Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.

In Archives of disease in childhood ; h5-index 49.0

OBJECTIVE : The COVID-19 pandemic and subsequent government restrictions have had a major impact on healthcare services and disease transmission, particularly those associated with acute respiratory infection. This study examined non-identifiable routine electronic patient record data from a specialist children's hospital in England, UK, examining the effect of pandemic mitigation measures on seasonal respiratory infection rates compared with forecasts based on open-source, transferable machine learning models.

METHODS : We performed a retrospective longitudinal study of respiratory disorder diagnoses between January 2010 and February 2022. All diagnoses were extracted from routine healthcare activity data and diagnosis rates were calculated for several diagnosis groups. To study changes in diagnoses, seasonal forecast models were fit to prerestriction period data and extrapolated.

RESULTS : Based on 144 704 diagnoses from 31 002 patients, all but two diagnosis groups saw a marked reduction in diagnosis rates during restrictions. We observed 91%, 89%, 72% and 63% reductions in peak diagnoses of 'respiratory syncytial virus', 'influenza', 'acute nasopharyngitis' and 'acute bronchiolitis', respectively. The machine learning predictive model calculated that total diagnoses were reduced by up to 73% (z-score: -26) versus expected during restrictions and increased by up to 27% (z-score: 8) postrestrictions.

CONCLUSIONS : We demonstrate the association between COVID-19 related restrictions and significant reductions in paediatric seasonal respiratory infections. Moreover, while many infection rates have returned to expected levels postrestrictions, others remain supressed or followed atypical winter trends. This study further demonstrates the applicability and efficacy of routine electronic record data and cross-domain time-series forecasting to model, monitor, analyse and address clinically important issues.

Bowyer Stuart A, Bryant William A, Key Daniel, Booth John, Briggs Lydia, Spiridou Anastassia, Cortina-Borja Mario, Davies Gwyneth, Taylor Andrew M, Sebire Neil J

2022-Aug-10

information technology, respiratory

General General

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.

In Biocybernetics and biomedical engineering

Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.

Fang Lingling, Wang Xin

2022-Aug-05

Adaptive region enhancement, COVID-19, Deep learning, Dense block, Mixed dataset

General General

Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.

In International journal of applied earth observation and geoinformation : ITC journal

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

Zhang Yecheng, Zhang Qimin, Zhao Yuxuan, Deng Yunjie, Zheng Hao

2022-Aug

Coronavirus disease, Deep learning, Design improvement, Incidence prediction, Spatial risk factors

General General

Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19.

In Infectious Disease Modelling

With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period and normal times, respectively. We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40-59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60-79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

Matsunaga Nobuaki, Kamata Keisuke, Asai Yusuke, Tsuzuki Shinya, Sakamoto Yasuaki, Ijichi Shinpei, Akiyama Takayuki, Yu Jiefu, Yamada Gen, Terada Mari, Suzuki Setsuko, Suzuki Kumiko, Saito Sho, Hayakawa Kayoko, Ohmagari Norio

2022-Aug-05

COVID-19, Japan, Machine learning, Risk prediction, Severity

General General

Effective hybrid deep learning model for COVID-19 patterns identification using CT images.

In Expert systems

Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over-and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. These three described pre-trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID-19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state-of-the-art studies. Because of its effectiveness in accurately screening COVID-19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals.

Ibrahim Dheyaa Ahmed, Zebari Dilovan Asaad, Mohammed Hussam J, Mohammed Mazin Abed

2022-May-01

COVID‐19 identification, CT scan images, deep learning models, feature fusion

General General

AI bot to detect fake COVID-19 vaccine certificate.

In IET information security

As the world is now fighting against rampant virus COVID-19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non-COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture-based feature extraction for extracting logo, symbol and for the signature we extract Crest-Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1-score, and computation time over state-of-art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201-LBP) outperforms with an accuracy of 0.94.

Arif Muhammad, Shamsudheen Shermin, Ajesh F, Wang Guojun, Chen Jianer

2022-May-11

COVID‐19, artificial intelligence, deep learning, forged certificate, vaccine certificate

Dermatology Dermatology

Partnering with a senior living community to optimise teledermatology via full body skin screening during the COVID-19 pandemic: A pilot programme.

In Skin health and disease

Background : Elderly patients in senior communities faced high barriers to care during the COVID-19 pandemic, including increased vulnerability to COVID-19, long quarantines for clinic visits, and difficulties with telemedicine adoption.

Objective : To pilot a new model of dermatologic care to overcome barriers for senior living communities during the COVID-19 pandemic and assess patient satisfaction.

Methods : From 16 November 2020 to 9 July 2021, this quality improvement programme combined in-residence full body imaging with real-time outlier lesion identification and virtual teledermatology. Residents from the Sequoias Portola Valley Senior Living Retirement Community (Portola Valley, California) voluntarily enroled in the Stanford Skin Scan Programme. Non-physician clinical staff with a recent negative COVID-19 test travelled on-site to obtain in-residence full body photographs using a mobile app-based system on an iPad called SkinIO that leverages deep learning to analyse patient images and suggest suspicious, outlier lesions for dermoscopic photos. A single dermatologist reviewed photographs with the patient and provided recommendations via a video visit. Objective measures included follow-up course and number of skin cancers detected. Subjective findings were obtained through patient experience surveys.

Results : Twenty-seven individuals participated, three skin cancers were identified, with 11 individuals scheduled for a follow up in-person visit and four individuals starting home treatment. Overall, 88% of patients were satisfied with the Skin Scan programme, with 77% likely to recommend the programme to others. 92% of patients agreed that the Skin Scan photographs were representative of their skin. In the context of the COVID-19 pandemic, 100% of patients felt the process was safer or comparable to an in-person visit. Despite overall appreciation for the programme, 31% of patients reported that they would prefer to see dermatologist in-person after the pandemic.

Conclusions : This programme offers a framework for how a hybrid skin scan programme may provide high utility for individuals with barriers to accessing in-person clinics.

Trinh Pavin, Yekrang Kiana, Phung Michelle, Pugliese Silvina, Chang Anne Lynn S, Bailey Elizabeth E, Ko Justin M, Sarin Kavita Y

2022-Jun-27

General General

LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.

In International journal of imaging systems and technology

The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.

