

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Reinhart Alex, Brooks Logan, Jahja Maria, Rumack Aaron, Tang Jingjing, Agrawal Sumit, Al Saeed Wael, Arnold Taylor, Basu Amartya, Bien Jacob, Cabrera Ángel A, Chin Andrew, Chua Eu Jing, Clark Brian, Colquhoun Sarah, DeFries Nat, Farrow David C, Forlizzi Jodi, Grabman Jed, Gratzl Samuel, Green Alden, Haff George, Han Robin, Harwood Kate, Hu Addison J, Hyde Raphael, Hyun Sangwon, Joshi Ananya, Kim Jimi, Kuznetsov Andrew, La Motte-Kerr Wichada, Lee Yeon Jin, Lee Kenneth, Lipton Zachary C, Liu Michael X, Mackey Lester, Mazaitis Kathryn, McDonald Daniel J, McGuinness Phillip, Narasimhan Balasubramanian, O’Brien Michael P, Oliveira Natalia L, Patil Pratik, Perer Adam, Politsch Collin A, Rajanala Samyak, Rucker Dawn, Scott Chris, Shah Nigam H, Shankar Vishnu, Sharpnack James, Shemetov Dmitry, Simon Noah, Smith Benjamin Y, Srivastava Vishakha, Tan Shuyi, Tibshirani Robert, Tuzhilina Elena, Van Nortwick Ana Karina, Ventura Valérie, Wasserman Larry, Weaver Benjamin, Weiss Jeremy C, Whitman Spencer, Williams Kristin, Rosenfeld Roni, Tibshirani Ryan J

2021-Dec-21

digital surveillance, internet surveys, medical insurance claims, open data

General General

COVID-19 Electrocardiograms Classification using CNN Models

ArXiv Preprint

With the periodic rise and fall of COVID-19 and numerous countries being affected by its ramifications, there has been a tremendous amount of work that has been done by scientists, researchers, and doctors all over the world. Prompt intervention is keenly needed to tackle the unconscionable dissemination of the disease. The implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the fundamentals of deep learning algorithms. In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization of Electrocardiogram (ECG) data with the integration of deep learning algorithms, specifically the Convolutional Neural Network (CNN) models. Several CNN models have been utilized in this proposed framework, including VGG16, VGG19, InceptionResnetv2, InceptionV3, Resnet50, and Densenet201. The VGG16 model has outperformed the rest of the models, with an accuracy of 85.92%. Our results show a relatively low accuracy in the rest of the models compared to the VGG16 model, which is due to the small size of the utilized dataset, in addition to the exclusive utilization of the Grid search hyperparameters optimization approach for the VGG16 model only. Moreover, our results are preparatory, and there is a possibility to enhance the accuracy of all models by further expanding the dataset and adapting a suitable hyperparameters optimization technique.

Ismail Shahin, Ali Bou Nassif, Mohamed Bader Alsabek

2021-12-15

oncology Oncology

Increased risk of COVID-19-related admissions in patients with active solid organ cancer in the West Midlands region of the UK: a retrospective cohort study.

In BMJ open

OBJECTIVE : Susceptibility of patients with cancer to COVID-19 pneumonitis has been variable. We aim to quantify the risk of hospitalisation in patients with active cancer and use a machine learning algorithm (MLA) and traditional statistics to predict clinical outcomes and mortality.

DESIGN : Retrospective cohort study.

SETTING : A single UK district general hospital.

PARTICIPANTS : Data on total hospital admissions between March 2018 and June 2020, all active cancer diagnoses between March 2019 and June 2020 and clinical parameters of COVID-19-positive admissions between March 2020 and June 2020 were collected. 526 COVID-19 admissions without an active cancer diagnosis were compared with 87 COVID-19 admissions with an active cancer diagnosis.

PRIMARY AND SECONDARY OUTCOME MEASURES : 30-day and 90-day post-COVID-19 survival.

RESULTS : In total, 613 patients were enrolled with male to female ratio of 1:6 and median age of 77 years. The estimated infection rate of COVID-19 was 87 of 22 729 (0.4%) in the patients with cancer and 526 of 404 379 (0.1%) in the population without cancer (OR of being hospitalised with COVID-19 if having cancer is 2.942671 (95% CI: 2.344522 to 3.693425); p<0.001). Survival was reduced in patients with cancer with COVID-19 at 90 days. R-Studio software determined the association between cancer status, COVID-19 and 90-day survival against variables using MLA. Multivariate analysis showed increases in age (OR 1.039 (95% CI: 1.020 to 1.057), p<0.001), urea (OR 1.005 (95% CI: 1.002 to 1.007), p<0.001) and C reactive protein (CRP) (OR 1.065 (95% CI: 1.016 to 1.116), p<0.008) are associated with greater 30-day and 90-day mortality. The MLA model examined the contribution of predictive variables for 90-day survival (area under the curve: 0.749); with transplant patients, age, male gender and diabetes mellitus being predictors of greater mortality.

CONCLUSIONS : Active cancer diagnosis has a threefold increase in risk of hospitalisation with COVID-19. Increased age, urea and CRP predict mortality in patients with cancer. MLA complements traditional statistical analysis in identifying prognostic variables for outcomes of COVID-19 infection in patients with cancer. This study provides proof of concept for MLA in risk prediction for COVID-19 in patients with cancer and should inform a redesign of cancer services to ensure safe delivery of cancer care.

Akingboye Akinfemi, Mahmood Fahad, Amiruddin Nabeel, Reay Michael, Nightingale Peter, Ogunwobi Olorunseun O

2021-Dec-13

COVID-19, oncology, risk management

General General

A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19

ArXiv Preprint

The World Health Organization (WHO) has recommended wearing face masks as one of the most effective measures to prevent COVID-19 transmission. In many countries, it is now mandatory to wear face masks, specially in public places. Since manual monitoring of face masks is often infeasible in the middle of the crowd, automatic detection can be beneficial. To facilitate that, we explored a number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask detection and evaluated them on two benchmark datasets. We also evaluated transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this context. We find that while the performances of all the models are quite good, transfer learning models achieve the best performance. Transfer learning improves the performance by 0.10\%--0.40\% with 30\% less training time. Our experiment also shows these high-performing models are not quite robust for real-world cases where the test dataset comes from a different distribution. Without any fine-tuning, the performance of these models drops by 47\% in cross-domain settings.

Junaed Younus Khan, Md Abdullah Al Alamin

2021-12-15

General General

COVID-19 patient diagnosis and treatment data mining algorithm based on association rules.

In Expert systems

Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID-19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID-19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre-process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID-19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision-maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID-19, CHD and hypertension, while 46.5% of people with COVID-19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID-19 disease.

Shan Zicheng, Miao Wei

2021-Oct-26

COVID‐19 patients, association rules, data warehouse, diagnosis treatment data mining, online analytical processing

General General

The role of contemporary digital tools and technologies in Covid-19 crisis: An exploratory analysis.

In Expert systems

Following the Covid-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the Coivd-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing Covid-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the Covid-19 crisis.

Subramanian Malliga, Shanmuga Vadivel Kogilavani, Hatamleh Wesam Atef, Alnuaim Abeer Ali, Abdelhady Mohamed, V E Sathishkumar

2021-Oct-06

Covid‐19, artificial intelligence, augmented reality, big data, blockchain, cloud computing, deep learning, intelligent Mobile apps and 5G, internet of things, machine learning, robots and drones, sensors, virtual reality

General General

Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images.

In Expert systems

The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K-nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG-19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID-19.

Mubarak Auwalu Saleh, Serte Sertan, Al-Turjman Fadi, Ameen Zubaida Sa’id, Ozsoz Mehmet

2021-Sep-29

COVID‐19, LBP, deep learning, feature extraction, machine learning

General General

Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores.

In Annals of intensive care ; h5-index 37.0

BACKGROUND : Predicting outcomes of critically ill intensive care unit (ICU) patients with coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU stays.

METHODS : The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID-ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14.

RESULTS : Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICU-entry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models. For the test dataset, SOSIC-7's area under the ROC curve was slightly higher (0.80 [0.74-0.86]) than those for SOSIC-1 (0.76 [0.71-0.81]) and SOSIC-14 (0.76 [0.68-0.83]). Similarly, SOSIC-1 and SOSIC-7 had excellent calibration curves, with similar Brier scores for the three models.

CONCLUSION : The SOSIC scores showed that entering 15 to 27 baseline and dynamic clinical parameters into an automatable XGBoost algorithm can potentially accurately predict the likely 90-day mortality post-ICU admission (sosic.shinyapps.io/shiny). Although external SOSIC-score validation is still needed, it is an additional tool to strengthen decisions about life-sustaining treatments and informing family members of likely prognosis.

Schmidt Matthieu, Guidet Bertrand, Demoule Alexandre, Ponnaiah Maharajah, Fartoukh Muriel, Puybasset Louis, Combes Alain, Hajage David

2021-Dec-11

Acute respiratory distress syndrome, COVID-19, Mechanical ventilation, Outcome, Predictive survival model

General General

Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization.

In Bioinformatics (Oxford, England)

MOTIVATION : Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms.

RESULTS : In this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with 'warp' modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data.

AVAILABILITYAND IMPLEMENTATION : https://github.com/xulabs/aitom.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Bandyopadhyay Hmrishav, Deng Zihao, Ding Leiting, Liu Sinuo, Uddin Mostofa Rafid, Zeng Xiangrui, Behpour Sima, Xu Min

2021-Nov-23

General General

Constructing tongue coating recognition model using deep transfer learning to assist syndromes diagnosis and its potential in noninvasive ethnopharmacological evaluation.

In Journal of ethnopharmacology ; h5-index 59.0

ETHNOPHARMACOLOGICAL RELEVANCE : Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndromes diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory.

AIM OF THE STUDY : The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19.

MATERIALS AND METHODS : Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19.

RESULTS : The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet.

CONCLUSIONS : Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.

Wang Xu, Wang Xinrong, Lou Yanni, Liu Jingwei, Huo Shirui, Pang Xiaohan, Wang Weilu, Wu Chaoyong, Chen Yufeng, Chen Yu, Chen Aiping, Bi Fukun, Xing Weiying, Deng Qingqiong, Jia Liqun, Chen Jianxin

2021-Dec-08

Artificial intelligence, COVID-19, Deep transfer learning, Greasy tongue coating, Tongue diagnosis, Traditional Chinese medicine

General General

Deep mutational scanning for therapeutic antibody engineering.

In Trends in pharmacological sciences ; h5-index 70.0

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.

Hanning Kyrin R, Minot Mason, Warrender Annmaree K, Kelton William, Reddy Sai T

2021-Dec-09

antibodies, machine learning, mutagenesis, protein engineering

General General

Willingness to vaccinate against SARS-CoV-2: The role of reasoning biases and conspiracist ideation.

In Vaccine ; h5-index 70.0

** : BACKGR1OUND: Widespread vaccine hesitancy and refusal complicate containment of the SARS-CoV-2 pandemic. Extant research indicates that biased reasoning and conspiracist ideation discourage vaccination. However, causal pathways from these constructs to vaccine hesitancy and refusal remain underspecified, impeding efforts to intervene and increase vaccine uptake.

METHOD : 554 participants who denied prior SARS-CoV-2 vaccination completed self-report measures of SARS-CoV-2 vaccine intentions, conspiracist ideation, and constructs from the Health Belief Model of medical decision-making (such as perceived vaccine dangerousness) along with tasks measuring reasoning biases (such as those concerning data gathering behavior). Cutting-edge machine learning algorithms (Greedy Fast Causal Inference) and psychometric network analysis were used to elucidate causal pathways to (and from) vaccine intentions.

RESULTS : Results indicated that a bias toward reduced data gathering during reasoning may cause paranoia, increasing the perceived dangerousness of vaccines and thereby reducing willingness to vaccinate. Existing interventions that target data gathering and paranoia therefore hold promise for encouraging vaccination. Additionally, reduced willingness to vaccinate was identified as a likely cause of belief in conspiracy theories, subverting the common assumption that the opposite causal relation exists. Finally, perceived severity of SARS-CoV-2 infection and perceived vaccine dangerousness (but not effectiveness) were potential direct causes of willingness to vaccinate, providing partial support for the Health Belief Model's applicability to SARS-CoV-2 vaccine decisions.

CONCLUSIONS : These insights significantly advance our understanding of the underpinnings of vaccine intentions and should scaffold efforts to prepare more effective interventions on hesitancy for deployment during future pandemics.

Bronstein Michael V, Kummerfeld Erich, MacDonald Angus, Vinogradov Sophia

2021-Dec-04

COVID-19, Conspiracy theories, GFCI, Reasoning, SARS-CoV-2, Vaccines

General General

Performance of a computer aided diagnosis system for SARS-CoV-2 pneumonia based on ultrasound images.

In European journal of radiology ; h5-index 47.0

PURPOSE : In this study we aimed to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of SARS-CoV-2 virus syndrome on Lung ultrasonography (LUS).

METHOD : A CAD system is developed based on a transfer learning of a residual network (ResNet) to extract features on LUS and help radiologists to distinguish SARS-CoV-2 virus syndrome from healthy and non-SARS-CoV-2 pneumonia. A publicly available LUS dataset for SARS-CoV-2 virus syndrome consisting of 3909 images has been employed. Six radiologists with different experiences participated in the experiment. A comprehensive LUS data set was constructed and employed to train and verify the proposed method. Several metrics such as accuracy, recall, precision, and F1-score, are used to evaluate the performance of the proposed CAD approach. The performances of the radiologists with and without the help of CAD are also evaluated quantitively. The p-values of the t-test shows that with the help of the CAD system, both junior and senior radiologists significantly improve their diagnosis performance on both balanced and unbalanced datasets.

RESULTS : Experimental results indicate the proposed CAD approach and the machine features from it can significantly improve the radiologists' performance in the SARS-CoV-2 virus syndrome diagnosis. With the help of the proposed CAD system, the junior and senior radiologists achieved F1-score values of 91.33% and 95.79% on balanced dataset and 94.20% and 96.43% on unbalanced dataset. The proposed approach is verified on an independent test dataset and reports promising performance.

CONCLUSIONS : The proposed CAD system reports promising performance in facilitating radiologists' diagnosis SARS-CoV-2 virus syndrome and might assist the development of a fast, accessible screening method for pulmonary diseases.

Shang Shiyao, Huang Chunwang, Yan Wenxiao, Chen Rumin, Cao Jinglin, Zhang Yukun, Guo Yanhui, Du Guoqing

2021-Nov-23

Computer aided diagnosis, Deep learning, Lung ultrasound, SARS-CoV-2 virus syndrome diagnosis

General General

Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus.

In Small science

The recent global spread of COVID-19 stresses the importance of developing diagnostic testing that is rapid and does not require specialized laboratories. In this regard, nanomaterial thin-film-based immunosensors fabricated via solution processing are promising, potentially due to their mass manufacturability, on-site detection, and high sensitivity that enable direct detection of virus without the need for molecular amplification. However, thus far, thin-film-based biosensors have been fabricated without properly analyzing how the thin-film properties are correlated with the biosensor performance, limiting the understanding of property-performance relationships and the optimization process. Herein, the correlations between various thin-film properties and the sensitivity of carbon nanotube thin-film-based immunosensors are systematically analyzed, through which optimal sensitivity is attained. Sensitivities toward SARS-CoV-2 nucleocapsid protein in buffer solution and in the lysed virus are 0.024 [fg/mL]-1 and 0.048 [copies/mL]-1, respectively, which are sufficient for diagnosing patients in the early stages of COVID-19. The technique, therefore, can potentially elucidate complex relationships between properties and performance of biosensors, thereby enabling systematic optimization to further advance the applicability of biosensors for accurate and rapid point-of-care (POC) diagnosis.

Kim Su Yeong, Lee Jeong-Chan, Seo Giwan, Woo Jun Hee, Lee Minho, Nam Jaewook, Sim Joo Yong, Kim Hyung-Ryong, Park Edmond Changkyun, Park Steve

2021-Nov-16

SARS-CoV-2, biosensors, carbon nanotubes, machine learning, solution shearing

General General

Data sharing and collaborations with Telco data during the COVID-19 pandemic: A Vodafone case study.