Kumar Sachin, Shastri Sourabh, Mahajan Shilpa, Singh Kuljeet, Gupta Surbhi, Rani Rajneesh, Mohan Neeraj, Mansotra Vibhakar

2022-Jun-11

COVID‐19, LiteCovidNet, chest X‐ray, classification, deep neural network

Public Health Public Health

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.

In International journal of imaging systems and technology

Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT-PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time-consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre-trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state-of-the-art segmentation methods was also performed. The experiments were carried out using a fivefold cross-validation technique on a publicly available database containing 100 single-slice CT scans from >40 patients with COVID-19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross-validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

Polat Hasan

2022-Jun-11

COVID‐19, DeepLabV3 +, ResNet, computed tomography, deep learning, segmentation

Public Health Public Health

Progress in COVID research and developments during pandemic.

In View (Beijing, China)

The pandemic respiratory disease COVID-19 has spread over the globe within a small span of time. Generally, there are two important points are being highlighted and considered towards the successful diagnosis and treatment process. The first point includes the reduction of the rate of infections and the next one is the decrease of the death rate. The major threat to public health globally progresses due to the absence of effective medication and widely accepted immunization for the COVID-19. Whereas, understanding of host susceptibility, clinical features, adaptation of COVID-19 to new environments, asymptomatic infection is difficult and challenging. Therefore, a rapid and an exact determination of pathogenic viruses play an important role in deciding treatments and preventing pandemic to save the people's lives. It is urgent to fix a standardized diagnostic approach for detecting the COVID-19. Here, this systematic review describes all the current approaches using for screening and diagnosing the COVID-19 infectious patient. The renaissance in pathogen due to host adaptability and new region, facing creates several obstacles in diagnosis, drug, and vaccine development process. The study shows that adaptation of accurate and affordable diagnostic tools based on candidate biomarkers using sensor and digital medicine technology can deliver effective diagnosis services at the mass level. Better prospects of public health management rely on diagnosis with high specificity and cost-effective manner along with multidisciplinary research, specific policy, and technology adaptation. The proposed healthcare model with defined road map represents effective prognosis system.

Shukla Sudheesh K, Patra Santanu, Das Trupti R, Kumar Dharmesh, Mishra Anshuman, Tiwari Ashutosh

2022-Jul-20

COVID science and technology, COVID‐19 diagnosis, artificial intelligence, corona virus, pandemic years, respiratory tract infection, serological test

General General

Real-time COVID-19 detection over chest x-ray images in edge computing.

In Computational intelligence

Severe Coronavirus Disease 2019 (COVID-19) has been a global pandemic which provokes massive devastation to the society, economy, and culture since January 2020. The pandemic demonstrates the inefficiency of superannuated manual detection approaches and inspires novel approaches that detect COVID-19 by classifying chest x-ray (CXR) images with deep learning technology. Although a wide range of researches about bran-new COVID-19 detection methods that classify CXR images with centralized convolutional neural network (CNN) models have been proposed, the latency, privacy, and cost of information transmission between the data resources and the centralized data center will make the detection inefficient. Hence, in this article, a COVID-19 detection scheme via CXR images classification with a lightweight CNN model called MobileNet in edge computing is proposed to alleviate the computing pressure of centralized data center and ameliorate detection efficiency. Specifically, the general framework is introduced first to manifest the overall arrangement of the computing and information services ecosystem. Then, an unsupervised model DCGAN is employed to make up for the small scale of data set. Moreover, the implementation of the MobileNet for CXR images classification is presented at great length. The specific distribution strategy of MobileNet models is followed. The extensive evaluations of the experiments demonstrate the efficiency and accuracy of the proposed scheme for detecting COVID-19 over CXR images in edge computing.

Xu Weijie, Chen Beijing, Shi Haoyang, Tian Hao, Xu Xiaolong

2022-Apr-30

CNN, COVID‐19, CXR images, edge computing

General General

A deep learning-based approach for diagnosing COVID-19 on chest x-ray images, and a test study with clinical experts.

In Computational intelligence

Pneumonia is among the common symptoms of the virus that causes COVID-19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x-ray (CXR) is a fast, low-cost, and practical method widely used in this field. The fact that different pathogens other than COVID-19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID-19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID-19, and other viral-induced pneumonia. In the classifications performed with five different state-of-art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.

Sevli Onur

2022-May-17

COVID‐19 diagnosis, chest x‐ray analysis, convolutional neural network, pneumonia detection

General General

Covid-19 detection from radiographs by feature-reinforced ensemble learning.

In Concurrency and computation : practice & experience

The coronavirus (Covid-19) epidemic continues to have a negative influence on the global population's well-being and health. Scientists in many fields around the world are working non-stop to find a solution to the prevention of this epidemic. In the field of computer science, this struggle is supported by studies on especially the analysis of X-ray and CT images with artificial intelligence. In this study, two different ensemble learning models, including deep learning and a combination of machine learning methods, are presented for the detection of SARS-CoV-2 infection from X-ray images. The main purpose of this study is to increase the classification ability of Residual Convolutional Neural Network (ResCNN), which is used as a deep learning method, with the assist of machine learning algorithms and extracted features from images. The proposed models were validated on a total of 5228 chest X-ray images categorized as Normal, Pneumonia, and Covid-19. The images in the dataset were sized in four different ways, 32 × 32, 64 × 64, 128 × 128, and 256 × 256, in order to analyze the validity of the proposed models in more detail. These four datasets were partitioned with the 10-fold cross-validation technique and converted into a total of 40 training and test data. Both proposed models use features derived from the ResCNN as the basis and test a certain number of machine learning algorithms with a majority voting technique by dividing them into subsets. In the architecture of the second model, it combines the features extracted from the Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) methods in addition to the features obtained from the ResCNN. It has been seen that the classification ability of both proposed models is better than the ResCNN in the experiments. In particular, the second model gives a similar classification score even though it is tested with images four-times smaller (e.g., 32 × 32 vs. 128 × 128) than those used in the ResCNN. This shows that the model can give ideal results with lower computational cost.

Elen Abdullah

2022-Jul-06

Covid‐19, X‐ray images, convolutional neural network, histogram‐oriented gradients, local binary patterns, machine learning

General General

Computational Scientific Discovery in Psychology.

In Perspectives on psychological science : a journal of the Association for Psychological Science

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

Bartlett Laura K, Pirrone Angelo, Javed Noman, Gobet Fernand

2022-Aug-09

AI, computational scientific discovery, creativity, philosophy of science, psychology

General General

Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19.

In Mathematical biosciences and engineering : MBE

COVID-19 is an infectious disease caused by a newly discovered coronavirus, which has become a worldwide pandemic greatly impacting our daily life and work. A large number of mathematical models, including the susceptible-exposed-infected-removed (SEIR) model and deep learning methods, such as long-short-term-memory (LSTM) and gated recurrent units (GRU)-based methods, have been employed for the analysis and prediction of the COVID-19 outbreak. This paper describes a SEIR-LSTM/GRU algorithm with time-varying parameters that can predict the number of active cases and removed cases in the US. Time-varying reproductive numbers that can illustrate the progress of the epidemic are also produced via this process. The investigation is based on the active cases and total cases data for the USA, as collected from the website "Worldometer". The root mean square error, mean absolute percentage error and r2 score were utilized to assess the model's accuracy.

Feng Lin, Chen Ziren, Jr Harold A Lay, Furati Khaled, Khaliq Abdul

2022-Jun-20

** COVID-19 , GRU , LSTM , SEIR , data-driven , time-varying parameters , time-varying reproduction number **

General General

When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic.

In Governance (Oxford, England)

Public officials use blame avoidance strategies when communicating performance information. While such strategies typically involve shifting blame to political opponents or other governments, we examine how they might direct blame to ethnic groups. We focus on the COVID-19 pandemic, where the Trump administration sought to shift blame by scapegoating (using the term "Chinese virus") and mitigate blame by positively framing performance information on COVID-19 testing. Using a novel experimental design that leverages machine learning techniques, we find scapegoating outgroups backfired, leading to greater blame of political leadership for the poor administrative response, especially among conservatives. Backlash was strongest for negatively framed performance data, demonstrating that performance framing shapes blame avoidance outcomes. We discuss how divisive blame avoidance strategies may alienate even supporters.

Porumbescu Gregory, Moynihan Donald, Anastasopoulos Jason, Olsen Asmus Leth

2022-Jun-03

General General

Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.

In Wiley interdisciplinary reviews. Data mining and knowledge discovery

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.

Dasgupta Abhijit, Bakshi Abhisek, Mukherjee Srijani, Das Kuntal, Talukdar Soumyajeet, Chatterjee Pratyayee, Mondal Sagnik, Das Puspita, Ghosh Subhrojit, Som Archisman, Roy Pritha, Kundu Rima, Sarkar Akash, Biswas Arnab, Paul Karnelia, Basak Sujit, Manna Krishnendu, Saha Chinmay, Mukhopadhyay Satinath, Bhattacharyya Nitai P, De Rajat K

EHR, deep learning, drug affinity, social media, x‐ray/CT/HRCT

Public Health Public Health

Challenges of data sharing in European Covid-19 projects: A learning opportunity for advancing pandemic preparedness and response.

In The Lancet regional health. Europe

The COVID-19 pandemic saw a massive investment into collaborative research projects with a focus on producing data to support public health decisions. We relay our direct experience of four projects funded under the Horizon2020 programme, namely ReCoDID, ORCHESTRA, unCoVer and SYNCHROS. The projects provide insight into the complexities of sharing patient level data from observational cohorts. We focus on compliance with the General Data Protection Regulation (GDPR) and ethics approvals when sharing data across national borders. We discuss procedures for data mapping; submission of new international codes to standards organisation; federated approach; and centralised data curation. Finally, we put forward recommendations for the development of guidelines for the application of GDPR in case of major public health threats; mandatory standards for data collection in funding frameworks; training and capacity building for data owners; cataloguing of international use of metadata standards; and dedicated funding for identified critical areas.

Tacconelli Evelina, Gorska Anna, Carrara Elena, Davis Ruth Joanna, Bonten Marc, Friedrich Alex W, Glasner Corinna, Goossens Herman, Hasenauer Jan, Abad Josep Maria Haro, Peñalvo José L, Sanchez-Niubo Albert, Sialm Anastassja, Scipione Gabriella, Soriano Gloria, Yazdanpanah Yazdan, Vorstenbosch Ellen, Jaenisch Thomas

2022-Oct

Cohort study, Data sharing, General Data Protection Regulation, Machine learning, Pandemic, Preparedeness, SARS-CoV-2

General General

Grayscale image statistics of COVID-19 patient CT scans characterize lung condition with machine and deep learning.

In Chronic diseases and translational medicine

Background : Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.

Method : Five hundred thirteen CT images relating to 57 patients (49 with COVID-19; 8 free of COVID-19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.

Results : The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians' visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians' assessments (99.81% AS accuracy; 1 error from 513 images).

Conclusion : Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

Ghashghaei Sara, Wood David A, Sadatshojaei Erfan, Jalilpoor Mansooreh

2022-May-31

COVID‐19 lung feature recognition, computed tomography analysis, confusion‐matrix analysis, grayscale image attributes, visual versus algorithmic classification

General General

COVID-19 detection using X-ray images and statistical measurements.

In Measurement : journal of the International Measurement Confederation

The COVID-19 pandemic spread all over the world, starting in China in late 2019, and significantly affected life in all aspects. As seen in SARS, MERS, COVID-19 outbreaks, coronaviruses pose a great threat to world health. The COVID-19 epidemic, which caused pandemics all over the world, continues to seriously threaten people's lives. Due to the rapid spread of COVID-19, many countries' healthcare sectors were caught off guard. This situation put a burden on doctors and healthcare professionals that they could not handle. All of the studies on COVID-19 in the literature have been done to help experts to recognize COVID-19 more accurately, to use more accurate diagnosis and appropriate treatment methods. The alleviation of this workload will be possible by developing computer aided early and accurate diagnosis systems with machine learning. Diagnosis and evaluation of pneumonia on computed tomography images provide significant benefits in investigating possible complications and in case follow-up. Pneumonia and lesions occurring in the lungs should be carefully examined as it helps in the diagnostic process during the pandemic period. For this reason, the first diagnosis and medications are very important to prevent the disease from progressing. In this study, a dataset consisting of Pneumonia and Normal images was used by proposing a new image preprocessing process. These preprocessed images were reduced to 15x15 unit size and their features were extracted according to their RGB values. Experimental studies were carried out by performing both normal values and feature reduction among these features. RGB values of the images were used in train and test processes for MLAs. In experimental studies, 5 different Machine Learning Algorithms (MLAs) (Multi Class Support Vector Machine (MC-SVM), k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB)). The following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.746377, 0.963768. Accuracy results in test operations were obtained as follows; 0.87755, 0.857143, 0.857143, 0.877551, 0.938776.