In Data & policy

With the outbreak of COVID-19 across Europe, anonymized telecommunications data provides a key insight into population level mobility and assessing the impact and effectiveness of containment measures. Vodafone's response across its global footprint was fast and delivered key new metrics for the pandemic that have proven to be useful for a number of external entities. Cooperation with national governments and supra-national entities to help fight the COVID-19 pandemic was a key part of Vodafone's response, and in this article the different methodologies developed are analyzed, as well as the key collaborations established in this context. In this article we also analyze the regulatory challenges found, and how these can pose a risk of the full benefits of these insights not being harnessed, despite clear and efficient Privacy and Ethics assessments to ensure individual safety and data privacy.

Lourenco Pedro Rente, Kaur Gurjeet, Allison Matthew, Evetts Terry

2021

CDRs, call detail records, COVID-19, KPI, key performance indicator, OD, origin destination, ROG, Radius of Gyration, data ethics, data privacy, data sharing and collaborations, mobility insights

General General

Understanding the emotional response to Covid-19 information in news and social media: A mental health perspective.

In Human behavior and emerging technologies

The impact of the Covid-19 pandemic and ensuing social restrictions has been profound, affecting the health, livelihoods, and wellbeing of populations worldwide. Studies have shown widespread effects on mental health, with an increase in stress, loneliness, and depression symptoms related to the pandemic. Media plays a critical role in containing and managing crises, by informing society and fostering positive behavior change. Social restrictions have led to a large increase in reliance on online media channels, and this can influence mental health and wellbeing. Anxiety levels, for instance, may be exacerbated by exposure to Covid-related content, contagion of negative sentiment among social networks, and "fake news." In some cases, this may trigger abstinence, leading to isolation and limited access to vital information. To be able to communicate distressing news during crises while protecting the wellbeing of individuals is not trivial; it requires a deeper understanding of people's emotional response to online and social media content. This paper selectively reviews research into consequences of social media usage and online news consumption for wellbeing and mental health, focusing on and discussing their effects in the context of the pandemic. Advances in Artificial Intelligence and Data Science, for example, Natural Language Processing, Sentiment Analysis, and Emotion Recognition, are discussed as useful methods for investigating effects on population mental health as the pandemic situation evolves. We present suggestions for future research, and for using these advances to assess large data sets of users' online content, to potentially inform strategies that enhance the mental health of social media users going forward.

Jones Rosalind, Mougouei Davoud, Evans Simon L

2021-Oct-28

Covid‐19, artificial intelligence, coronavirus, data science, emotions, mental health, natural language processing, news, pandemic, social media

General General

COVID-19 and black fungus: Analysis of the public perceptions through machine learning.

In Engineering reports : open access

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID-19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this SVM model was used to predict the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this article found public perceptions towards black fungus during COVID-19 pandemic belong mostly to sad (n= 2370, 36.59%), followed by joy (n = 2095, 32.34%), fear (n = 1914, 29.55%) and anger (n = 98, 1.51%). This article also found that public perceptions are varied to some critical concerns like education, lockdown, hospital, oxygen, quarantine, and vaccine. For example, people mostly exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen. Again, it was found that mass people have an ignorance tendency to lockdown, COVID-19 restrictions, and prescribed hygiene rules although the coronavirus and black fungus infection rates broke the previous infection records.

Imtiaz Khan Nafiz, Mahmud Tahasin, Nazrul Islam Muhammad

2021-Nov-14

COVID‐19, black fungus, data mining, machine learning, mucormycosis, sentiment analysis, support vector machine

Cardiology Cardiology

Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study.

In Frontiers in cardiovascular medicine

Background: The relationship between cardiac functions and the fatal outcome of coronavirus disease 2019 (COVID-19) is still largely underestimated. We aim to explore the role of heart failure (HF) and NT-proBNP in the prognosis of critically ill patients with COVID-19 and construct an easy-to-use predictive model using machine learning. Methods: In this multicenter and prospective study, a total of 1,050 patients with clinical suspicion of COVID-19 were consecutively screened. Finally, 402 laboratory-confirmed critically ill patients with COVID-19 were enrolled. A "triple cut-point" strategy of NT-proBNP was applied to assess the probability of HF. The primary outcome was 30-day all-cause in-hospital death. Prognostic risk factors were analyzed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression, further formulating a nomogram to predict mortality. Results: Within a 30-day follow-up, 27.4% of the 402 patients died. The mortality rate of patients with HF likely was significantly higher than that of the patient with gray zone and HF unlikely (40.8% vs. 25 and 16.5%, respectively, P < 0.001). HF likely [Odds ratio (OR) 1.97, 95% CI 1.13-3.42], age (OR 1.04, 95% CI 1.02-1.06), lymphocyte (OR 0.36, 95% CI 0.19-0.68), albumin (OR 0.92, 95% CI 0.87-0.96), and total bilirubin (OR 1.02, 95% CI 1-1.04) were independently associated with the prognosis of critically ill patients with COVID-19. Moreover, a nomogram was developed by bootstrap validation, and C-index was 0.8 (95% CI 0.74-0.86). Conclusions: This study established a novel nomogram to predict the 30-day all-cause mortality of critically ill patients with COVID-19, highlighting the predominant role of the "triple cut-point" strategy of NT-proBNP, which could assist in risk stratification and improve clinical sequelae.

Gao Weibo, Fan Jiasai, Sun Di, Yang Mengxi, Guo Wei, Tao Liyuan, Zheng Jingang, Zhu Jihong, Wang Tianbing, Ren Jingyi

2021

COVID-19, NT-ProBNP, heart failure, nomogram, prognosis

General General

Feature extraction with capsule network for the COVID-19 disease prediction though X-ray images.

In Materials today. Proceedings

Past couple of years, the world is going through one of the biggest pandemic named COVID-19. In the mid of year 2019, it is a very difficult process to predict the COVID-19 just by viewing the images. Later on AI based technology has done a significant role in the prediction of COVID-19 through biomedical images such as CT scan, X ray etc. This study also implemented the deep learning model for the prediction of COVID-19 through X-ray images. The implemented model is termed as XR-CAPS which consist of two models such as U-Net model and the capsule network. The U Net model is used for performing the segmentation of the images and the capsule networks are applied for performing the feature extraction. The XR-CAPS model is applied on the X-ray images for the prediction of COVID-19 and the evaluation of the model is done by three parameters that are accuracy, sensitivity and specificity. The model is compared with other existing models like ResNet50, DenseNet121 and DenseCapsNet, this has achieved an accuracy of 93.2%, sensitivity of 94% and specificity of 97.1% which is better than other states of the art algorithms.

Darji Pinesh Arvindbhai, Nayak Nihar Ranjan, Ganavdiya Sunny, Batra Neera, Guhathakurta Rajib

2021-Dec-07

Batch normalization, L2 regularization, Max pooling, Sensitivity, U net model

General General

Pandemic Strategies with Computational and Structural Biology against COVID-19: A Retrospective.

In Computational and structural biotechnology journal

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life for the better part of 2020 and into 2021. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Liu Ching-Hsuan, Lu Cheng-Hua, Lin Liang-Tzung

2021-Dec-05

COVID-19, SARS-CoV-2, artificial intelligence, disease prediction, drug design, drug screening, machine learning, vaccine development

General General

Lung Detection and Severity Prediction of Pneumonia Patients based on COVID-19 DET-PRE Network.

In Expert review of medical devices

BACKGROUND : The sudden outbreak of COVID-19 pneumonia has brought a heavy disaster to individuals globally. Facing this new virus, the clinicians have no automatic tools to assess the severity of pneumonia patients.

METHODS : In the current work, a COVID-19 DET-PRE network with two pipelines was proposed. Firstly, the lungs in X-rays were detected and segmented through the improved YOLOv3 Dense network to remove redundant features. Then, the VGG16 classifier was pre-trained on the source domain, and the severity of the disease was predicted on the target domain by means of transfer learning.

RESULTS : The experiment results demonstrated that the COVID-19 DET-PRE network can effectively detect the lungs from X-rays and accurately predict the severity of the disease. The mean average precisions (mAPs) of lung detection in patients with mild and severe illness were 0.976 and 0.983 respectively. Moreover, the accuracy of severity prediction of COVID-19 pneumonia can reach 86.1%.

CONCLUSIONS : The proposed neural network has high accuracy, which is suitable for the clinical diagnosis of COVID-19 pneumonia.

Zhang Jiaqiao, Yan Yan, Ni Hongjun, Ni Zhonghua

2021-Dec-13

COVID-19, deep learning, lung detection, neural network, severity prediction, transfer learning

General General

Language Models for the Prediction of SARS-CoV-2 Inhibitors

bioRxiv Preprint

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ~9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, A. E.; Gounley, J.; Bhowmik, D.; Chandra Shekar, M.; Lyngaas, I.; Gao, S.; Yin, J.; Tsaris, A.; Wang, F.; Glaser, J.

2021-12-14

Radiology Radiology

COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks

ArXiv Preprint

In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality issues impacting the diagnostic process and affecting the accuracy of the deep learning detection models. A significant scarcity of COVID-19 radiography images introduced an imbalance in data motivating us to use over-sampling techniques. In the study, we include an extensive set of X-ray imaging of human lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers to achieve an extensible and accurate CNN model. In the experimentation phase of the research, we evaluated a variety of convolutional network architectures, selecting a sequential convolutional network with two traditional convolutional layers and two pooling layers with maximum function. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95. We chose the Azure Machine Learning service to perform network experimentation and solution deployment. The auto-scaling compute clusters offered a significant time reduction in network training. We would like to see scientists across fields of artificial intelligence and human biology collaborating and expanding on the proposed solution to provide rapid and comprehensive diagnostics, effectively mitigating the spread of the virus

Julianna Antonchuk, Benjamin Prescott, Philip Melanchthon, Robin Singh

2021-12-14

General General

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.

In Computational and mathematical methods in medicine

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

Alam Kazi Nabiul, Khan Md Shakib, Dhruba Abdur Rab, Khan Mohammad Monirujjaman, Al-Amri Jehad F, Masud Mehedi, Rawashdeh Majdi

2021

General General

A novel Gray-Scale spatial exploitation learning Net for COVID-19 by crawling Internet resources.

In Biomedical signal processing and control

Today, the earth planet suffers from the decay of active pandemic COVID-19 which motivates scientists and researchers to detect and diagnose the infected people. Chest X-ray (CXR) image is a common utility tool for detection. Even the CXR suffers from low informative details about COVID-19 patches; the computer vision helps to overcome it through grayscale spatial exploitation analysis. In turn, it is highly recommended to acquire more CXR images to increase the capacity and ability to learn for mining the grayscale spatial exploitation. In this paper, an efficient Gray-scale Spatial Exploitation Net (GSEN) is designed by employing web pages crawling across cloud computing environments. The motivation of this work are i) utilizing a framework methodology for constructing consistent dataset by web crawling to update the dataset continuously per crawling iteration; ii) designing lightweight, fast learning, comparable accuracy, and fine-tuned parameters gray-scale spatial exploitation deep neural net; iii) comprehensive evaluation of the designed gray-scale spatial exploitation net for different collected dataset(s) based on web COVID-19 crawling verse the transfer learning of the pre-trained nets. Different experiments have been performed for benchmarking both the proposed web crawling framework methodology and the designed gray-scale spatial exploitation net. Due to the accuracy metric, the proposed net achieves 95.60% for two-class labels, and 92.67% for three-class labels, respectively compared with the most recent transfer learning Google-Net, VGG-19, Res-Net 50, and Alex-Net approaches. Furthermore, web crawling utilizes the accuracy rates improvement in a positive relationship to the cardinality of crawled CXR dataset.

ElAraby Mohamed E, Elzeki Omar M, Shams Mahmoud Y, Mahmoud Amena, Salem Hanaa

2022-Mar

COVID-19, CXR images, Classification, Cloud computing, Deep convolutional neural networks, Web crawler

Radiology Radiology

Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images.

In Applied soft computing

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.

de Moura Joaquim, Novo Jorge, Ortega Marcos

2022-Jan

Computer-aided diagnosis, Covid-19, Deep learning, Pneumonia, Pulmonary disease detection, X-ray imaging

General General

Machine learning-based forecasting of firemen ambulances' turnaround time in hospitals, considering the COVID-19 impact.

In Applied soft computing

When ambulances' turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances' TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of ± 10 min.

Cerna Selene, Arcolezi Héber H, Guyeux Christophe, Royer-Fey Guillaume, Chevallier Céline

2021-Sep

COVID-19, Emergency medical services, Firemen, Forecasting, Service breakdown, Turnaround time

Radiology Radiology

A deep and handcrafted features-based framework for diagnosis of COVID-19 from chest x-ray images.

In Concurrency and computation : practice & experience

Automatic early diagnosis of COVID-19 with computer-aided tools is crucial for disease treatment and control. Radiology images of COVID-19 and other lung diseases like bacterial pneumonia, viral pneumonia have common features. Thus, this similarity makes it difficult for radiologists to detect COVID-19 cases. A reliable method for classifying non-COVID-19 and COVID-19 chest x-ray images could be useful to reduce triage process and diagnose. In this study, we develop an original framework (HANDEFU) that supports handcrafted, deep, and fusion-based feature extraction techniques for feature engineering. The user interactively builds any model by selecting feature extraction technique and classification method through the framework. Any feature extraction technique and model could then be added dynamically to the library of software at a later time upon request. The novelty of this study is that image preprocessing and diverse feature extraction and classification techniques are assembled under an original framework. In this study, this framework is utilized for diagnosing COVID-19 from chest x-ray images on an open-access dataset. All of the experimental results and performance evaluations on this dataset are performed with this software. In experimental studies, COVID-19 prediction is performed by 27 different models through software. The superior performance with accuracy of 99.36% is obtained by LBP+SVM model.

Bozkurt Ferhat

2021-Nov-19

COVID‐19, feature engineering, feature extraction, machine learning

General General

Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance.

In Concurrency and computation : practice & experience

Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.

Almustafa Khaled Mohamad

2021-Oct-16

Covid19, classification, feature importance, feature selection, machine learning, prediction

General General

Randomly initialized convolutional neural network for the recognition of COVID-19 using X-ray images.

In International journal of imaging systems and technology

By the start of 2020, the novel coronavirus (COVID-19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID-19 rapidly and effectively is by analyzing chest X-ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND-CNN) architecture for the recognition of COVID-19. This network consists of a set of differently-sized hidden layers all created from scratch. The performance of this RND-CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID-19 datasets. Each of these datasets consists of medical images (X-rays) in one of three different classes: chests with COVID-19, with pneumonia, or in a normal state. The proposed RND-CNN model yields encouraging results for its accuracy in detecting COVID-19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID-19 dataset.

Ben Atitallah Safa, Driss Maha, Boulila Wadii, Ben Ghézala Henda

2021-Sep-19

COVID‐19, deep learning, random initialized CNN, recognition

General General

The effect of deep feature concatenation in the classification problem: An approach on COVID-19 disease detection.

In International journal of imaging systems and technology

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet-B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: "COVID-19 Image Dataset," "COVID-19 Pneumonia Normal Chest X-ray (PA) Dataset," and "COVID-19 Radiography Database" for COVID-19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1-score values, as well. Contribution of paper is as follows: COVID-19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer-aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

Cengil Emine, Çınar Ahmet

2021-Oct-10

COVID‐19, classification, convolutional neural network (CNN), features concatenation, machine learning algorithms

Radiology Radiology

COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.

In International journal of imaging systems and technology

We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7'333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98-0.99) and 0.91 ± 0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, -0.12 to 0.18) and -0.18 ± 3.4% (95% CI, -0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16-0.59) and 0.81 ± 6.6% (95% CI, -0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (-6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

Shiri Isaac, Arabi Hossein, Salimi Yazdan, Sanaat Amirhossein, Akhavanallaf Azadeh, Hajianfar Ghasem, Askari Dariush, Moradi Shakiba, Mansouri Zahra, Pakbin Masoumeh, Sandoughdaran Saleh, Abdollahi Hamid, Radmard Amir Reza, Rezaei-Kalantari Kiara, Ghelich Oghli Mostafa, Zaidi Habib

2021-Oct-28

COVID‐19, X‐ray CT, deep learning, pneumonia, segmentation

General General

DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection.

In Expert systems

Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.