Avuçlu Emre

2022-Sep-30

Biomedical images, COVID-19, Feature extraction, Machine learning algorithms

Public Health Public Health

Changes in blood pressure and related risk factors among nurses working in a negative pressure isolation ward.

In Frontiers in public health

Objective : To observe changes in blood pressure (ΔBP) and explore potential risk factors for high ΔBP among nurses working in a negative pressure isolation ward (NPIW).

Methods : Data from the single-center prospective observational study were used. Based on a routine practice plan, female nurses working in NPIW were scheduled to work for 4 days/week in different shifts, with each day working continuously for either 5 or 6 h. BP was measured when they entered and left NPIW. Multivariable logistic regression was used to assess potential risk factors in relation to ΔBP ≥ 5 mm Hg.

Results : A total of 84 nurses were included in the analysis. The ΔBP was found to fluctuate on different working days; no significant difference in ΔBP was observed between the schedules of 5 and 6 h/day. The standardized score from the self-rating anxiety scale (SAS) was significantly associated with an increased risk of ΔBP ≥ 5 mm Hg (odds ratio [OR] = 1.12, 95% CI: 1.00-1.24). Working 6 h/day (vs. 5 h/day) in NPIW was non-significantly related to decreased risk of ΔBP (OR = 0.70), while ≥ 2 consecutive working days (vs. 1 working day) was non-significantly associated with increased risk of ΔBP (OR = 1.50).

Conclusion : This study revealed no significant trend for ΔBP by working days or working time. Anxiety was found to be significantly associated with increased ΔBP, while no <2 consecutive working days were non-significantly related to ΔBP. These findings may provide some preliminary evidence for BP control in nurses who are working in NPIW for Coronavirus Disease 2019 (COVID-19).

Wang Yaoyao, Tian Junzhang, Qu Hongying, Yu Lingna, Zhang Xiaoqin, Huang Lishan, Zhou Jianqun, Lian Wanmin, Wang Ruoting, Wang Lijun, Li Guowei, Tang Li

2022

COVID-19, blood pressure, negative pressure isolation ward, nurse, risk factors

General General

An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework.

In International journal of imaging systems and technology

COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.

Mannepalli Durga Prasad, Namdeo Varsha

2022-Jul

classification, deep learning, feature extraction, feature selection, optimization, preprocessing

General General

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

In Computational intelligence and neuroscience

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.

Jain Deepak Kumar, Singh Tarishi, Saurabh Praneet, Bisen Dhananjay, Sahu Neeraj, Mishra Jayant, Rahman Habibur

2022

General General

The effect of information-driven resource allocation on the propagation of epidemic with incubation period.

In Nonlinear dynamics

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

Zhu Xuzhen, Liu Yuxin, Wang Xiaochen, Zhang Yuexia, Liu Shengzhi, Ma Jinming

2022-Aug-02

Epidemic spreading, Exposed state, Information-driven resource allocation, Microscopic Markov chain, Multiplex network

Pathology Pathology

Digital pathology - Rising to the challenge.

In Frontiers in medicine

Digital pathology has gone through considerable technical advances during the past few years and certain aspects of digital diagnostics have been widely and swiftly adopted in many centers, catalyzed by the COVID-19 pandemic. However, analysis of requirements, careful planning, and structured implementation should to be considered in order to reap the full benefits of a digital workflow. The aim of this review is to provide a practical, concise and hands-on summary of issues relevant to implementing and developing digital diagnostics in the pathology laboratory. These include important initial considerations, possible approaches to overcome common challenges, potential diagnostic pitfalls, validation and regulatory issues and an introduction to the emerging field of image analysis in routine.

Dawson Heather

2022

artificial intelligence, digital pathology, image analysis, scanner acquisition, validation

General General

A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images.

In IEEE transactions on molecular, biological, and multi-scale communications

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due to the limited data, existing models suffer from incapable feature extraction and poor network convergence and optimization. Accordingly, a multi-stage residual network, MSRCovXNet, is proposed for effective detection of COVID-19 from chest x-ray (CXR) images. As a shallow yet effective classifier with the ResNet-18 as the feature extractor, MSRCovXNet is optimized by fusing two proposed feature enhancement modules (FEM), i.e., low-level and high-level feature maps (LLFMs and HLFMs), which contain respectively more local information and rich semantic information, respectively. For effective fusion of these two features, a single-stage FEM (MSFEM) and a multi-stage FEM (MSFEM) are proposed to enhance the semantic feature representation of the LLFMs and the local feature representation of the HLFMs, respectively. Without ensembling other deep learning models, our MSRCovXNet has a precision of 98.9% and a recall of 94% in detection of COVID-19, which outperforms several state-of-the-art models. When evaluated on the COVIDGR dataset, an average accuracy of 82.2% is achieved, leading other methods by at least 1.2%.

Fang Zhenyu, Ren Jinchang, MacLellan Calum, Li Huihui, Zhao Huimin, Hussain Amir, Fortino Giancarlo

2022-Mar

COVID-19, MSRCovXNet, ResNet-18, chest x-ray imaging, feature enhancement module

Cardiology Cardiology

Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19.

In Frontiers in cardiovascular medicine

Background : As automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms.

Methods : In this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae.

Results : Feasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival.

Conclusion : Automated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.

Pellikka Patricia A, Strom Jordan B, Pajares-Hurtado Gabriel M, Keane Martin G, Khazan Benjamin, Qamruddin Salima, Tutor Austin, Gul Fahad, Peterson Eric, Thamman Ritu, Watson Shivani, Mandale Deepa, Scott Christopher G, Naqvi Tasneem, Woodward Gary M, Hawkes William

2022

COVID-19, artificial intelligence, deformation imaging, echocardiography, machine learning, strain rate imaging

General General

MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans.

In Signal, image and video processing

In the field of diagnosis and treatment planning of Coronavirus disease 2019 (COVID-19), accurate infected area segmentation is challenging due to the significant variations in the COVID-19 lesion size, shape, and position, boundary ambiguity, as well as complex structure. To bridge these gaps, this study presents a robust deep learning model based on a novel multi-scale contextual information fusion strategy, called Multi-Level Context Attentional Feature Fusion (MLCA2F), which consists of the Multi-Scale Context-Attention Network (MSCA-Net) blocks for segmenting COVID-19 lesions from Computed Tomography (CT) images. Unlike the previous classical deep learning models, the MSCA-Net integrates Multi-Scale Contextual Feature Fusion (MC2F) and Multi-Context Attentional Feature (MCAF) to learn more lesion details and guide the model to estimate the position of the boundary of infected regions, respectively. Practically, extensive experiments are performed on the Kaggle CT dataset to explore the optimal structure of MLCA2F. In comparison with the current state-of-the-art methods, the experiments show that the proposed methodology provides efficient results. Therefore, we can conclude that the MLCA2F framework has the potential to dramatically improve the conventional segmentation methods for assisting clinical decision-making.

Bakkouri Ibtissam, Afdel Karim

2022-Aug-03

COVID-19 pneumonia, Context attentional features, Contextual information, Multi-level fusion, Multi-scale features, Segmentation

General General

A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles

bioRxiv Preprint

Monkeypox has emerged as a fast-spreading disease around the world and an outbreak has been reported in 75 countries so far. Although the clinical attributes of Monkeypox are similar to those of Smallpox, skin lesions and rashes caused by Monkeypox often resemble those of other types of pox, for example, chickenpox and cowpox. This scenario makes an early diagnosis of Monkeypox challenging for the healthcare professional just by observing the visual appearance of lesions and rashes. The rarity of Monkeypox before the current outbreak further created a knowledge gap among healthcare professionals around the world. To tackle this challenging situation, scientists are taking motivation from the success of supervised machine learning in COVID-19 detection. However, the lack of Monkeypox skin image data is making the bottleneck of using machine learning in Monkeypox detection from patient skin images. Therefore, in this project, we introduce the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. We used web-scraping to collect Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles infected skin as well as healthy skin images to build a comprehensive image database and made it publicly available. We believe that our database will facilitate the development of baseline machine learning algorithms for early detection of Monkeypox in clinical settings. Our dataset is available at the following Kaggle link: https://www.kaggle.com/datasets/arafathussain/monkeypox-skin-image-dataset-2022.

Islam, T.; Hussain, M. A.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-09

General General

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.

In Signal processing. Image communication

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

Chi Jianning, Zhang Shuang, Han Xiaoying, Wang Huan, Wu Chengdong, Yu Xiaosheng

2022-Aug-02

COVID-19, CT image, Convolutional neural networks, Deep learning, Infection segmentation

General General

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

bioRxiv Preprint

An outbreak of Monkeypox has been reported in 75 countries so far, and it is spreading in fast pace around the world. The clinical attributes of Monkeypox resemble those of Smallpox, while skin lesions and rashes of Monkeypox often resemble those of other poxes, for example, Chickenpox and Cowpox. These similarities make Monkeypox detection challenging for healthcare professionals by examining the visual appearance of lesions and rashes. Additionally, there is a knowledge gap among healthcare professionals due to the rarity of Monkeypox before the current outbreak. Motivated by the success of artificial intelligence (AI) in COVID-19 detection, the scientific community has shown an increasing interest in using AI in Monkeypox detection from digital skin images. However, the lack of Monkeypox skin image data has been the bottleneck of using AI in Monkeypox detection. Therefore, recently, we introduced the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. In addition, in this paper, we utilize this dataset to study the feasibility of using state-of-the-art AI deep models on skin images for Monkeypox detection. Our study found that deep AI models have great potential in the detection of Monkeypox from digital skin images (precision of 85%). However, achieving a more robust detection power requires larger training samples to train those deep models.

Islam, T.; Hussain, M. A.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-09

General General

Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images.

In Multimedia tools and applications

The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million people globally. For its early diagnosis, researchers consider chest X-ray examinations as a standard screening technique in addition to RT-PCR test. Majority of research work till date focused only on application of deep learning approaches that is relevant but lacking in better pre-processing of CXR images. Towards this direction, this study aims to explore cumulative effects of image denoising and enhancement approaches on the performance of deep learning approaches. Regarding pre-processing, suitable methods for X-ray images, Histogram equalization, CLAHE and gamma correction have been tested individually and along with adaptive median filter, median filter, total variation filter and gaussian denoising filters. Proposed study compared eleven combinations in exploration of most coherent approach in greedy manner. For more robust analysis, we compared ten CNN architectures for performance evaluation with and without enhancement approaches. These models are InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, Vgg19, NASNetMobile, ResNet101, DenseNet121, DenseNet169, DenseNet201. These models are trained in 4-way (COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal) and 3-way classification scenario (COVID-19 vs Pneumonia vs Normal) on two benchmark datasets. The proposed methodology determines with TVF + Gamma, models achieve higher classification accuracy and sensitivity. In 4-way classification MobileNet with TVF + Gamma achieves top accuracy of 93.25% with 1.91% improvement in accuracy score, COVID-19 sensitivity of 98.72% and F1-score of 92.14%. In 3-way classification our DenseNet201 with TVF + Gamma gains accuracy of 91.10% with improvement of 1.47%, COVID-19 sensitivity of 100% and F1-score of 91.09%. Proposed study concludes that deep learning modes with gamma correction and TVF + Gamma has superior performance compared to state-of-the-art models. This not only minimizes overlapping between COVID-19 and virus pneumonia but advantageous in time required to converge best possible results.

Sharma Ajay, Mishra Pramod Kumar

2022-Aug-01

COVID-19 analysis, Chest X-ray, Deep learning, Image denoising, Image enhancement, Pneumonia classification

Public Health Public Health

Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models.

In Technological forecasting and social change

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

Middya Asif Iqbal, Roy Sarbani

2022-Oct

Covid-19, Deep learning, Spatio-temporal variation

General General

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.

In Structural chemistry

The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Niranjan Vidya, Setlur Anagha Shamsundar, Karunakaran Chandrashekar, Uttarkar Akshay, Kumar Kalavathi Murugan, Skariyachan Sinosh

2022-Aug-03

COVID-19, Computational sciences, Drug repurposing, Protein targets, SARS-CoV-2, Variants of concern

General General

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic.