Shah Pir Masoom, Ullah Hamid, Ullah Rahim, Shah Dilawar, Wang Yulin, Islam Saif Ul, Gani Abdullah, Rodrigues Joel J P C

2021-Oct-19

COVID‐19, X‐rays, convolutional neural networks, deep‐convolutional generative adversarial networks, synthetic images

General General

Structural models of SARS-CoV-2 Omicron variant in complex with ACE2 receptor or antibodies suggest altered binding interfaces

bioRxiv Preprint

There is enormous ongoing interest in characterizing the binding properties of the SARS-CoV-2 Omicron Variant of Concern (VOC) (B.1.1.529), which continues to spread towards potential dominance worldwide. To aid these studies, based on the wealth of available structural information about several SARS-CoV-2 variants in the Protein Data Bank (PDB) and a modeling pipeline we have previously developed for tracking the ongoing global evolution of SARS-CoV-2 proteins, we provide a set of computed structural models (henceforth models) of the Omicron VOC receptor-binding domain (omRBD) bound to its corresponding receptor Angiotensin-Converting Enzyme (ACE2) and a variety of therapeutic entities, including neutralizing and therapeutic antibodies targeting previously-detected viral strains. We generated bound omRBD models using both experimentally-determined structures in the PDB as well as machine learning-based structure predictions as starting points. Examination of ACE2-bound omRBD models reveals an interdigitated multi-residue interaction network formed by omRBD-specific substituted residues (R493, S496, Y501, R498) and ACE2 residues at the interface, which was not present in the original Wuhan-Hu-1 RBD-ACE2 complex. Emergence of this interaction network suggests optimization of a key region of the binding interface, and positive cooperativity among various sites of residue substitutions in omRBD mediating ACE2 binding. Examination of neutralizing antibody complexes for Barnes Class 1 and Class 2 antibodies modeled with omRBD highlights an overall loss of interfacial interactions (with gain of new interactions in rare cases) mediated by substituted residues. Many of these substitutions have previously been found to independently dampen or even ablate antibody binding, and perhaps mediate antibody-mediated neutralization escape (e.g., K417N). We observe little compensation of corresponding interaction loss at interfaces when potential escape substitutions occur in combination. A few selected antibodies (e.g., Barnes Class 3 S309), however, feature largely unaltered or modestly affected protein-protein interfaces. While we stress that only qualitative insights can be obtained directly from our models at this time, we anticipate that they can provide starting points for more detailed and quantitative computational characterization, and, if needed, redesign of monoclonal antibodies for targeting the Omicron VOC Spike protein. In the broader context, the computational pipeline we developed provides a framework for rapidly and efficiently generating retrospective and prospective models for other novel variants of SARS-CoV-2 bound to entities of virological and therapeutic interest, in the setting of a global pandemic.

Lubin, J. H.; Markosian, C.; Balamurugan, D.; Pasqualini, R.; Arap, W.; Burley, S. K.; Khare, S. D.

2021-12-13

General General

Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction

ArXiv Preprint

Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural networks to address these challenges. In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations. During the training process, both supervised and semi-supervised losses guide the updates of the entire network to: 1) prevent overfitting, 2) refine feature selection, 3) learn useful spatiotemporal representations, and 4) improve overall prediction. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment - Los Angeles. The experiment demonstrates that the proposed approach provides accurate fine-spatial-scale air quality predictions and reveals the critical environmental factors affecting the results.

Yijun Lin, Yao-Yi Chiang, Meredith Franklin, Sandrah P. Eckel, José Luis Ambite

2021-12-10

Internal Medicine Internal Medicine

Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.

In Frontiers in medicine

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76-89], diagnostic odds ratio (DOR) of 496 [198-1,245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94-98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96-0.98] and 0.92 [0.91-0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

Marateb Hamid Reza, Ziaie Nezhad Farzad, Mohebian Mohammad Reza, Sami Ramin, Haghjooy Javanmard Shaghayegh, Dehghan Niri Fatemeh, Akafzadeh-Savari Mahsa, Mansourian Marjan, Mañanas Miquel Angel, Wolkewitz Martin, Binder Harald

2021

COVID-19, computer-aided diagnosis, machine learning, screening, validation studies

General General

Classification of COVID-19 by using supervised optimized machine learning technique.

In Materials today. Proceedings

In recent two years, covid-19 diseases is the most harmful diseases in entire world. This disease increase the high mortality rate in several developed countries. Earlier identification of covid-19 symptoms can avoid the over illness or death. However, there are several researchers are introduced different methodology to identification of diseases symptoms. But, identification and classification of covid-19 diseases is the difficult task for every researchers and doctors. In this modern world, machine learning techniques is useful for several medical applications. This study is more focused in applying machine learning classifier model as SVM for classification of diseases. By improve the classification accuracy of the classifier by using hyper parameter optimization technique as modified cuckoo search algorithm. High dimensional data have unrelated, misleading features, which maximize the search space size subsequent in struggle to process data further thus not contributing to the learning practise, So we used a hybrid feature selection technique as mRMR (Minimum Redundancy Maximum Relevance) algorithm. The experiment is conducted by using UCI machine learning repository dataset. The classifier is conducted to classify the two set of classes such as COVID-19, and normal cases. The proposed model performance is analysed by using different parametric metrics, which are explained in result section.

Sharma Dilip Kumar, Subramanian Muthukumar, Malyadri Pacha, Reddy Bojja Suryanarayana, Sharma Mukta, Tahreem Madiha

2021-Nov-29

Classification, Covid-19, Feature selection, Machine learning, Modified cuckoo search algorithm and optimization

Public Health Public Health

Sentiments Evoked by WHO Public Health Posts During the COVID-19 Pandemic: A Neural Network-Based Machine Learning Analysis.

In Cureus

Introduction The World Health Organization (WHO) is a specialized agency of the United Nations responsible for international public health. Established on April 7, 1948, it has since played a pivotal role in several public health achievements and has had considerable success. But never since the establishment of the WHO has it faced a pandemic of such a huge scale. The spread of the coronavirus and the inability of the WHO to contain it has raised many questions about its efficiency and role. The present study explores the range of emotions and sentiments evoked by public health information posts of WHO over the course of the pandemic. Methods This study uses Bidirectional Encoder Representations from Transformers (BERT), which is a neural network-based technique for natural language processing. Three timeframes of five months each, starting from March 2020, were defined. A total of six posts, two posts from each timeframe, were then analysed. Comments were classified as positive, neutral and negative. The broader positive and negative classes were further subclassified into two classes each. Natural language processing was further applied to obtain results. Results The general trend of the sentiments over the period of pandemic showed a significant and dominant proportion of negative comments that overshadowed the neutral, positive and irrelevant comments over all timeframes. Specifically, the negative sentiments peaked during the second timeframe. The negativity was directed more towards the WHO, governments and people not complying with coronavirus disease 2019-appropriate norms. Positive comments were mostly expressed towards health workers. Conclusion An unusually high proportion of negative sentiment was observed in response to relatively innocuous public health posts. This may be a result of heightened anxiety, questionable credibility of the sources of information and geopolitical power play maligning the image of the WHO.

Pathak Tanmay S, Athavale Harsh, Pathak Amey S, Athavale Sunita

2021-Oct

bert, covid 19, neural networks, sentiment analysis, social media analytics, who- world health organization

Radiology Radiology

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

In Digital health

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

Cushnan Dominic, Berka Rosalind, Bertolli Ottavia, Williams Peter, Schofield Daniel, Joshi Indra, Favaro Alberto, Halling-Brown Mark, Imreh Gergely, Jefferson Emily, Sebire Neil J, Reilly Gerry, Rodrigues Jonathan C L, Robinson Graham, Copley Susan, Malik Rizwan, Bloomfield Claire, Gleeson Fergus, Crotty Moira, Denton Erika, Dickson Jeanette, Leeming Gary, Hardwick Hayley E, Baillie Kenneth, Openshaw Peter Jm, Semple Malcolm G, Rubin Caroline, Howlett Andy, Rockall Andrea G, Bhayat Ayub, Fascia Daniel, Sudlow Cathie, Jacob Joseph

Imaging, artificial intelligence, coronavirus SARS-CoV-2 disease, general, machine learning, medicine, radiology, respiratory

Public Health Public Health

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.

In Frontiers in psychiatry

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.

Badal Varsha D, Nebeker Camille, Shinkawa Kaoru, Yamada Yasunori, Rentscher Kelly E, Kim Ho-Cheol, Lee Ellen E

2021

NLP, Social support, artificial intelligence, gender, linguistic features, loneliness, social connectedness

General General

Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic.

In AI & society

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: (1) The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; (2) the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period.

de Sá Angela A R, Carvalho Jairo D, Naves Eduardo L M

2021-Nov-27

Artificial intelligence, COVID-19, Espistemology, Intelligent systems

General General

Contactless Body Temperature Monitoring of In-Patient Department (IPD) Using 2.4 GHz Microwave Frequency via the Internet of Things (IoT) Network.

In Wireless personal communications

Since the COVID-19 situation keeps going on started from 2019. Many solutions are to against the spreading of coronavirus disease. The nurses have died, and other medical workers are in critical condition from operating in the hospital. It is a deadly virus that kills many humans; Thailand's solutions have urged the public to be confident about the Government's handling of the 2019-novel Coronavirus. At the same time, everyone has to embrace the new normal lifestyle and social distancing while patiently waiting for scientists and doctors to discover vaccines and treatments to defeat COVID-19. This work proposes an innovation of wireless body temperature that instead of the used manual by medical workers in the hospital of "the contactless body temperature monitoring (CBTM) of the in-patient department (IPD)." The proposed CBTM implementation applied artificial intelligence and Internet of Things (IoT) technologies. The specified infrared body temperature on the MLX90614 DCI used for the medical field was selected to embed the IoT-CBTM for IPD using the IoT platform. The MLX90614 is an accurate sensor that matches to use for medical promotion. The detected information data from IPD will be sent to the host computer and stored in the cloud internet service at a microwave band frequency of 2.5/5.0 GHz. This paper presents the accuracy test of the IoT-CBTM prototype calibrated with the manual body temperature verifying device under Thai Industrial Institute to close with the accuracy standard requirement. The experiments were repeated many times until raise up over 70% to get more reliability accuracy. The findings indicated that the proposed prototype achieved a reliability calibration of 74.7%. The actual use of IoT-CBTM is convenient to the nurse, doctor, and medical workers to collect body temperature data into the host computer, and they can monitor this information at all times in the working room, which is far away from the COVID-19 patients. Therefore, this novel innovation was achieved because it took to try out at a local health-promoting hospital in Songkhla Province, Thailand, which the IoT-CBTM system was satisfied by the medical staff because it can safe their time, and genuinely reaching the new norm on medical distancing real-time monitoring.

Boonsong Wasana, Senajit Narongrit, Prasongchan Piya

2021-Nov-29

CBTM, COVID-19, IPD, TISI

General General

Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study.

In Computers in human behavior ; h5-index 125.0

This study is informed by two research gaps. One, Artificial Intelligence's (AI's) Machine Learning (ML) techniques have the potential to help separate information and misinformation, but this capability has yet to be empirically verified in the context of COVID-19. Two, while older adults can be particularly susceptible to the virus as well as its online infodemic, their information processing behaviour amid the pandemic has not been understood. Therefore, this study explores and understands how ML techniques (Study 1), and humans, particularly older adults (Study 2), process the online infodemic regarding COVID-19 prevention and cure. Study 1 employed ML techniques to classify information and misinformation. They achieved a classification accuracy of 86.7% with the Decision Tree classifier, and 86.67% with the Convolutional Neural Network model. Study 2 then investigated older adults' information processing behaviour during the COVID-19 infodemic period using some of the posts from Study 1. Twenty older adults were interviewed. They were found to be more willing to trust traditional media rather than new media. They were often left confused about the veracity of online content related to COVID-19 prevention and cure. Overall, the paper breaks new ground by highlighting how humans' information processing differs from how algorithms operate. It offers fresh insights into how during a pandemic, older adults-a vulnerable demographic segment-interact with online information and misinformation. On the methodological front, the paper represents an intersection of two very disparate paradigms-ML techniques and interview data analyzed using thematic analysis and concepts drawn from grounded theory to enrich the scholarly understanding of human interaction with cutting-edge technologies.

Choudrie Jyoti, Banerjee Snehasish, Kotecha Ketan, Walambe Rahee, Karende Hema, Ameta Juhi

2021-Jun

AI, COVID-19 pandemic, Information-misinformation, Interview, Machine learning techniques, Older adult

General General

Impacts of COVID-19 local spread and Google search trend on the US stock market.

In Physica A

We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. We use both conventional econometric and Machine Learning (ML) models that incorporate the local spread dynamics, COVID-19 cases and death, and Google search activities to assess if incorporating information about local spreads improves the predictive accuracy of models for the US stock market. The results suggest that COVID-19 cases and deaths, its local spread, and Google searches have impacts on abnormal stock prices between January 2020 to May 2020. Furthermore, incorporating information about local spread significantly improves the performance of forecasting models of the abnormal stock prices at longer forecasting horizons.

Dey Asim K, Hoque G M Toufiqul, Das Kumer P, Panovska Irina

2021-Sep-30

Abnormal price, Causality, Covid-19, Stock market, Temporal network, Volatility

Public Health Public Health

Digital Interventions to Reduce Distress Among Health Care Providers at the Frontline: Protocol for a Feasibility Trial.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Stress, anxiety, distress, and depression are high among healthcare workers during the COVID-19 pandemic, and they have reported acting in ways that are contrary to their moral values and professional commitments that degrade their integrity. This creates moral distress and injury due to constraints they have encountered, such as limited resources.

OBJECTIVE : The purpose of this study is to develop and show the feasibility of digital platforms (a virtual reality (VR) and a mobile platform) to understand the causes and ultimately reduce the moral distress of healthcare providers during the COVID-19 pandemic. This project is a proof-of-concept integration of concepts/applications to demonstrate viability over six months and guide future studies to develop these state-of-the-art technologies to help frontline healthcare workers work in complex moral contexts. In addition, the project will develop innovations that can be used for future pandemics and in other contexts prone to producing moral distress and injury.

METHODS : This will be a prospective, single cohort, pre- and post-test study examining the effect of a brief informative video describing moral distress on perceptual, psychological, and physiological indicators of stress and decision-making during the scenario known to potentially elicit moral distress. To accomplish this, we have developed a VR simulation scenario that will be used before and after the digital intervention for monitoring short-term impacts. The simulation involves an ICU setting during the COVID-19 pandemic, and participants will be placed in a morally challenging situation. The participants will be engaged in an educational intervention at the individual, team, and organizational levels. During each test, data will be collected for a) physiological measures of stress and after each test, data will be collected regarding b) thoughts, feelings and behaviours during a morally challenging situation, and c) perceptual estimates of psychological stress. We aim to create an effective compound intervention composed of the VR-based simulation educational intervention that is examined through the data collected from mental health questionnaires. In addition, participants will continue to be monitored for moral distress and other psychological stresses for eight weeks through our Digital intervention/intelligence Group mobile (DiiG) platform for a longer-term impact. Finally, a comparison will be conducted using machine learning and biostatistical techniques to analyze the short- and long-term impacts of the VR intervention.

RESULTS : Funded in (November, 2020), approved by REB in (March, 2021), the study is ongoing.

CONCLUSIONS : This project aims to demonstrate the feasibility of using digital platforms to understand the continuum of moral distress that can lead to moral injury. Demonstration of feasibility will lead to future studies to examine the efficacy of digital platforms to reduce moral distress.

CLINICALTRIAL : Trial registry name: ClinicalTrials.gov, registration/identifier number: NCT05001542, URL: https://clinicaltrials.gov/ct2/show/NCT05001542, Unity Health Toronto protocol record: 21-066.

Nguyen Binh, Torres Andrei, Sim Walter, Kenny Deborah, Campbell Douglas M, Beavers Lindsay, Lou Wendy, Kapralos Bill, Peter Elizabeth, Dubrowski Adam, Krishnan Sridhar, Bhat Venkat

2021-Nov-22

Public Health Public Health

Fire association with respiratory disease and COVID-19 complications in the State of Pará, Brazil.

In Lancet Regional Health. Americas

Background : Brazil has faced two simultaneous problems related to respiratory health: forest fires and the high mortality rate due to COVID-19 pandemics. The Amazon rain forest is one of the Brazilian biomes that suffers the most with fires caused by droughts and illegal deforestation. These fires can bring respiratory diseases associated with air pollution, and the State of Pará in Brazil is the most affected. COVID-19 pandemics associated with air pollution can potentially increase hospitalizations and deaths related to respiratory diseases. Here, we aimed to evaluate the association of fire occurrences with the COVID-19 mortality rates and general respiratory diseases hospitalizations in the State of Pará, Brazil.

Methods : We employed machine learning technique for clustering k-means accompanied with the elbow method used to identify the ideal quantity of clusters for the k-means algorithm, clustering 10 groups of cities in the State of Pará where we selected the clusters with the highest and lowest fires occurrence from the 2015 to 2019. Next, an Auto-regressive Integrated Moving Average Exogenous (ARIMAX) model was proposed to study the serial correlation of respiratory diseases hospitalizations and their associations with fire occurrences. Regarding the COVID-19 analysis, we computed the mortality risk and its confidence level considering the quarterly incidence rate ratio in clusters with high and low exposure to fires.

Findings : Using the k-means algorithm we identified two clusters with similar DHI (Development Human Index) and GDP (Gross Domestic Product) from a group of ten clusters that divided the State of Pará but with diverse behavior considering the hospitalizations and forest fires in the Amazon biome. From the auto-regressive and moving average model (ARIMAX), it was possible to show that besides the serial correlation, the fires occurrences contribute to the respiratory diseases increase, with an observed lag of six months after the fires for the case with high exposure to fires. A highlight that deserves attention concerns the relationship between fire occurrences and deaths. Historically, the risk of mortality by respiratory diseases is higher (about the double) in regions and periods with high exposure to fires than the ones with low exposure to fires. The same pattern remains in the period of the COVID-19 pandemic, where the risk of mortality for COVID-19 was 80% higher in the region and period with high exposure to fires. Regarding the SARS-COV-2 analysis, the risk of mortality related to COVID-19 is higher in the period with high exposure to fires than in the period with low exposure to fires. Another highlight concerns the relationship between fire occurrences and COVID-19 deaths. The results show that regions with high fire occurrences are associated with more cases of COVID deaths.

Interpretation : The decision-make process is a critical problem mainly when it involves environmental and health control policies. Environmental policies are often more cost-effective as health measures than the use of public health services. This highlight the importance of data analyses to support the decision making and to identify population in need of better infrastructure due to historical environmental factors and the knowledge of associated health risk. The results suggest that The fires occurrences contribute to the increase of the respiratory diseases hospitalization. The mortality rate related to COVID-19 was higher for the period with high exposure to fires than the period with low exposure to fires. The regions with high fire occurrences is associated with more COVID-19 deaths, mainly in the months with high number of fires.

Funding : No additional funding source was required for this study.

Schroeder Lucas, de Souza Eniuce Menezes, Rosset Clévia, Marques Junior Ademir, Boquett Juliano André, Francisco Rofatto Vinicius, Brum Diego, Gonzaga Luiz, Zagonel de Oliveira Marcelo, Veronez Mauricio Roberto

2022-Feb

ARIMAX, COVID-19, Fire, Hospitalizations, Incidence rate ratio, K-Means, Mortality, Respiratory diseases, Risk, SARS-Cov-2, Time series analysis

General General

I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece.

In Public health in practice (Oxford, England)

Objectives : In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health.

Study design : An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries.

Methods : Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion.

Results : Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown.

Conclusions : From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation.

Carollo Alessandro, Bizzego Andrea, Gabrieli Giulio, Wong Keri Ka-Yee, Raine Adrian, Esposito Gianluca

2021-Nov

COVID-19, Global study, Lockdown, Loneliness, Machine learning, Mental health

General General

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.

In Current research in pharmacology and drug discovery

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Prasad Kartikay, Kumar Vijay

2021

Artificial intelligence, COVID-19, Deep learning, Drug repurposing, SARS-CoV-2, Structure

Pathology Pathology

A Deep Learning Approach for Predicting Severity of COVID-19 Patients Using A Parsimonious Set of Laboratory Markers.

In iScience

The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy to deploy system to predict the severe manifestation of disease in COVID-19 patients with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0·78 95% CI: 0·77-0·82, and the negative predictive value NPV of 0·86 95% CI: 0·84-0·88 for the need to use a ventilator and has an accuracy with AUC of 0·85 95% CI: 0·84-0·86, and the NPV of 0·94 95% CI: 0·92-0·96 for predicting in-hospital 30-day mortality.

Singh Vivek, Kamaleswaran Rishi, Chalfin Donald, Buño-Soto Antonio, San Roman Janika, Rojas-Kenney Edith, Molinaro Ross, von Sengbusch Sabine, Hodjat Parsa, Comaniciu Dorin, Kamen Ali

2021-Nov-27

General General

Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm.

In Infectious Disease Modelling

This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.

Krivorotko Olga, Sosnovskaia Mariia, Vashchenko Ivan, Kerr Cliff, Lesnic Daniel

2022-Mar

Agent-based modeling, COVID-19, Coronavirus data analysis, Epidemiology, Forecasting scenarios, Interventions analysis, Optimization, Parameter identification, Reproduction number

General General

HLAncPred: A method for predicting promiscuous non-classical HLA binding sites

bioRxiv Preprint

In the last two decades, ample of methods have been developed to predict the classical HLA binders in an antigen. In contrast, limited attempts have been made to develop methods for predicting binders for non-classical HLA; due to the scarcity of sufficient experimental data and lack of community interest. Of Note, non-classical HLA plays a crucial immunomodulatory role and regulates various immune responses. Recent studies revealed that non-classical HLA (HLA-E & HLA-G) based immunotherapies have many advantages over classical HLA based-immunotherapy, particularly against COVID-19. In order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of non-classical HLA alleles (HLA-G and HLA-E). All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning based-models achieved more than 0.98 AUC for HLA-G alleles on validation or independent dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.88 on the validation dataset for HLA-E*01:01, HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA binders and compared with the models developed in this study. Moreover, we have also predicted the non-classical HLA binders in the spike protein of different variants of virus causing COVID-19 including omicron (B.1.1.529) to facilitate the community. One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. In order to predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred), and a standalone package.

Dhall, A.; Patiyal, S.; Raghava, G. P. S.

2021-12-06

Radiology Radiology

Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation

ArXiv Preprint

Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testing kits like Brazil and India, radiological imaging can act as an important diagnostic tool to accurately classify covid-19 patients and prescribe the necessary treatment in due time. With this motivation, we present our study based on deep learning architecture for detecting covid-19 infected lungs using chest X-rays. Dataset: We collected a total of 2470 images for three different class labels, namely, healthy lungs, ordinary pneumonia, and covid-19 infected pneumonia, out of which 470 X-ray images belong to the covid-19 category. Methods: We first pre-process all the images using histogram equalization techniques and segment them using U-net architecture. VGG-16 network is then used for feature extraction from the pre-processed images which is further sampled by SMOTE oversampling technique to achieve a balanced dataset. Finally, the class-balanced features are classified using a support vector machine (SVM) classifier with 10-fold cross-validation and the accuracy is evaluated. Result and Conclusion: Our novel approach combining well-known pre-processing techniques, feature extraction methods, and dataset balancing method, lead us to an outstanding rate of recognition of 98% for COVID-19 images over a dataset of 2470 X-ray images. Our model is therefore fit to be utilized in healthcare facilities for screening purposes.

Hitendra Singh Bhadouria, Krishan Kumar, Aman Swaraj, Karan Verma, Arshpreet Kaur, Shasvat Sharma, Ghanshyam Singh, Ashok Kumar, Leandro Melo de Sales

2021-12-05

General General

An investigation into the deep learning approach in sentimental analysis using graph-based theories.

In PloS one ; h5-index 176.0

Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a "black-box" and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features' extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on the input unit and backward path respectively. We experiment on the Twitter Health News dataset were the model has been extended to approach different approximations (tweet/aspect and tweets' source levels, frequency, polarity/subjectivity), it was also transparent and traceable. Moreover, results of comparing with four recent models on same data corpus for tweets analysis showed a rapid convergence with an overall accuracy of ≈83% and 94% of correctly identified true positive sentiments. Therefore, weights can be ideally assigned to specific active features by following the proposed method. As opposite to other compared works, the inferred features are conditioned through the users' preferences (i.e., frequency degree) and via the activation's derivatives (i.e., reject feature if not scored). Future direction will address the inductive aspect of graph embeddings to include dynamic graph structures and expand the model resiliency by considering other datasets like SemEval task7, covid-19 tweets, etc.

Kentour Mohamed, Lu Joan

2021

General General

Detection of COVID-19 with CT Images using Hybrid Complex Shearlet Scattering Networks.

In IEEE journal of biomedical and health informatics

With the ongoing worldwide coronavirus disease 2019 (COVID-19) pandemic, it is desirable to develop effective algorithms for the automatic detection of COVID-19 with chest computed tomography (CT) images. As deep learning has achieved breakthrough results in numerous computer vision and image understanding tasks, a good choice is to consider diagnosis models based on deep learning. Recently, a considerable number of methods have indeed been proposed. However, training an accurate deep learning model requires a large-scale chest CT dataset, which is hard to collect due to the high contagiousness of COVID-19. To achieve improved COVID-19 detection performance, this paper proposes a hybrid framework that fuses the complex shearlet scattering transform (CSST) and a suitable convolutional neural network into a single model. The introduced CSST cascades complex shearlet transforms with modulus nonlinearities and low-pass filter convolutions to compute a sparse and locally invariant image representation. The features computed from the input chest CT images are discriminative for the detection of COVID-19. Furthermore, a wide residual network with a redesigned residual block (WR2N) is developed to learn more granular multiscale representations by applying it to scattering features. The combination of the model-based CSST and data-driven WR2N leads to a more convenient neural network for image representation, where the idea is to learn only the image parts that the CSST cannot handle instead of all parts. The experimental results obtained on two public chest CT datasets for COVID-19 detection demonstrate the superiority of the proposed method. We can obtain more accurate results than several state-of-the-art COVID-19 classification methods in terms of measures such as accuracy, the F1-score, and the area under the receiver operating characteristic curve.

Ren Qingyun, Zhou Bingyin, Tian Liang, Guo Wei

2021-Dec-02

General General

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.

In Journal of healthcare engineering

Methods : Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance.

Results : SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained.

Conclusion : The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

Tchagna Kouanou Aurelle, Mih Attia Thomas, Feudjio Cyrille, Djeumo Anges Fleurio, Ngo Mouelas Adèle, Nzogang Mendel Patrice, Tchito Tchapga Christian, Tchiotsop Daniel

2021

General General

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.

In Scientific reports ; h5-index 158.0

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

Gidde Prashant Sadashiv, Prasad Shyam Sunder, Singh Ajay Pratap, Bhatheja Nitin, Prakash Satyartha, Singh Prateek, Saboo Aakash, Takhar Rohit, Gupta Salil, Saurav Sumeet, M V Raghunandanan, Singh Amritpal, Sardana Viren, Mahajan Harsh, Kalyanpur Arjun, Mandal Atanendu Shekhar, Mahajan Vidur, Agrawal Anurag, Agrawal Anjali, Venugopal Vasantha Kumar, Singh Sanjay, Dash Debasis

2021-Dec-01

General General

Use of Medical Information and Digital Services for Self-Empowerment before, during, and after a Major Disaster.

In The Tohoku journal of experimental medicine

Disaster response procedures have been developed and improved following the Great East Japan Earthquake. Innovative services have also been created through digital transformation, including an acceleration and deepening of artificial intelligence technology. Things that were once technically impossible are now possible. These innovative technologies will spread across various fields, and disaster response will not be an exception. The Ministry of Health, Labour and Welfare is promoting the use of personal health records in a way that effectively supports the management of treatments by using data from wearable devices and specific applications. During the COVID-19 pandemic, the trade-off between protecting personal information and enabling social benefits, such as in the use of digital tracking, and infodemics, including misinformation, have become new social challenges. Reviewing past disaster preparedness and the services and value provided by digital transformation indicates what new disaster preparedness should be. Digital transformation does not require literacy (ability to collect, analyze, and use information) but competence (beneficial behavioral traits derived from experience). Understanding behavior through data and enabling rational behavior are crucial. By increasing human productivity, we can save time and improve self- and mutual-help in times of disaster. Medical information and digital services must be properly used in normal times. A society that uses such services will be more disaster resilient.

Fujii Susumu, Nonaka Sayuri, Nakayama Masaharu

2021-11

Great East Japan Earthquake, digital transformation, disaster medicine, medical informatics, personal health record

General General

Delta variant with P681R critical mutation revealed by ultra-large atomic-scale ab initio simulation: Implications for the fundamentals of biomolecular interactions

bioRxiv Preprint

SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing the virus to invade human cells much faster and with increased efficiency. Particularly, an especially dangerous mutation P681R close to the furin cleavage site has been identified as responsible for increasing the infection rate. Together with the earlier reported mutation D614G in the same domain, it offers an excellent instance to investigate the nature of mutations and how they affect the interatomic interactions in the spike protein. Here, using ultra large-scale ab initio computational modeling, we study the P681R and D614G mutations in the SD2-FP domain including the effect of double mutation and compare the results with the wild type. We have recently developed a method of calculating the amino acid-amino acid bond pairs (AABP) to quantitatively characterize the details of the interatomic interactions, enabling us to explain the nature of mutation at the atomic resolution. Our most significant find is that the mutations reduce the AABP value, implying a reduced bonding cohesion between interacting residues and increasing the flexibility of these amino acids to cause the damage. The possibility of using this unique mutation quantifiers in a machine learning protocol could lead to the prediction of emerging mutations.

Adhikari, P.; Jawad, B.; Rao, P.; Podgornik, R.; Ching, W.-Y.

2021-12-03

General General

COVID-19. Implications for Paediatric Anaesthesia, lessons learnt and how to prepare for the next pandemic.

In Paediatric anaesthesia

COVID-19 is mainly considered an "adult pandemic", but it also has strong implications for children and consequently for paediatric anaesthesia. Despite the lethality of SARS-CoV-2 infection being directly correlated with age, children have equally experienced the negative impacts of this pandemic. In fact, the spectrum of COVID-19 symptoms among children ranges from very mild to those resembling adults, but may also present as a multisystemic inflammatory syndrome. Moreover, the vast majority of children might be affected by asymptomatic or pauci-symptomatic infection making them the "perfect" carriers for spreading the disease in the community. Beyond the clinical manifestations of SARS-CoV-2 infection, the COVID-19 pandemic may ultimately have catastrophic health and socioeconomic consequences for children and adolescents which are yet to be defined. The aim of this narrative review is to highlight how COVID-19 pandemic has affected and changed the paediatric anaesthesia practice and which lessons are to be learned in case of a future "pandemic". In particular, the rapid evolution and dissemination of research and clinical findings have forced the scientific community to adapt and alter clinical practice on an unseen and pragmatic manner. Equally, implementation of new platforms, techniques and devices together with artificial intelligence and large-scale collaborative efforts may present a giant step for mankind. The valuable lessons of this pandemic will ultimately translate into new treatments modalities for various diseases but will also have the potential for safety improvement and better quality of care. However, this pandemic has revealed the vulnerability and deficiencies of our healthcare system. If not addressed properly, we may end up with a tsunami of burn-out and compassionate fatigue among health care professionals. Paediatric anaesthesia and critical care staff are no exceptions.

Afshari Arash, Disma Nicola, von Ungern-Sternberg Britta S, Matava Clyde

2021-Nov-30

children, coronavirus, covid-19, paediatric anaesthesia, simulation

General General

An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.

In GigaScience

BACKGROUND : The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19-affected UK population in terms of geographic, demographic, and temporal coverage.

FINDINGS : The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage.

CONCLUSION : The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Cushnan Dominic, Bennett Oscar, Berka Rosalind, Bertolli Ottavia, Chopra Ashwin, Dorgham Samie, Favaro Alberto, Ganepola Tara, Halling-Brown Mark, Imreh Gergely, Jacob Joseph, Jefferson Emily, Lemarchand François, Schofield Daniel, Wyatt Jeremy C

2021-Nov-25

COVID-19, SARS-CoV2, machine learning, medical imaging, thoracic imaging

Public Health Public Health

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens.