In Applied soft computing

Quantifying and analysing excess mortality in crises such as the ongoing COVID-19 pandemic is crucial for policymakers. Traditional measures fail to take into account differences in the level, long-term secular trends, and seasonal patterns in all-cause mortality across countries and regions. This paper develops and empirically investigates the forecasting performance of a novel, flexible and dynamic ensemble learning strategy for the seasonal time series forecasting of monthly respiratory disease death data across a pool of 61 heterogeneous countries. The strategy is based on a Bayesian model ensemble (BME) of heterogeneous time series methods involving both the selection of the subset of best forecasters (model confidence set), the identification of the best holdout period for each contributed model, and the determination of optimal weights using out-of-sample predictive accuracy. A model selection strategy is also developed to remove the outlier models and to combine the models with reasonable accuracy in the ensemble. The empirical outcomes of this large set of experiments show that the accuracy of the BME approach is noticeably improved when selecting a flexible and dynamic holdout period. Additionally, the BME forecasts of respiratory disease deaths for each country are highly accurate and exhibit a high correlation (94%) with COVID-19 deaths in 2020.

Ashofteh Afshin, Bravo Jorge M, Ayuso Mercedes

2022-Aug-01

Ensemble Bayesian model averaging (EBMA), Ensemble learning, Forecasting, Healthcare, Layered learning, Machine learning, Multiple learning processes, Respiratory disease deaths, SARS-CoV-2, Time series

General General

A novel deep fusion strategy for COVID-19 prediction using multimodality approach.

In Computers & electrical engineering : an international journal

Over the last two years, the novel coronavirus has become a significant threat to the health of the public, and numerous approaches are developed to determine the symptoms of COVID-19. To deal with the complex symptoms of COVID-19, a Deep Learning-assisted Multi-modal Data Analysis (DMDA) approach is introduced to determine COVID-19 symptoms by utilizing acoustic and image-based data. Furthermore, the classified events are forwarded to the proposed Dynamic Fusion Strategy (DFS) for confirming the health status of the individual. Initially, the performance of the proposed solution is evaluated on both acoustic and image-based samples and the proposed solution attains the maximum accuracy of 96.88% and 98.76%, respectively. Similarly, the DFS has achieved an overall symptom determination accuracy of 98.72% which is highly acceptable for decision-making. Moreover, the proposed solution shows high reliability with an accuracy of 95.64% even in absence of any one of the data modalities during testing.

Manocha Ankush, Bhatia Munish

2022-Aug-03

Covid-19, Deep learning, Semi-supervised learning, Smart healthcare, Smart monitoring

General General

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.

In Health services research and managerial epidemiology

Introduction : The federal government legislated supplemental funding to support community health centers (CHCs) in response to the COVID-19 pandemic. Supplemental funding included standard base payments and adjustments for the number of total and uninsured patients served before the pandemic. However, not all CHCs share similar patient population characteristics and health risks.

Objective : To use machine learning to identify the most important factors for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 during the first year of the pandemic.

Methods : Our analytic sample included data from 1342 CHCs across the 50 states and D.C. in 2020. We trained a random forest (RF) classifier model, incorporating 5-fold cross-validation to validate the RF model while optimizing the model's hyperparameters. Final performance metrics were calculated following the application of the model that had the best fit to the held-out test set.

Results : CHCs with a high burden of COVID-19 had an average of 65.3 patients diagnosed with COVID-19 per 1000 patients in 2020. Our RF model had 80.9% accuracy, 80.1% precision, 25.0% sensitivity, and 98.1% specificity. The percentage of Hispanic patients served in 2020 was the most important feature for predicting whether CHCs had high COVID-19 burden.

Conclusions : Findings from our RF model suggest patient population race and ethnicity characteristics were most important for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 in 2020, though sensitivity was low. Enhanced support for CHCs serving large Hispanic patient populations may have an impact on addressing future COVID-19 waves.

Goldstein Evan V, Wilson Fernando A

COVID-19, community health, community health centers, health promotion, prevention, primary care

Public Health Public Health

Developing a Digital Technology System to Address COVID-19 Health Needs in Guatemala: A Scientific Diaspora Case Study.

In Frontiers in research metrics and analytics

Scientific diasporas are organized groups of professionals who work together to contribute to their country of origin. Since the start of the COVID-19 pandemic in 2020, scientific diasporas around the world have focused their efforts to support the public health response in their countries of origin. As the first cases of COVID-19 were reported in Guatemala in March of 2020, a team of four Guatemalan nationals, residing abroad and in-country, started collaborating to tackle COVID-19 misinformation and issues with healthcare services navigation. Their collaboration was facilitated by FUNDEGUA, a Guatemalan nonprofit, which provided a legal framework to establish partnerships and fundraise. The team created a digital technological system called ALMA (Asistente de Logística Médica Automatizada in Spanish). A female character named ALMA was created to personify the digital information services, through social media profiles, an interactive website, a free national multilingual call center, and an artificial intelligence-based chatbot. More members joined the nascent interdisciplinary diaspora through professional/personal references or social media. ALMA provided a platform for Guatemalan nationals to contribute with their skillset to their country during a global crisis through flexible schedules and short- or long-term involvement. As the team grew, the services for query resolution and information dissemination expanded as well. The ALMA initiative shows that scientific diasporas can provide an avenue for professionals to contribute to Guatemala, regardless of their residence and job commitments.

Alvarado Juan Roberto, Lainfiesta Ximena, Paniagua-Avila Alejandra, Asturias Gabriela

2022

COVID-19 pandemic, Guatemala, brain circulation, capacity building, chatbot, scientific diasporas, technology

General General

COVID-19: Machine learning for safe transportation.

In Concurrency and computation : practice & experience

Entire world has been affected by Covid-19 pandemic. In fighting against the Covid-19, social distancing and face mask have a paramount role in freezing the spread of the disease. People are asked to limit their interactions with each other, to reduce the spread of the disease. Here an alert system has to be maintained to caution people traveling in vehicles. Our proposed solution will work primarily on computer vision. The video stream is captured using a camera. Footage is processed using single shot detector algorithm for face mask detection. Second, YOLOv3 object detection algorithm is used to detect if social distancing is maintained or not inside the vehicle. If passengers do not follow the safety rules such as wearing a mask at any point of the time in the whole journey, alarm/alert is given via buzzer/speaker. This ensures that people abide by the safety rules without affecting their daily norms of transportation. It also helps the government to keep the situation under control.