In Network modeling and analysis in health informatics and bioinformatics

COVID-19 is a pandemic disease caused by novel corona virus, SARS-CoV-2, initially originated from China. In response to this serious life-threatening disease, designing and developing more accurate and sensitive tests are crucial. The aim of this study is designing a multi-epitope of spike and nucleocapsid antigens of COVID-19 virus by bioinformatics methods. The sequences of nucleotides obtained from the NCBI Nucleotide Database. Transmembrane structures of proteins were predicted by TMHMM Server and the prediction of signal peptide of proteins was performed by Signal P Server. B-cell epitopes' prediction was performed by the online prediction server of IEDB server. Beta turn structure of linear epitopes was also performed using the IEDB server. Conformational epitope prediction was performed using the CBTOPE and eventually, eight antigenic epitopes with high physicochemical properties were selected, and then, all eight epitopes were blasted using the NCBI website. The analyses revealed that α-helices, extended strands, β-turns, and random coils were 28.59%, 23.25%, 3.38%, and 44.78% for S protein, 21.24%, 16.71%, 6.92%, and 55.13% for N Protein, respectively. The S and N protein three-dimensional structure was predicted using the prediction I-TASSER server. In the current study, bioinformatics tools were used to design a multi-epitope peptide based on the type of antigen and its physiochemical properties and SVM method (Machine Learning) to design multi-epitopes that have a high avidity against SARS-CoV-2 antibodies to detect infections by COVID-19.

Javadi Mamaghani Amirreza, Arab-Mazar Zahra, Heidarzadeh Siamak, Ranjbar Mohammad Mehdi, Molazadeh Shima, Rashidi Sama, Niazpour Farzad, Naghi Vishteh Mohadeseh, Bashiri Homayoon, Bozorgomid Arezoo, Behniafar Hamed, Ashrafi Mohammad

2021

Multi-epitopes, Nucleocapsid phosphoprotein, SARS-CoV2, Serological tests, Spike glycoprotein

General General

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

In Journal of ambient intelligence and humanized computing

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

Priya Bhanu, Malhotra Jyoteesh

2021-Nov-26

Artificial Intelligence, Double deep reinforcement learning, Edge computing, QoE, Resource utilisation factor, Software-defined wireless networking

General General

The Feasibility of Conducting Safe Objective Structured Clinical Exams (OSCEs) During the COVID-19 Era.

In Advances in medical education and practice

Purpose : Objective structured clinical examination (OSCE) is an effective tool for learners' assessment that require hands-on performance. During the COVID-era, many schools decided to minimize all forms of in-person communication between faculty members and students to mitigate the risk of COVID-19 transmission. We aimed to describe our experience in conducting physical OSCEs during the COVID-19 era. We also reported students' satisfaction during this time.

Materials and Methods : Descriptive cohort study by comparing the 2019-2020 cohort to the 2020-2021 cohort. Descriptive framework for the feasibility of conducting physical OSCEs in the college of pharmacy at King Saudi Bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia.

Results : There were no reported cases of COVID-19 transmission among students and faculty members during the OSCE assessments. Overall, the 2020-2021 cohort reported increased satisfaction compared to their peers in the 2019-2020 cohort; p < 0.05. We observed an increased need for coordination to ensure students' and staff safety while adopting machine learning applications as a public measure when possible.

Conclusion : Owing to the implementation of clear and strong measures, it was feasible to conduct OSCEs, and there were no reported cases of COVID-19 transmission. Other universities may adopt a similar approach so as to provide an optimal educational experience while ensuring the safety of their staff and faculty.

Alshaya Abdulrahman, Alowais Shuroug, Alharbi Shmeylan, Albekairy Abdulkareem

2021

COVID-19, OSCE, education, simulation, teaching

General General

Deep learning-based exchange rate prediction during the COVID-19 pandemic.

In Annals of operations research

This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.

Abedin Mohammad Zoynul, Moon Mahmudul Hasan, Hassan M Kabir, Hajek Petr

2021-Nov-26

Bagging ridge, Bi-LSTM, COVID-19, Deep learning, Exchange rate forecasting, Machine learning

oncology Oncology

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

In Scientific reports ; h5-index 158.0

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Hsieh Kanglin, Wang Yinyin, Chen Luyao, Zhao Zhongming, Savitz Sean, Jiang Xiaoqian, Tang Jing, Kim Yejin

2021-Nov-30

General General

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.

In Scientific reports ; h5-index 158.0

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.

Jia Lijing, Wei Zijian, Zhang Heng, Wang Jiaming, Jia Ruiqi, Zhou Manhong, Li Xueyan, Zhang Hankun, Chen Xuedong, Yu Zheyuan, Wang Zhaohong, Li Xiucheng, Li Tingting, Liu Xiangge, Liu Pei, Chen Wei, Li Jing, He Kunlun

2021-Nov-30

General General

A machine learning model for nowcasting epidemic incidence.

In Mathematical biosciences

Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID - 19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting.

Sahai Saumya Yashmohini, Gurukar Saket, KhudaBukhsh Wasiur R, Parthasarathy Srinivasan, Rempała Grzegorz A

2021-Nov-27

Backfilling, COVID-19 incidence, Nowcasting, Random forest

General General

T cell receptor repertoire signatures associated with COVID-19 severity

bioRxiv Preprint

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoires composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of viral infection such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we performed a large-scale analysis of over 4.7 billion sequences across 2,130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identified and characterized convergent COVID-19 associated CDR3 gene usages, specificity groups, and sequence patterns. T cell clonal expansion was found to be associated with upregulation of T cell effector function, TCR signaling, NF-kB signaling, and Interferon-gamma signaling pathways. Machine learning approaches accurately predicted disease severity for patients based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores across various predictor permutations. These analyses provided an integrative, systems immunology view of T cell adaptive immune responses to COVID-19.

Park, J.; Lee, K.; Lam, S.; Chen, S.

2021-12-02

Public Health Public Health

Horizon Scanning: Rise of Planetary Health Genomics and Digital Twins for Pandemic Preparedness.

In Omics : a journal of integrative biology

The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.

Geanta Marius, Tanwar Ankit Singh, Lehrach Hans, Satyamoorthy Kapaettu, Brand Angela

2021-Dec-01

SARS-CoV-2 sequencing, digital transformation, digital twins, genomic surveillance, pandemic preparedness, public health genomics

General General

Computational anti-COVID-19 drug design: progress and challenges.

In Briefings in bioinformatics

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Wang Jinxian, Zhang Ying, Nie Wenjuan, Luo Yi, Deng Lei

2021-Nov-30

COVID-19, SARS-CoV-2, artificial intelligence, computational drug design, structure-based

Radiology Radiology

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020.

In European journal of radiology ; h5-index 47.0

PURPOSE : To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020.

METHODS : Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors' affiliation was manually extracted from each article. The Bibliographic Item Co-Occurrence Matrix Builder and VOSviewer were used to identify the research topics of the included articles and illustrate the future trajectories of studies.

RESULTS : The study included 2685 original articles. The number of publications on DL and medical imaging has increased substantially since 2017, accounting for 97.2% of all included articles. We evaluated the rules of the application of 47 DL networks to eight radiological tasks on 11 human organ sites. Neuroradiology, thorax, and abdomen were frequent research subjects, while thyroid was under-represented. Segmentation and classification tasks were the primary purposes. U-Net, ResNet, and VGG were the most frequently used Convolutional neural network-derived networks. GAN-derived networks were widely developed and applied in 2020, and transfer learning was highlighted in the COVID-19 studies. Brain, prostate, and diabetic retinopathy-related studies were mature research topics in the field. Breast- and lung-related studies were in a stage of rapid development.

CONCLUSIONS : This study evaluates the general rules and future trajectories of DL network application in medical image analyses and provides guidance for future studies.

Wang Lu, Wang Hairui, Huang Yingna, Yan Baihui, Chang Zhihui, Liu Zhaoyu, Zhao Mingfang, Cui Lei, Song Jiangdian, Li Fan

2021-Nov-24

Bibliometrics, Deep learning, Diagnostic imaging, Radiology

General General

Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification.

In Computers in biology and medicine

Deep learning (DL) has shown great success in the field of medical image analysis. In the wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on DL have made significant progress in automated screening of COVID-19 disease from the chest X-ray (CXR) images. But these DL models have no inherent way of expressing uncertainty associated with the model's prediction, which is very important in medical image analysis. Therefore, in this paper, we develop an uncertainty-aware convolutional neural network model, named UA-ConvNet, for the automated detection of COVID-19 disease from CXR images, with an estimation of associated uncertainty in the model's predictions. The proposed approach utilizes the EfficientNet-B3 model and Monte Carlo (MC) dropout, where an EfficientNet-B3 model has been fine-tuned on the CXR images. During inference, MC dropout has been applied for M forward passes to obtain the posterior predictive distribution. After that mean and entropy have been calculated on the obtained predictive distribution to get the mean prediction and model uncertainty. The proposed method is evaluated on the three different datasets of chest X-ray images, namely the COVID19CXr, X-ray image, and Kaggle datasets. The proposed UA-ConvNet model achieves a G-mean of 98.02% (with a Confidence Interval (CI) of 97.99-98.07) and sensitivity of 98.15% for the multi-class classification task on the COVID19CXr dataset. For binary classification, the proposed model achieves a G-mean of 99.16% (with a CI of 98.81-99.19) and a sensitivity of 99.30% on the X-ray Image dataset. Our proposed approach shows its superiority over the existing methods for diagnosing the COVID-19 cases from the CXR images.

Gour Mahesh, Jain Sweta

2021-Nov-23

COVID-19 automatic screening uncertainty estimation Monte Carlo dropout pre-trained EfficientNet CNN Chest X-ray images

Public Health Public Health

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world.

In bioRxiv : the preprint server for biology

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.

Li Yawei, Liu Qingyun, Zeng Zexian, Luo Yuan

2021-Nov-24

General General

Severe COVID-19 infection is associated with aberrant cytokine production by infected lung epithelial cells rather than by systemic immune dysfunction.

In Research square

The mechanisms explaining progression to severe COVID-19 remain poorly understood. It has been proposed that immune system dysregulation/over-stimulation may be implicated, but it is not clear how such processes would lead to respiratory failure. We performed comprehensive multiparameter immune monitoring in a tightly controlled cohort of 128 COVID-19 patients, and used the ratio of oxygen saturation to fraction of inspired oxygen (SpO2 / FiO2) as a physiologic measure of disease severity. Machine learning algorithms integrating 139 parameters identified IL-6 and CCL2 as two factors predictive of severe disease, consistent with the therapeutic benefit observed with anti-IL6-R antibody treatment. However, transcripts encoding these cytokines were not detected among circulating immune cells. Rather, in situ analysis of lung specimens using RNAscope and immunofluorescent staining revealed that elevated IL-6 and CCL2 were dominantly produced by infected lung type II pneumocytes. Severe disease was not associated with higher viral load, deficient antibody responses, or dysfunctional T cell responses. These results refine our understanding of severe COVID-19 pathophysiology, indicating that aberrant cytokine production by infected lung epithelial cells is a major driver of immunopathology. We propose that these factors cause local immune regulation towards the benefit of the virus.

Gajewski Thomas, Rouhani Sherin, Trujillo Jonathan, Pyzer Athalia, Yu Jovian, Fessler Jessica, Cabanov Alexandra, Higgs Emily, Cron Kyle, Zha Yuanyuan, Lu Yihao, Bloodworth Jeffrey, Abasiyanik Mustafa, Okrah Susan, Flood Blake, Hatogai Ken, Leung Michael, Pezeshk Apameh, Kozloff Lara, Reschke Robin, Strohbehn Garth, Chervin Carolina Soto, Kumar Madan, Schrantz Stephen, Madariaga Maria Lucia, Beavis Kathleen, Yeo Kiang-Teck, Sweis Randy, Segal Jeremy, Tay SavaÅŸ, Izumchenko Evgeny, Mueller Jeffrey, Chen Lin

2021-Nov-24

General General

Machine Learning-Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study.

In JMIRx med

Background : The onset and development of the COVID-19 pandemic have placed pressure on hospital resources and staff worldwide. The integration of more streamlined predictive modeling in prognosis and triage-related decision-making can partly ease this pressure.

Objective : The objective of this study is to assess the performance impact of dimensionality reduction on COVID-19 mortality prediction models, demonstrating the high impact of a limited number of features to limit the need for complex variable gathering before reaching meaningful risk labelling in clinical settings.

Methods : Standard machine learning classifiers were employed to predict an outcome of either death or recovery using 25 patient-level variables, spanning symptoms, comorbidities, and demographic information, from a geographically diverse sample representing 17 countries. The effects of feature reduction on the data were tested by running classifiers on a high-quality data set of 212 patients with populated entries for all 25 available features. The full data set was compared to two reduced variations with 7 features and 1 feature, respectively, extracted using univariate mutual information and chi-square testing. Classifier performance on each data set was then assessed on the basis of accuracy, sensitivity, specificity, and received operating characteristic-derived area under the curve metrics to quantify benefit or loss from reduction.

Results : The performance of the classifiers on the 212-patient sample resulted in strong mortality detection, with the highest performing model achieving specificity of 90.7% (95% CI 89.1%-92.3%) and sensitivity of 92.0% (95% CI 91.0%-92.9%). Dimensionality reduction provided strong benefits for performance. The baseline accuracy of a random forest classifier increased from 89.2% (95% CI 88.0%-90.4%) to 92.5% (95% CI 91.9%-93.0%) when training on 7 chi-square-extracted features and to 90.8% (95% CI 89.8%-91.7%) when training on 7 mutual information-extracted features. Reduction impact on a separate logistic classifier was mixed; however, when present, losses were marginal compared to the extent of feature reduction, altogether showing that reduction either improves performance or can reduce the variable-sourcing burden at hospital admission with little performance loss. Extreme feature reduction to a single most salient feature, often age, demonstrated large standalone explanatory power, with the best-performing model achieving an accuracy of 81.6% (95% CI 81.1%-82.1%); this demonstrates the relatively marginal improvement that additional variables bring to the tested models.

Conclusions : Predictive statistical models have promising performance in early prediction of death among patients with COVID-19. Strong dimensionality reduction was shown to further improve baseline performance on selected classifiers and only marginally reduce it in others, highlighting the importance of feature reduction in future model construction and the feasibility of deprioritizing large, hard-to-source, and nonessential feature sets in real world settings.

Doyle Riccardo

COVID-19, artificial intelligence, automation, comorbidities, coronavirus, dimensionality reduction, epidemiology, hospital, machine learning, medical informatics, model development, mortality, pre-existing conditions, prediction, prognosis, public data, resource management, triage

General General

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia.

In Tomography (Ann Arbor, Mich.)

We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration. Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality. In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12-7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02-4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32-2.33, p < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 95% CI = 1.02-1.15, p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined. AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification.

Szabó István Viktor, Simon Judit, Nardocci Chiara, Kardos Anna Sára, Nagy Norbert, Abdelrahman Renad-Heyam, Zsarnóczay Emese, Fejér Bence, Futácsi Balázs, Müller Veronika, Merkely Béla, Maurovich-Horvat Pál

2021-Nov-01

COVID-19, artificial intelligence, computed tomography

General General

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models.

In SN computer science

As the number of COVID-19 cases increases day by day, the situation and livelihood of people throughout the world deteriorates. The goal of this study is to use machine learning models to identify disease and forecast whether or not a person is infected with the virus or another common illness. More articles about COVID-19 will be released starting in 2020, but we still do not have a reliable prediction mechanism to diagnose the disease with 100% accuracy. This comparison is done to see which model is the most effective in detecting and predicting disease. Despite the fact that we have immunizations, we require a best-prediction strategy to assist all humans in surviving. Researchers claimed that the supervised learning method predicts more accurately than the unsupervised learning method in the majority of studies. Supervised learning is the process of mapping inputs to derived outputs using a set of variables and created functions. This will also help us to optimize performance criteria using experience. It is further divided into two categories: classification and regression. According to recent studies, classification models are more accurate than other models.

Abirami R Sudha, Kumar G Suresh

2022

Classification, Coronavirus disease (COVID-19), Machine learning, Regression, Supervised, Unsupervised

General General

A method for the identification of COVID-19 biomarkers in human breath using Proton Transfer Reaction Time-of-Flight Mass Spectrometry.

In EClinicalMedicine

Background : COVID-19 has caused a worldwide pandemic, making the early detection of the virus crucial. We present an approach for the determination of COVID-19 infection based on breath analysis.

Methods : A high sensitivity mass spectrometer was combined with artificial intelligence and used to develop a method for the identification of COVID-19 in human breath within seconds. A set of 1137 positive and negative subjects from different age groups, collected in two periods from two hospitals in the USA, from 26 August, 2020 until 15 September, 2020 and from 11 September, 2020 until 11 November, 2020, was used for the method development. The subjects exhaled in a Tedlar bag, and the exhaled breath samples were subsequently analyzed using a Proton Transfer Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS). The produced mass spectra were introduced to a series of machine learning models. 70% of the data was used for these sub-models' training and 30% was used for testing.

Findings : A set of 340 samples, 95 positives and 245 negatives, was used for the testing. The combined models successfully predicted 77 out of the 95 samples as positives and 199 out of the 245 samples as negatives. The overall accuracy of the model was 81.2%. Since over 50% of the total positive samples belonged to the age group of over 55 years old, the performance of the model in this category was also separately evaluated on 339 subjects (170 negative and 169 positive). The model correctly identified 166 out of the 170 negatives and 164 out of the 169 positives. The model accuracy in this case was 97.3%.

Interpretation : The results showed that this method for the identification of COVID-19 infection is a promising tool, which can give fast and accurate results.

Liangou Aikaterini, Tasoglou Antonios, Huber Heinz J, Wistrom Christopher, Brody Kevin, Menon Prahlad G, Bebekoski Thomas, Menschel Kevin, Davidson-Fiedler Marlise, DeMarco Karl, Salphale Harshad, Wistrom Jonathan, Wistrom Skyler, Lee Richard J

2021-Dec

General General

COVID-19 Anomaly Detection and Classification Method based on Supervised Machine Learning of Chest X-ray Images.

In Results in physics

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

Hasoon Jamal N, Hussein Fadel Ali, Subhi Hameed Rasha, Mostafa Salama A, Ahmed Khalaf Bashar, Abed Mohammed Mazin, Nedoma Jan

2021-Nov-22

COVID-19 diagnosis, Haralick, k-nearest neighbor, local binary pattern, machine learning, support vector machine, x-ray image

General General

Office buildings occupancy analysis and prediction associated with the impact of the COVID-19 pandemic.

In Sustainable cities and society

Buildings' occupancy is one of the important factors causing the energy performance and sustainability gap in buildings. Better occupancy prediction decreases this gap both in the design stage and in the use phase of the building. Machine learning-based models proved to be very accurate and fast for occupancy prediction when buildings are exploited under normal conditions. Meanwhile, during the Covid-19 pandemic occupancy of the offices has dramatically changed. The study presents 2 office buildings' long-term monitoring results for different periods of the pandemic. It aims to analyse actual occupancies during the pandemic and its influence on the ELM (Extreme Learning Machine) based occupancy-forecasting models' reliability. The results show much lower actual occupancies in the offices than given in standards and methodologies; it is still low even when quarantines are cancelled. Average peak occupancy within the whole measured period is: for Building A - 12-20 % and for Building B - 2-23 %. The daily occupancy schedules differ for both offices as they belong to different industries. ELM-SA model has shown low accuracies during pandemic periods as a result of lower occupancies - R2 = 0.27-0.56.

Motuzienė Violeta, Bielskus Jonas, Lapinskienė Vilūnė, Rynkun Genrika, Bernatavičienė Jolita

2021-Nov-20

Covid-19, ELM, long-term monitoring, occupancy, pandemic, prediction

Radiology Radiology

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

In Applied soft computing

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

Huang Zhongwei, Lei Haijun, Chen Guoliang, Li Haimei, Li Chuandong, Gao Wenwen, Chen Yue, Wang Yaofa, Xu Haibo, Ma Guolin, Lei Baiying

2021-Nov-24

3D-CNN, COVID-19 diagnosis, Decision fusion, Histogram of oriented gradient, Multi-center sparse learning

General General

Towards sound based testing of COVID-19-Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge.

In Computer speech & language

The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the "Coswara" project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled "Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge". The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs.

Sharma Neeraj Kumar, Muguli Ananya, Krishnan Prashant, Kumar Rohit, Chetupalli Srikanth Raj, Ganapathy Sriram

2021-Nov-24

Acoustics, COVID-19, Healthcare, Machine learning, Respiratory diagnosis

Radiology Radiology

Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.

In Iranian journal of medical sciences

Background : Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.

Methods : In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest.

Results : The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%).

Conclusion : In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.

Tabatabaei Mohsen, Tasorian Baharak, Goyal Manu, Moini Abdollatif, Sotoudeh Houman

2021-Nov

** Artificial intelligence, COVID-19, Influenza, Human, Tomography**

General General

A large-scale systematic survey of SARS-CoV-2 antibodies reveals recurring molecular features

bioRxiv Preprint

In the past two years, the global research in combating COVID-19 pandemic has led to isolation and characterization of numerous human antibodies to the SARS-CoV-2 spike. This enormous collection of antibodies provides an unprecedented opportunity to study the antibody response to a single antigen. Using information derived from 88 research publications and 13 patents, we have assembled a dataset of ~8,000 human antibodies to the SARS-CoV-2 spike from >200 donors. Analysis of antibodies that target different domains of the spike protein reveals a number of common (public) responses to SARS-CoV-2, exemplified via recurring IGHV/IGK(L)V pairs, CDR H3 sequences, IGHD usage, and somatic hypermutation. We further present a proof-of-concept for predicting antigen specificity by using deep learning to differentiate sequences of antibodies to SARS-CoV-2 spike and to influenza hemagglutinin. Overall, this study not only provides an informative resource for antibody research, but fundamentally advances our molecular understanding of public antibody responses.

Wang, Y.; Yuan, M.; Peng, J.; Wilson, I. A.; Wu, N. C.

2021-11-30

Public Health Public Health

Impacts of emergency health protection measures upon air quality, traffic and public health: Evidence from Oxford, UK.

In Environmental pollution (Barking, Essex : 1987)

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM2.5, PM10 and O3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM2.5 and O3 concentration changes were not significant, but PM10 increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO2 emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO2 emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting.

Singh Ajit, Bartington Suzanne E, Song Congbo, Ghaffarpasand Omid, Kraftl Martin, Shi Zongbo, Pope Francis D, Stacey Brian, Hall James, Thomas G Neil, Bloss William J, Leach Felix C P

2021-Nov-26

Air quality, COVID-19, Deweathered, Meteorology, Oxford city, Vehicle emissions

General General

Prediction of COVID-19 epidemic situation via fine-tuned IndRNN.

In PeerJ. Computer science

The COVID-19 pandemic is the most serious catastrophe since the Second World War. To predict the epidemic more accurately under the influence of policies, a framework based on Independently Recurrent Neural Network (IndRNN) with fine-tuning are proposed for predict the epidemic development trend of confirmed cases and deaths in the United Stated, India, Brazil, France, Russia, China, and the world to late May, 2021. The proposed framework consists of four main steps: data pre-processing, model pre-training and weight saving, the weight fine-tuning, trend predicting and validating. It is concluded that the proposed framework based on IndRNN and fine-tuning with high speed and low complexity, has great fitting and prediction performance. The applied fine-tuning strategy can effectively reduce the error by up to 20.94% and time cost. For most of the countries, the MAPEs of fine-tuned IndRNN model were less than 1.2%, the minimum MAPE and RMSE were 0.05%, and 1.17, respectively, by using Chinese deaths, during the testing phase. According to the prediction and validation results, the MAPEs of the proposed framework were less than 6.2% in most cases, and it generated lowest MAPE and RMSE values of 0.05% and 2.14, respectively, for deaths in China. Moreover, Policies that play an important role in the development of COVID-19 have been summarized. Timely and appropriate measures can greatly reduce the spread of COVID-19; untimely and inappropriate government policies, lax regulations, and insufficient public cooperation are the reasons for the aggravation of the epidemic situations. The code is available at https://github.com/zhhongsh/COVID19-Precdiction. And the prediction by IndRNN model with fine-tuning are now available online (http://47.117.160.245:8088/IndRNNPredict).

Hong Zhonghua, Fan Ziyang, Tong Xiaohua, Zhou Ruyan, Pan Haiyan, Zhang Yun, Han Yanling, Wang Jing, Yang Shuhu, Wu Hong, Li Jiahao

2021

COVID-19, Deep Learning, Fine-tuning, Gated-Recurrent-Unit, Independently Recurrent Neural Network, Long-Short-Term-Memory, Prediction Model

General General

Household visitation during the COVID-19 pandemic.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic has posed novel risks related to the indoor mixing of individuals from different households and challenged policymakers to adequately regulate this behaviour. While in many cases household visits are necessary for the purpose of social care, they have been linked to broadening community transmission of the virus. In this study we propose a novel, privacy-preserving framework for the measurement of household visitation at national and regional scales, making use of passively collected mobility data. We implement this approach in England from January 2020 to May 2021. The measures expose significant spatial and temporal variation in household visitation patterns, impacted by both national and regional lockdown policies, and the rollout of the vaccination programme. The findings point to complex social processes unfolding differently over space and time, likely informed by variations in policy adherence, vaccine relaxation, and regional interventions.

Ross Stuart, Breckenridge George, Zhuang Mengdie, Manley Ed

2021-Nov-25

Public Health Public Health

Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.

In Computers in biology and medicine

The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust experimental design combining deep learning with molecular docking experiments to identify the most promising candidates from the list of FDA-approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning-based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2440 FDA-approved and 8168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA-approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran around 50,000 docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. A list of 49 most promising FDA-approved drugs with the best consensus KIBA scores and binding affinity values against selected SARS-CoV-2 viral proteins was generated. Most importantly, 16 drugs including anidulafungin, velpatasvir, glecaprevir, rifapentine, flavin adenine dinucleotide (FAD), terlipressin, and selinexor demonstrated the highest predicted inhibitory potential against key SARS-CoV-2 viral proteins. We further measured the inhibitory activity of 5 compounds (rifapentine, velpatasvir, glecaprevir, anidulafungin, and FAD disodium) on SARS-CoV-2 PLpro using Ubiquitin-Rhodamine 110 Gly fluorescent intensity assay. The highest inhibition of PLpro activity was seen with rifapentine (IC50: 15.18 μM) and FAD disodium (IC50: 12.39 μM), the drugs with high predicted KIBA scores and binding affinities.

Anwaar Muhammad U, Adnan Farjad, Abro Asma, Khan Rayyan A, Rehman Asad U, Osama Muhammad, Rainville Christopher, Kumar Suresh, Sterner David, Javed Saad, Jamal Syed B, Baig Ahmadullah, Shabbir Muhammad R, Ahsan Waseh, Butt Tauseef R, Assir Muhammad Z

2021-Nov-20

Binding affinity, Docking, Drug repurposing, Machine learning, SARS-CoV-2

General General

Deep-AVPpred: Artificial intelligence driven discovery of peptide drugs for viral infections.

In IEEE journal of biomedical and health informatics

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

Sharma Ritesh, Shrivastava Sameer, Singh Sanjay Kumar, Kumar Abhinav, Singh Amit Kumar, Saxena Sonal

2021-Nov-25

General General

Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations.

In Bioengineering (Basel, Switzerland)

The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing resources exists in regional and developing areas of the world. In this paper, we evaluate and report the computational and predictive performance design trade-offs for four candidate deep learning models that can be deployed for rapid malaria case finding. The goal is to maximise malaria detection accuracy while reducing computing resource and energy consumption. Based on our experimental results using a blood smear malaria test data set, the quantised versions of Basic Convolutional Neural Network (B-CNN) and MobileNetV2 have better malaria detection performance (up to 99% recall), lower memory usage (2MB 8-bit quantised model) and shorter inference time (33-95 microseconds on mobile phones) than VGG-19 fine-tuned and quantised models. Hence, we have implemented MobileNetV2 in our mobile application as it has even a lower memory requirement than B-CNN. This work will help to counter the negative effects of COVID-19 on the previous successes towards global malaria elimination.

Eze Peter U, Asogwa Clement O

2021-Oct-21

deep learning, digital health, edge devices, malaria, model quantisation, resource optimisation

General General

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

In Computational and mathematical methods in medicine

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

Bangyal Waqas Haider, Qasim Rukhma, Rehman Najeeb Ur, Ahmad Zeeshan, Dar Hafsa, Rukhsar Laiqa, Aman Zahra, Ahmad Jamil

2021

General General

Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net.

In Scientific reports ; h5-index 158.0

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.

Zhang Qin, Ren Xiaoqiang, Wei Benzheng

2021-Nov-24

Surgery Surgery

The amputation and mortality of inpatients with diabetic foot ulceration in the COVID-19 pandemic and postpandemic era: A machine learning study.

In International wound journal

This study aimed to explore the clinical characteristic and outcomes of inpatients with diabetic foot ulceration (DFU) in 2019 (prelockdown) and 2020 (postlockdown) due to the COVID-19 pandemic, at an emergency medical service unit. Prediction models for mortality and amputation were developed to describe the risk factors using a machine learning-based approach. Hospitalized DFU patients (N = 23) were recruited after the lockdown in 2020 and matched with corresponding inpatients (N = 23) before lockdown in 2019. Six widely used machine learning models were built and internally validated using 3-fold cross-validation to predict the risk of amputation and death in DFU inpatients under the COVID-19 pandemic. Previous DF ulcers, prehospital delay, and mortality were significantly higher in 2020 compared to 2019. Diabetic foot patients in 2020 had higher hs-CRP levels (P = .037) but lower hemoglobin levels (P = .017). The extreme gradient boosting (XGBoost) performed best in all models for predicting amputation and mortality with the highest area under the curve (0.86 and 0.94), accuracy (0.80 and 0.90), sensitivity (0.67 and 1.00), and negative predictive value (0.86 and 1.00). A long delay in admission and a higher risk of mortality was observed in patients with DFU who attended the emergency center during the COVID-19 post lockdown. The XGBoost model can provide evidence-based risk information for patients with DFU regarding their amputation and mortality. The prediction models would benefit DFU patients during the COVID-19 pandemic.

Du Chenzhen, Li Yuyao, Xie Puguang, Zhang Xi, Deng Bo, Wang Guixue, Hu Youqiang, Wang Min, Deng Wu, Armstrong David G, Ma Yu, Deng Wuquan

2021-Nov-24

COVID-19 pandemic, amputation, diabetic foot ulceration, machine learning, mortality

General General

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19.

In Research square

BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

Xu Yixi, Trivedi Anusua, Becker Nicholas, Blazes Marian, Ferres Juan, Lee Aaron, Liles W, Bhatraju Pavan

2021-Nov-16

General General

Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods.

In Procedia computer science

The large amount of data accumulated so far on the dynamics of the COVID-19 outbreak has allowed assessing the accuracy of forecasting methods in retrospect. This work compares several basic time series analysis methods, including machine learning methods, for forecasting the number of confirmed cases for some days ahead. Year-long data for all regions of Russia has been used from the Yandex DataLens platform. As a result, accuracy estimates for these basic methods have been obtained for Russian regions and Russia as a whole, in dependence on the forecasting horizon. The best basic models for forecasting for 14 days are exponential smoothing and ARIMA, with an error of 11-19% by the MAPE metric for the latest part of the course of the epidemic. The accuracies obtained can be considered as baselines for more complex prospective models.

Naumov A V, Moloshnikov I A, Serenko A V, Sboev A G, Rybka R B

2021

COVID forecasting, Machine learning, Time series analysis

General General

India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability.

In Soft computing

The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed.

Ketu Shwet, Mishra Pramod Kumar

2021-Nov-19

CNN-LSTM, COVID-19, Deep learning, Medical resource, Time series prediction

General General

A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods.