Sankari Subbiah, Varshini Subramaniam Sankaran, Aafia Shifana Savvas Mohamed

2022-Aug-30

COVID‐19, YOLOv3, computer vision, face mask, single shot detector, social distancing

General General

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

bioRxiv Preprint

An outbreak of Monkeypox has been reported in 75 countries so far, and it is spreading in fast pace around the world. The clinical attributes of Monkeypox resemble those of Smallpox, while skin lesions and rashes of Monkeypox often resemble those of other poxes, for example, Chickenpox and Cowpox. These similarities make Monkeypox detection challenging for healthcare professionals by examining the visual appearance of lesions and rashes. Additionally, there is a knowledge gap among healthcare professionals due to the rarity of Monkeypox before the current outbreak. Motivated by the success of artificial intelligence (AI) in COVID-19 detection, the scientific community has shown an increasing interest in using AI in Monkeypox detection from digital skin images. However, the lack of Monkeypox skin image data has been the bottleneck of using AI in Monkeypox detection. Therefore, recently, we introduced the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. In addition, in this paper, we utilize this dataset to study the feasibility of using state-of-the-art AI deep models on skin images for Monkeypox detection. Our study found that deep AI models have great potential in the detection of Monkeypox from digital skin images (precision of 85%). However, achieving a more robust detection power requires larger training samples to train those deep models.

Hussain, M. A.; Islam, T.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-08

oncology Oncology

Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning.

In Molecular & cellular proteomics : MCP

The recent surge of COVID-19 hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of 100s of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and non-survivors. With increasing length of hospitalization, the survivors' samples showed a trend towards normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of ten proteins and five metabolites we could predict patient survival with 92% accuracy (AUC 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

Richard Vincent R, Gaither Claudia, Popp Robert, Chaplygina Daria, Brzhozovskiy Alexander, Kononikhin Alexey, Mohammed Yassene, Zahedi René P, Nikolaev Evgeny N, Borchers Christoph H

2022-Aug-03

General General

Answering hospital caregivers' questions at any time: proof of concept of an artificial intelligence-based chatbot in a French hospital.

In JMIR human factors

BACKGROUND : Access to accurate information in health is a key point for caregivers to avoid medication errors, especially with the reorganization of staff and drugs circuits during health crises such as COVID 19. It is therefore the role of the hospital pharmacy to answer caregivers' questions. Some may require the expertise of a pharmacist, some should be answered by pharmacy technicians, but others are simple and redundant, and automated responses may be given.

OBJECTIVE : We aimed at developing and implementing a chatbot to answer questions from hospital caregivers, 24 hours a day, about drugs and pharmacy organization, and evaluated this tool.

METHODS : The ADDIE model: Analysis, Design, Development, Implementation, Evaluation, was used by a multi-professional team composed of 3 hospital pharmacists, 2 members of the Innovation and Transformation Department, and the Information Technology (IT) service provider. Based on an analysis of the caregivers' needs about drugs and pharmacy organization, we designed and developed a chatbot. The tool was then evaluated before the implementation into the hospital intranet. Its relevance and conversations with testers were monitored via the IT provider's back office.

RESULTS : Needs analysis with 5 hospital pharmacists and 33 caregivers from 5 health services allowed us to identify 7 themes about drugs and pharmacy organization (such as opening hours and specific prescriptions). After a year of chatbot design and development, the test version obtained good evaluation scores: its speed was rated 8.2/10, usability 8.1/10, and appearance 7.5/10. Testers were generally satisfied (70%) and were hoping for the content to be enhanced.

CONCLUSIONS : The chatbot seems to be a relevant tool for hospital caregivers, helping them to get reliable and verified information they need on drugs and pharmacy organization. In the context of significant mobility of nursing staff during the health crisis due to COVID-19, the chatbot could be a suitable tool for transmitting relevant information related to drugs circuits or specific procedures. To our knowledge, this is the first time that such a tool has been designed for caregivers. Its development further continued by means of tests conducted with other users such as pharmacy technicians, and via the integration of additional data, before the implementation on the two hospital sites.

CLINICALTRIAL :

Daniel Thomas, de Chevigny Alix, Champrigaud Adeline, Valette Julie, Sitbon Marine, Jardin Meryam, Chevalier Delphine, Renet Sophie

2022-Aug-02

Public Health Public Health

Predictors of invasive mechanical ventilation in hospitalized COVID-19 patients: a retrospective study from Jordan.

In Expert review of respiratory medicine

OBJECTIVES : To identify early indicators for invasive mechanical ventilation utilization among COVID-19 patients.

METHODS : This retrospective study evaluated COVID-19 patients who were admitted to hospital from September 20, 2020, to August 8, 2021. Patients' clinical characteristics, demographics, comorbidities, and laboratory results were evaluated. Multivariable logistic regression and machine learning (ML) methods were employed to assess variable significance.

RESULTS : Among 1,613 confirmed COVID-19 patients, 365 patients (22.6%) received invasive mechanical ventilation (IMV). Factors associated with IMV included older age >65 years (OR,1.46; 95%CI, 1.13 - 1.89), current smoking status (OR, 1.71; 95%CI, 1.22-2.41), critical disease at admission (OR, 1.97; 95%CI, 1.28-3.03), and chronic kidney disease (OR, 2.07; 95%CI, 1.37-3.13). Laboratory abnormalities that were associated with increased risk for IMV included high leukocyte count (OR, 2.19; 95%CI, 1.68 - 2.87), low albumin (OR, 1.76; 95%CI, 1.33 - 2.34) and high AST (OR, 1.71; 95%CI, 1.31 - 2.22).

CONCLUSION : Our study suggests that there are several factors associated with the increased need for IMV among COVID-19 patients including older age, current smoking status, critical disease status on admission, and chronic kidney disease. In addition, laboratory markers such as high leukocyte count, low albumin and high AST were determined. These findings will help in early identification of patients at high risk for IMV and reallocation of hospital resources towards patients who need them the most to improve their outcomes.