In Expert systems with applications

The restrictions have been preferred by governments to reduce the spread of Covid-19 and to protect people's health according to regional risk levels. The risk levels of locations are determined due to threshold values ​​based on the number of cases per 100,000 people without environmental variables. The purpose of our study is to apply unsupervised machine learning techniques to determine the cities with similar risk levels by using the number of cases and environmental parameters. Hierarchical, partitional, soft, and gray relational clustering algorithms were applied to different datasets created with weekly the number of cases, population densities, average ages, and air pollution levels. Comparisons of the clustering algorithms were performed by using internal validation indexes, and the most successful method was identified. In the study, it was revealed that the most successful method in clustering based on the number of cases is Gray Relational Clustering. The results show that using the environmental variables for restrictions requires more clusters than 4 for healthier decisions and Gray Relational Clustering gives stable results, unlike other algorithms.

Fidan Huseyin, Erkan Yuksel Mehmet

2021-Nov-19

Clustering, Covid-19, Gray relational clustering, Restrictions, Risk levels, Unsupervised machine learning

Radiology Radiology

Factors determining generalization in deep learning models for scoring COVID-CT images.

In Mathematical biosciences and engineering : MBE

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.

Horry Michael James, Chakraborty Subrata, Pradhan Biswajeet, Fallahpoor Maryam, Chegeni Hossein, Paul Manoranjan

2021-Oct-27

** COVID-19 scoring , computed tomography , deep learning , external validation , image pre-processing , model generalization **

General General

A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences.

In Mathematical biosciences and engineering : MBE

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.

Deif Mohanad A, Solyman Ahmed A A, Kamarposhti Mehrdad Ahmadi, Band Shahab S, Hammam Rania E

2021-Oct-15

** COVID-19 , GRU , LSTM Multi-class classification , SARS-CoV-2 , coronavirus , deep learning , recurrent neural networks **

Public Health Public Health

Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study.

In Mathematical biosciences and engineering : MBE

Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that there is a significant benefit in adhering to the COVID-19 NPIs.

Iyaniwura Sarafa A, Rabiu Musa, David Jummy F, Kong Jude D

2021-Oct-15

** COVID-19 , SARS-CoV-2 , adherent and non-adherent , direct and indirect transmission , epidemics , non-pharmaceutical intervention , population dynamics , seir models **

General General

Tackling pandemics in smart cities using machine learning architecture.

In Mathematical biosciences and engineering : MBE

With the recent advancement in analytic techniques and the increasing generation of healthcare data, artificial intelligence (AI) is reinventing the healthcare system for tackling pandemics securely in smart cities. AI tools continue register numerous successes in major disease areas such as cancer, neurology and now in new coronavirus SARS-CoV-2 (COVID-19) detection. COVID-19 patients often experience several symptoms which include breathlessness, fever, cough, nausea, sore throat, blocked nose, runny nose, headache, muscle aches, and joint pains. This paper proposes an artificial intelligence (AI) algorithm that predicts the rate of likely survivals of COVID-19 suspected patients based on good immune system, exercises and age quantiles securely. Four algorithms (Naïve Bayes, Logistic Regression, Decision Tree and k-Nearest Neighbours (kNN)) were compared. We performed True Positive (TP) rate and False Positive (FP) rate analysis on both positive and negative covid patients data. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Naïve Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive covid patients, Naïve Bayes outperformed other models with a score of 10.90%. On the other hand, Naïve Bayes obtained a score of 89.10% for FP rate for negative patients while Logistic Regression, kNN, and Decision Tree obtained scores of 93.90%, 93.90%, and 94.50%, respectively.

Ngabo Desire, Dong Wang, Ibeke Ebuka, Iwendi Celestine, Masabo Emmanuel

2021-Sep-27

** artificial intelligence , pandemics , smart cities **

General General

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification.

In IEEE access : practical innovations, open solutions

The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.

El-Kenawy El-Sayed M, Mirjalili Seyedali, Ibrahim Abdelhameed, Alrahmawy Mohammed, El-Said M, Zaki Rokaia M, Eid Marwa Metwally

2021

Chest X-ray, convolutional neural network, multilayer perceptron, optimization algorithm, squirrel search optimization, transfer learning

General General

Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs.

In IEEE access : practical innovations, open solutions

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid.

Zhang Pengyi, Zhong Yunxin, Deng Yulin, Tang Xiaoying, Li Xiaoqiong

2020

COVID-19~diagnosis, DRRs, X-ray imaging, deep learning, infection segmentation

Radiology Radiology

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.

In IEEE access : practical innovations, open solutions

Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.

Casiraghi Elena, Malchiodi Dario, Trucco Gabriella, Frasca Marco, Cappelletti Luca, Fontana Tommaso, Esposito Alessandro Andrea, Avola Emanuele, Jachetti Alessandro, Reese Justin, Rizzi Alessandro, Robinson Peter N, Valentini Giorgio

2020

Associative tree, Boruta feature selection, COVID-19, clinical data analysis, generalized linear models, missing data imputation, random forest classifier, risk prediction

General General

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

In IEEE access : practical innovations, open solutions

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19's endemic and pandemic.

Lai Yaoming, Li Guangming, Wu Dongmei, Lian Wanmin, Li Cheng, Tian Junzhang, Ma Xiaofen, Chen Hui, Xu Wen, Wei Jun, Zhang Yaqin, Jiang Guihua

2020

2019 novel coronavirus-infected pneumonia, COVID-19, chest CT, few-shot learning

General General

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life.

In IEEE access : practical innovations, open solutions

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

Vedaei Seyed Shahim, Fotovvat Amir, Mohebbian Mohammad Reza, Rahman Gazi M E, Wahid Khan A, Babyn Paul, Marateb Hamid Reza, Mansourian Marjan, Sami Ramin

2020

COVID-19, IoT, health monitoring, pandemic, smart healthcare

General General

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

In Soft computing

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.

Laxmi Lydia E, Anupama C S S, Beno A, Elhoseny Mohamed, Alshehri Mohammad Dahman, Selim Mahmoud M

2021-Nov-18

COVID-19, Chest X-ray images, Cognitive computing, Deep learning, Edge computing, Federated learning, Internet of things, Pattern recognition

General General

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

In Molecular informatics

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

John Lijo, Soujanya Yarasi, Mahanta Hridoy Jyoti, Narahari Sastry G

2021-Nov-23

Antivirals, Chemoinformatics, Feature Selection, MCC, Machine Learning, Molecular Descriptors, SARS-COVID-19

Public Health Public Health

Data revolution, health status transformation and the role of artificial intelligence for health and pandemic preparedness in the African context.

In BMC proceedings

BACKGROUND : Artificial Intelligence (AI) platforms, increasingly deployed in public health, utilize robust data systems as a critical component for health emergency preparedness. Yet, Africa faces numerous challenges in the availability, analyses, and use of data to inform health decision-making. Countries have limited access to their population data. Those with access, struggle to utilize these data for program improvements. Owing to the rapid growth of mobile phone ownership and use in the region, Africa is poised to leverage AI technologies to increase the adoption, access and use of data for health. To discuss and propose solutions for responsible development and adoption of innovations like AI in Africa, a virtual workshop was organized from the 21st to 24th June, 2021. This report highlights critical policy dimensions of strengthening digital health ecosystems by high-level policymakers, technical experts, academia, public and private sector partners.

METHOD : The four days' workshop focused on nine sessions, with each session focusing on three themes. Discussions during the sessions concentrated on public and private sectors, the academia and multilateral organizations' deployment of AI. These discussions expanded participants' understanding of AI, the opportunities and challenges that exist during adoption, including the future of AI for health in the African region. Approximately 250 participants attended the workshop, including countries representatives from ministries of Health, Information and Technology, Developmental Organizations, Private Sector, Academia and Research Institutions among others.

RESULTS : The workshop resolved that governments and relevant stakeholders should collaborate to ensure that AI and digital health receive critical attention. Government ownership and leadership were identified as critical for sustainable financing and effective scale-up of AI-enabled applications in Africa. Thus, government is to ensure that key recommendations from the workshop are implemented to improve health sector development in Africa.

CONCLUSIONS : The AI workshop was a good forum to deliberate important issues regarding AI for health in the African context. It was concluded that there is a need to focus on vital priorities in deploying AI in Africa: Data protection, privacy and sharing protocols; training and creating platforms for researchers; funding and business models; developing frameworks for assessing and implementing AI; organizing forums and conferences on AI; and instituting regulations, governance and ethical guidelines for AI. There is a need to adopt a health systems approach in planning for AI to reduce inefficiencies, redundancies while increasing effectiveness in the use of AI. Thus, robust collaborations and partnerships among governments and various stakeholders were identified as key.

Ibeneme Sunny, Okeibunor Joseph, Muneene Derrick, Husain Ishrat, Bento Pascoal, Gaju Carol, Housseynou Ba, Chibi Moredreck, Karamagi Humphrey, Makubalo Lindiwe

2021-Nov-22

Africa, Artificial intelligence, COVID-19, Digital health, Healthcare innovation

General General

Mental health symptoms among American veterans during the COVID-19 Pandemic.

In Psychiatry research ; h5-index 64.0

We examined the symptom trajectories of posttraumatic stress disorder (PTSD), depression, and anxiety among 1,230 American veterans assessed online one month prior to the COVID-19 outbreak in the United States (February 2020) through the next year (August 2020, November 2020, February 2021). Veterans slightly increased mental health symptoms over time and those with pre-pandemic alcohol and cannabis use disorders reported greater symptoms compared to those without. Women and racial/ethnic minority veterans reported greater symptoms pre-pandemic but less steep increases over time compared to men and white veterans. Findings point to the continued need for mental health care efforts with veterans.

Pedersen Eric R, Davis Jordan P, Prindle John, Fitzke Reagan E, Tran Denise D, Saba Shaddy

2021-Nov-17

Mental health, Substance use, Veterans

General General

Methods to Engage Patients in the Modern Clinic.

In Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology

OBJECTIVE : To identify current patient and provider engagement methods that utilize technology in allergy and immunology clinics, hospitals and at home.

DATA SOURCES : Apple App Store and Google searches for allergy and immunology technology apps, PubMed search of literature involving keywords of: website, technology, EMR, medical devices, disparity in technology, coding for remote patient monitoring and artificial intelligence.

STUDY SELECTIONS : Studies that addressed the keywords were included and narrowed down based upon their applicability in the allergy and immunology clinic.

RESULTS : There has been rapid innovation in the digital healthcare space with expansion of EMR services and the patient portal, creation of allergy and immunology specific medical devices and apps with remote patient monitoring capabilities, and website and artificial intelligence development to interact with patients.

CONCLUSION : These technological advances provide distinct advantages to the provider and patient, but also have a burden of time for evaluation of the data for the provider and disparate access to certain technologies for patients. The development of these technologies has been fast-tracked since the start of the Covid-19 pandemic. With the explosion in telehealth and medical device development, advancement of medical technology is not showing any signs of slowing down. It is paving a new way to interact with patients in the future.

Maurer Laura E, Bansal Chandani, Bansal Priya

2021-Nov-20

App, Artificial Intelligence, Covid-19, Digital, EMR, Healthcare Disparities, Mobile Device, Patient Portal, Technology, Website

General General

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

In PloS one ; h5-index 176.0

BACKGROUND : Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis.

METHODS AND PERFORMANCE : Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days).

CONCLUSION : We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.

Bishop Jennifer A, Javed Hamza A, El-Bouri Rasheed, Zhu Tingting, Taylor Thomas, Peto Tim, Watkinson Peter, Eyre David W, Clifton David A

2021

General General

Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning.

In Patterns (New York, N.Y.)

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome including previously unappreciated features in spike, RdRp and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural and immunological features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.

Park Jonathan J, Chen Sidi

2021-Nov-18

General General

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic.

In IEEE access : practical innovations, open solutions

Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

Poongodi M, Malviya Mohit, Hamdi Mounir, Rauf Hafiz Tayyab, Kadry Seifedine, Thinnukool Orawit

2021

5G, CT-scan, Epidemic, X-Ray, artificial intelligence, cloud, coronavirus, drone, telemedicine

General General

UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.

In BMC medical imaging

BACKGROUND : With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning.

METHODS : This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality.

RESULTS : The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models.

CONCLUSION : In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

Miao Rui, Dong Xin, Xie Sheng-Li, Liang Yong, Lo Sio-Long

2021-Nov-22

CNN, COVID-19, UMLF-COVID, X-ray

General General

Unsupervised clustering analysis of SARS-Cov-2population structure reveals six major subtypes atearly stage across the world

bioRxiv Preprint

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.

Li, Y.; Liu, Q.; Zeng, Z.; Luo, Y.

2021-11-24

General General

From Hume to Wuhan: An Epistemological Journey on the Problem of Induction in COVID-19 Machine Learning Models and its Impact Upon Medical Research.

In IEEE access : practical innovations, open solutions

Advances in computer science have transformed the way artificial intelligence is employed in academia, with Machine Learning (ML) methods easily available to researchers from diverse areas thanks to intuitive frameworks that yield extraordinary results. Notwithstanding, current trends in the mainstream ML community tend to emphasise wins over knowledge, putting the scientific method aside, and focusing on maximising metrics of interest. Methodological flaws lead to poor justification of method choice, which in turn leads to disregard the limitations of the methods employed, ultimately putting at risk the translation of solutions into real-world clinical settings. This work exemplifies the impact of the problem of induction in medical research, studying the methodological issues of recent solutions for computer-aided diagnosis of COVID-19 from chest X-Ray images.

Vega Carlos

2021

Biomedical imaging, X-rays, computational systems biology, machine learning, philosophical considerations

Public Health Public Health

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.

In IEEE access : practical innovations, open solutions

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Nguyen Dinh C, Ding Ming, Pathirana Pubudu N, Seneviratne Aruna

2021

Artificial Intelligence (AI), Blockchain, SARS-CoV-2, coronavirus (COVID-19), deep learning, epidemic, machine learning, privacy, security

General General

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images.

In IEEE access : practical innovations, open solutions

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.

Calderon-Ramirez Saul, Yang Shengxiang, Moemeni Armaghan, Colreavy-Donnelly Simon, Elizondo David A, Oala Luis, Rodriguez-Capitan Jorge, Jimenez-Navarro Manuel, Lopez-Rubio Ezequiel, Molina-Cabello Miguel A

2021

Coronavirus, Covid-19, MixMatch, Uncertainty estimation, chest x-ray, computer aided diagnosis, semi-supervised deep learning

General General

COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images.

In IEEE access : practical innovations, open solutions

As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.

Zhou Changjian, Song Jia, Zhou Sihan, Zhang Zhiyao, Xing Jinge

2021

COVID-19, Resnet-SVM, deep learning, medical image processing

General General

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.

In IEEE access : practical innovations, open solutions

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

Navaz Alramzana Nujum, Serhani Mohamed Adel, El Kassabi Hadeel T, Al-Qirim Nabeel, Ismail Heba

2021

Artificial intelligence, COVID-19, IoT, big data, deep learning, healthcare, robotics, smart and connected healthcare

General General

Deep Learning for SARS COV-2 Genome Sequences.

In IEEE access : practical innovations, open solutions

The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses. Besides, we classify whether a genome sequence contains candidate regulatory motifs or otherwise. Regulatory motifs bind to transcription factors. Transcription factors are responsible for the expression of genes. The experimental results show that at peak performance, the proposed convolutional neural network bi-directional long short-term memory (CNN-Bi-LSTM) model achieves a classification accuracy of 99.95%, area under curve receiver operating characteristic (AUC ROC) of 100.00%, a specificity of 99.97%, the sensitivity of 99.97%, Cohen's Kappa equal to 0.9978, Mathews Correlation Coefficient (MCC) equal to 0.9978 for the classification of SARS CoV-2 amongst Coronaviruses. Also, the CNN-Bi-LSTM correctly detects whether a sequence has candidate regulatory motifs or binding-sites with a classification accuracy of 99.76%, AUC ROC of 100.00%, a specificity of 99.76%, a sensitivity of 99.76%, MCC equal to 0.9980, and Cohen's Kappa of 0.9970 at peak performance. These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regulatory motifs in the SARS CoV-2 genes.

Whata Albert, Chimedza Charles

2021

Bi-directional long-short memory, SARS-CoV-2, convolutional neural network, coronavirus deep learning, deoxyribonucleic acid

Public Health Public Health

IoT Platform for COVID-19 Prevention and Control: A Survey.

In IEEE access : practical innovations, open solutions

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and low vaccination rates, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.

Dong Yudi, Yao Yu-Dong

2021

COVID-19, Internet of Things, SARS-CoV-2, artificial intelligence, big data, fog computing, smart healthcare

General General

MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

In IEEE access : practical innovations, open solutions

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

Pei Hong-Yang, Yang Dan, Liu Guo-Ru, Lu Tian

2021

COVID-19, CT, MPS-Net, U-Nnet

General General

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients.

In IEEE access : practical innovations, open solutions

Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.

Catala Omar Del Tejo, Igual Ismael Salvador, Perez-Benito Francisco Javier, Escriva David Millan, Castello Vicent Ortiz, Llobet Rafael, Perez-Cortes Juan-Carlos

2021

COVID-19, Deep learning, bias, chest X-ray, convolutional neural networks, saliency map, segmentation

General General

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal.

In IEEE access : practical innovations, open solutions

The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%~19.98% better precision, getting closer to achievable bounds for estimation.

Su Zhuoran, Pahlavan Kaveh, Agu Emmanuel

2021

BLE, COVID-19, PACT, RSSI features, classical estimation theory, machine learning, proximity detection

General General

Efficient masked face recognition method during the COVID-19 pandemic.

In Signal, image and video processing

The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN), namely VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods.

Hariri Walid

2021-Nov-15

COVID-19, Deep learning, Face recognition, Masked face

General General

Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network.

In Biomedical signal processing and control

Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.

Soltanian Mohammad, Borna Keivan

2022-Feb

Computational complexity, Convolutional neural network, Kernel separation, MFCC, Quadratic convolution

General General

Novel multi-site graph convolutional network with supervision mechanism for COVID-19 diagnosis from X-ray radiographs.

In Applied soft computing

The novel Coronavirus disease 2019 (COVID-2019) has become a global pandemic and affected almost all aspects of our daily life. The total number of positive COVID-2019 cases has exponentially increased in the last few months due to the easy transmissibility of the virus. It can be detected using the nucleic acid test or the antibodies blood test which are not always available and take several hours to get the results. Therefore, researchers proposed computer-aided diagnosis systems using the state-of-the-art artificial intelligence techniques to learn imaging biomarkers from chest computed tomography and X-ray radiographs to effectively diagnose COVID-19. However, previous methods either adopted transfer learning from a pre-trained model on natural images or trained on limited datasets. Either cases may lead to accuracy deficiency or overfitting. In addition, feature space suffers from noise and outliers when collecting X-ray images from multiple datasets. In this paper, we overcome the previous limitation by firstly collecting a large-scale X-ray dataset from multiple resources. Our dataset includes 11,312 images collected from 10 different data repositories. To alleviate the effect of the noise, we suppress it in the feature space of our new dataset. Secondly, we introduce a supervision mechanism and combine it with the VGG-16 network to consider the differences between the COVID-19 and healthy cases in the feature space. Thirdly, we propose a multi-site (center) COVID-19 graph convolutional network (GCN) that exploits dataset information, the status of training samples, and initial scores to effectively classify the disease status. Extensive experiments using different convolutional neural network-based methods with and without the supervision mechanism and different classifiers are performed. Results demonstrate the effectiveness of the proposed supervision mechanism in all models and superior performance with the proposed GCN.

Elazab Ahmed, Elfattah Mohamed Abd, Zhang Yuexin

2021-Nov-16

COVID-19, Chest X-ray radiographs, Multi-site graph convolutional network, Supervision mechanism, VGG-16 network

General General

Novel approaches for COVID-19 diagnosis and treatment: a nonsystematic review.

In Turkish journal of biology = Turk biyoloji dergisi

Since COVID-19 pandemic has been continuously rising and spreading, several original contributions and review articles on COVID-19 started to appear in the literature. The review articles are mainly focus on the current status of the pandemic along with current status of the corona diagnosis and treatment process. Due to some disadvantages of the currently used methods, the improvement on the novel promising diagnosis and treatment methods of corona virus is very important issue. In this review, after briefly discussing the status of current diagnosis and treatment methods, we present to the scientific community, novel promising methods in the diagnosis and treatment of COVID-19. As with other novel approaches, first, the diagnosis potential of mass spectroscopy and optical spectroscopic methods such as UV/visible, infrared, and Raman spectroscopy coupled with chemometrics will be discussed for the corona virus infected samples based on the relevant literature. In vibrational spectroscopy studies, due to complexity of the data, multivariate analysis methods are also applied to data. The application of multivariate analysis tools that can be used to extract useful information from the data for diagnostic and characterisation purposes is also included in this review. The reviewed methods include hierarchical cluster analysis, principal component analysis, linear and quadratic discriminant analysis, support vector machine algorithm, and one form of neural networks namely deep learning method. Second, novel treatment methods such as photodynamic therapy and the use of nanoparticles in the in-corona virus therapy will be discussed. Finally, the advantages of novel promising diagnosis and treatment methods in COVID-19, over standard methods will be discussed. One of the main aims of this paper is to encourage the scientific community to explore the potential of this novel tools for their use in corona virus characterization, diagnosis, and treatment.

Garip Ustaoğlu Şebnem, Kaygusuz Hakan, Bilgin Mehmet Dinçer, Severcan Feride

2021

COVID-19, Raman spectroscopy, diagnosis, infrared spectroscopy, nanotechnology, novel methods

General General

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.

In Neural computing & applications

Recently, the COVID-19 pandemic has triggered different behaviors in education, especially during the lockdown, to contain the virus outbreak in the world. As a result, educational institutions worldwide are currently using online learning platforms to maintain their education presence. This research paper introduces and examines a dataset, E-LearningDJUST, that represents a sample of the student's study progress during the pandemic at Jordan University of Science and Technology (JUST). The dataset depicts a sample of the university's students as it includes 9,246 students from 11 faculties taking four courses in spring 2020, summer 2020, and fall 2021 semesters. To the best of our knowledge, it is the first collected dataset that reflects the students' study progress within a Jordanian institute using e-learning system records. One of this work's key findings is observing a high correlation between e-learning events and the final grades out of 100. Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. However, the proportion of students with high marks remained similar to that of pre-pandemic courses.

Abdullah Malak, Al-Ayyoub Mahmoud, AlRawashdeh Saif, Shatnawi Farah

2021-Nov-13

COVID-19, Correlation, E-learning, Machine learning

General General

Modular composite building in urgent emergency engineering projects: A case study of accelerated design and construction of Wuhan Thunder God Mountain/Leishenshan hospital to COVID-19 pandemic.

In Automation in construction

Wuhan Leishenshan/Leishenshan ("Leishenshan" for short) hospital is a makeshift emergency hospital for treating patients diagnosed with the novel coronavirus-infected pneumonia (NCIP). Engineering construction uses modular composite building finished products to the greatest extent, which reduces the workload of field operations and saves a lot of time. The building information model (BIM) technology assists in design and construction work to meet rapid construction requirements. Besides, based on the unmanned aerial vehicles (UAVs) data analysis and application platform, digitization and intelligence in engineering construction are improved. Simultaneously, on-site construction and overall hoisting were carried out to achieve maximum efficiency. This article aims to take the construction of Leishenshan Hospital as an example to illustrate how to adopt BIM technology and other high-tech technology such as big data, artificial intelligence, drones, and 5G for the fast construction of the fabricated steel structure systems in emergency engineering projects.

Chen Ling-Kun, Yuan Rui-Peng, Ji Xing-Jun, Lu Xing-Yu, Xiao Jiang, Tao Jun-Bo, Kang Xin, Li Xin, He Zhen-Hua, Quan Shu, Jiang Li-Zhong

2021-Apr

Accelerated design and construction, Building information model (BIM) technology, Light steel structure, Modular composite building, Off-site building modular units, Unmanned aerial vehicles (UAVs), Wuhan Leishenshan hospital

General General

Global Sensitivity Analysis in Epidemiological Modeling.

In European journal of operational research

Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.

Lu Xuefei, Borgonovo Emanuele

2021-Nov-16

Analytics, COVID-19 pandemic, Global sensitivity analysis, OR in pandemics, SIR models

Public Health Public Health

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

In Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.

Jiang Jyun-Yu, Zhou Yichao, Chen Xiusi, Jhou Yan-Ru, Zhao Liqi, Liu Sabrina, Yang Po-Chun, Ahmar Jule, Wang Wei

2022-Jan-10

knowledge graph, natural language processing, pandemic surveillance, social media mining

Public Health Public Health

Data science approaches to confronting the COVID-19 pandemic: a narrative review.

In Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Zhang Qingpeng, Gao Jianxi, Wu Joseph T, Cao Zhidong, Dajun Zeng Daniel

2022-Jan-10

COVID-19, big data, data science, infectious disease, mathematical modelling

Cardiology Cardiology

Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic.

In PloS one ; h5-index 176.0

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.

Melms Leander, Falk Evelyn, Schieffer Bernhard, Jerrentrup Andreas, Wagner Uwe, Matrood Sami, Schaefer Jürgen R, Müller Tobias, Hirsch Martin

2021

General General

Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic.

In PloS one ; h5-index 176.0

The COVID-19 pandemic continues to impact people worldwide-steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people's intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people's trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people's medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures-without eroding trust in human physicians.

Frank Darius-Aurel, Elbæk Christian T, Børsting Caroline Kjær, Mitkidis Panagiotis, Otterbring Tobias, Borau Sylvie

2021

General General

A Markerless 2D Video, Facial Feature Recognition-Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible.

OBJECTIVE : The study aimed to develop a markerless 2D video, facial feature recognition-based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis.

METHODS : We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists.

RESULTS : The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD.

CONCLUSIONS : PD patients commonly exhibit masked facial features. Videos of a facial feature recognition-based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient's condition, especially during the COVID-19 pandemic.

Hou Xinyao, Zhang Yu, Wang Yanping, Wang Xinyi, Zhao Jiahao, Zhu Xiaobo, Su Jianbo

2021-Nov-19

Parkinson disease, artificial intelligence, diagnosis, facial features

General General

Intelligent financial fraud detection practices in post-pandemic era.

In Innovation (New York, N.Y.)

The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.

Zhu Xiaoqian, Ao Xiang, Qin Zidi, Chang Yanpeng, Liu Yang, He Qing, Li Jianping

2021-Nov-28

COVID-19 pandemic, artificial intelligence, financial fraud detection

oncology Oncology

Impaired Dendritic Cell Homing in COVID-19.

In Frontiers in medicine

The high mortality of COVID-19 is mostly attributed to acute respiratory distress syndrome (ARDS), whose histopathological correlate is diffuse alveolar damage (DAD). Furthermore, severe COVID-19 is often accompanied by a cytokine storm and a disrupted response of the adaptive immune system. Studies aiming to depict this dysregulation have mostly investigated the peripheral cell count as well as the functionality of immune cells. We investigated the impact of SARS-CoV-2 on antigen-presenting cells using multiplexed immunofluorescence. Similar to MERS-CoV and SARS-CoV, SARS-CoV-2 appears to be impairing the maturation of dendritic cells (DCs). DC maturation involves a switch in surface antigen expression, which enables the cells' homing to lymph nodes and the subsequent activation of T-cells. As quantitative descriptions of the local inflammatory infiltrate are still scarce, we compared the cell population of professional antigen-presenting cells (APC) in the lungs of COVID-19 autopsy cases in different stages of DAD. We found an increased count of myeloid dendritic cells (mDCs) in later stages. Interestingly, mDCs also showed no significant upregulation of maturation markers in DAD-specimens with high viral load. Accumulation of immature mDCs, which are unable to home to lymph nodes, ultimately results in an inadequate T-cell response.

Borcherding Lukas, Teksen Alime Sema, Grosser Bianca, Schaller Tina, Hirschbühl Klaus, Claus Rainer, Spring Oliver, Wittmann Michael, Römmele Christoph, Sipos Éva, Märkl Bruno

2021

COVID-19, SARS-CoV-2, artificial intelligence, dendritic cells, diffuse alveolar damage (DAD), homing, maturation, multiplexed immunofluorescence

General General

Effects of Social Mobility and Stringency Measures on the COVID-19 Outcomes: Evidence From the United States.

In Frontiers in public health

This paper examines the effects of stringency measures (provided by the Oxford Coronavirus Government Response Tracker) and total time spent away from home (provided by the Google COVID-19 Community Mobility Reports) on the COVID-19 outcomes (measured by total COVID-19 cases and total deaths related to the COVID-19) in the United States. The paper focuses on the daily data from March 11, 2020 to August 13, 2021. The ordinary least squares and the machine learning estimators show that stringency measures are negatively related to the COVID-19 outcomes. A higher time spent away from home is positively associated with the COVID-19 outcomes. The paper also discusses the potential economic implications for the United States.

Sun Jianmin, Kwek Keh, Li Min, Shen Hongzhou

2021

COVID-19 outcomes, machine learning estimator, ordinary least squares, social mobility, stringency measures, the United States economy

Radiology Radiology

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission.

In Cureus

Aim This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission. Methods The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. Results A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. Conclusion Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.

Tabatabaie Mohsen, Sarrami Amir Hossein, Didehdar Mojtaba, Tasorian Baharak, Shafaat Omid, Sotoudeh Houman

2021-Oct

artificial intelligence, covid-19, machine learning, mortality, pandemics, prognosis

General General

Machine Learning Approach Effectively Predicts Binding Between SARS-CoV-2 Spike and ACE2 Across Mammalian Species - Worldwide, 2021.

In China CDC weekly

Introduction : Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recently emergent coronavirus of natural origin and caused the coronavirus disease (COVID-19) pandemic. The study of its natural origin and host range is of particular importance for source tracing, monitoring of this virus, and prevention of recurrent infections. One major approach is to test the binding ability of the viral receptor gene ACE2 from various hosts to SARS-CoV-2 spike protein, but it is time-consuming and labor-intensive to cover a large collection of species.

Methods : In this paper, we applied state-of-the-art machine learning approaches and created a pipeline reaching >87% accuracy in predicting binding between different ACE2 and SARS-CoV-2 spike.

Results : We further validated our prediction pipeline using 2 independent test sets involving >50 bat species and achieved >78% accuracy. A large-scale screening of 204 mammal species revealed 144 species (or 61%) were susceptible to SARS-CoV-2 infections, highlighting the importance of intensive monitoring and studies in mammalian species.

Discussion : In short, our study employed machine learning models to create an important tool for predicting potential hosts of SARS-CoV-2 and achieved the highest precision to our knowledge in experimental validation. This study also predicted that a wide range of mammals were capable of being infected by SARS-CoV-2.

Ma Yue, Hu Yu, Xia Binbin, Du Pei, Wu Lili, Liang Mifang, Chen Qian, Yan Huan, Gao George F, Wang Qihui, Wang Jun

2021-Nov-12

ACE2, SARS-CoV-2, machine learning

General General

The impact of weather condition and social activity on COVID-19 transmission in the United States.

In Journal of environmental management

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

Zhang Xinxuan, Maggioni Viviana, Houser Paul, Xue Yuan, Mei Yiwen

2021-Nov-11

COVID-19 transmission, Machine learning, Random forest regression model, Social activity factor, Weather condition

General General

Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction.

In Journal of biomedical informatics ; h5-index 55.0

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.

Weinzierl Maxwell A, Harabagiu Sanda M

2021-Nov-17

COVID-19, Machine learning, Natural Language Processing, Social Media, knowledge graph embedding, vaccine misinformation

General General

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.

In General hospital psychiatry

OBJECTIVE : To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHOD : Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission.

RESULTS : Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave.

CONCLUSION : This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Castro Victor M, Hart Kamber L, Sacks Chana A, Murphy Shawn N, Perlis Roy H, McCoy Thomas H

2021-Nov-02

COVID-19, Delirium, Electronic health records, Machine learning, Predictive modeling, Replication

General General

Exploration of SARS-CoV-2 3CLpro Inhibitors by Virtual Screening Methods, FRET Detection, and CPE Assay.

In Journal of chemical information and modeling

COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3',4'-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3',4'-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3',4'-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.

Zhao Jun, Ma Qinhai, Zhang Baoyue, Guo Pengfei, Wang Zhe, Liu Yi, Meng Minsi, Liu Ailin, Yang Zifeng, Du Guanhua

2021-Nov-19