Kabbaha Suad, Al-Azzam Sayer, Karasneh Reema A, Khassawneh Basheer Y, Al-Mistarehi Abdel-Hameed, Lattyak William J, Aldiab Motasem, Hasan Syed Shahzad, Conway Barbara R, Aldeyab Mamoon A

2022-Aug-05

COVID-19, Comorbidity, Invasive Mechanical Ventilation, Laboratory, Predictor, Risk Factor, SARS‐CoV‐2, Severity

General General

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis.

In Computational and mathematical methods in medicine

As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms. In the battle against the virus, however, researchers and decision-makers must contend with an ever-increasing volume of data, referred to as "big data." VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine-tune our model on lung ultrasound frames from healthy people as well as from patients with COVID-19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non-COVID-19. COVID-19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID-19.

Sangeetha S K B, Kumar M Sandeep, K Deeba, Rajadurai Hariharan, Maheshwari V, Dalu Gemmachis Teshite

2022

General General

COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology.

In Frontiers in public health

At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.

Nayan Nazrul Anuar, Jie Yi Choon, Suboh Mohd Zubir, Mazlan Nur-Fadhilah, Periyasamy Petrick, Abdul Rahim Muhammad Yusuf Zawir, Shah Shamsul Azhar

2022

COVID-19, diagnostic, machine learning, non-invasive, photoplethysmogram, prediction

Ophthalmology Ophthalmology

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.

In Frontiers in molecular biosciences

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Li Hao, Huang Feiming, Liao Huiping, Li Zhandong, Feng Kaiyan, Huang Tao, Cai Yu-Dong

2022

COVID-19, classification algorithm, feature selection, immune cell, machine learning

Public Health Public Health

Artificial Intelligence in Accelerating Drug Discovery and Development.

In Recent patents on biotechnology

Drug discovery and development are critical processes that enable the treatment of a wide variety of health-related problems. These are time-consuming, tedious, complicated, and costly processes. Numerous difficulties arise throughout the entire process of drug discovery, from design to testing. Corona Virus Disease 2019 (COVID-19) recently posed a significant threat to global public health. SARS-Cov-2 and its variants are rapidly spreading in humans due to their high transmission rate. To effectively treat COVID-19, potential drugs and vaccines must be developed quickly. The advancement of artificial intelligence has shifted the focus of drug development away from traditional methods and toward bioinformatics tools. Computer-aided drug design techniques have demonstrated tremendous utility in dealing with massive amounts of biological data and developing efficient algorithms. Artificial intelligence enables more effective approaches to complex problems associated with drug discovery and development through the use of machine learning. Artificial intelligence-based technologies improve the pharmaceutical industry's ability to discover effective drugs. This review summarizes significant challenges encountered during the drug discovery and development processes, as well as the applications of artificial intelligence-based methods to overcome those obstacles in order to provide effective solutions to health problems. This may provide additional insight into the mechanism of action, resulting in the development of vaccines and potent substitutes for repurposed drugs that can be used to treat not only COVID-19 but also other ailments.

Tripathi Anushree, Misra Krishna, Dhanuka Richa, Singh Jyoti Prakash

2022-Aug-02

Artificial Intelligence (AI), Bioinformatics, COVID-19, Drug design, Machine Learning (ML)., Pharmaceutical applications

Radiology Radiology

Primary SARS-CoV-2 Pneumonia Screening in Adults: Analysis of the Correlation between High-Resolution Computed Tomography Pulmonary Patterns and Initial Oxygen Saturation Levels.

In Current medical imaging

BACKGROUND : Chest high-resolution computed tomography (HRCT) is mandatory for patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and a high respiratory rate (RR) because sublobar consolidation is the likely pathological pattern in addition to ground glass opacities (GGOs).

OBJECTIVE : The present study determined the correlation between the percentage extent of typical pulmonary lesions on HRCT, as a representation of severity, and the RR and peripheral oxygen saturation level (SpO2), as measured through pulse oximetry, in patients with reverse transcriptase polymerase chain reaction (RT-PCR)-confirmed primary (noncomplicated) SARS-CoV-2 pneumonia.

METHODS : The present retrospective study was conducted in 332 adult patients who presented withzdyspnea and hypoxemia and were admitted to Prince Mohammed bin Abdulaziz Hospital, Riyadh, Saudi Arabia between May 15, 2020 and December 15, 2020. All the patients underwent chest HRCT. Of the total, 198 patients with primary noncomplicated SARS-CoV-2 pneumonia were finally selected based on the typical chest HRCT patterns. The main CT patterns, GGO and sublobar consolidation, were individually quantified as a percentage of the total pulmonary involvement through algebraic summation of the percentage of the 19 pulmonary segments affected. Additionally, the statistical correlation strength between the total percentage pulmonary involvement and the age, initial RR, and percentage SpO2 of the patients was determined.

RESULTS : The mean ± standard deviation (SD) age of the 198 patients was 48.9 ± 11.4 years. GGO magnitude alone exhibited a significant weak positive correlation with patients' age (r = 0.2; p = 0.04). Sublobar consolidation extent exhibited a relatively stronger positive correlation with RR than GGO magnitude (r = 0.23; p = 0.002). A relatively stronger negative correlation was observed between the GGO extent and SpO2 (r = - 0.38; p = 0.002) than that between sublobar consolidation and SpO2 (r = - 0.2; p = 0.04). An increase in the correlation strength was demonstrated with increased case segregation with GGO extent (r = - 0.34; p = 0.01).

CONCLUSION : The correlation between the magnitudes of typical pulmonary lesion patterns, particularly GGO, which exhibited an incremental correlation pattern on chest HRCT, and the SpO2 percentage, may allow the establishment of an artificial intelligence program to differentiate primary SARS-CoV-2 pneumonia from other complications and associated pathology influencing SpO2.

Alonazi Batil, Mostafa Mohamed A, Farghaly Ahmed M, Zindani Salah A, Al-Watban Jehad A, Altaimi Feras, Almotairy Abdulrahim S, Fagiry Moram A, Mahmoud Mustafa Z

2022-Aug-02

Artificial intelligence, chest high-resolution computed tomography, ground glass opacities, primary SARS-CoV-2 pneumonia, respiratory rate

General General

Deep learning based COVID-19 detection using medical images: Is insufficient data handled well?

In Current medical imaging

The deep learning is a prominent method for automatic detection of COVID-19 disease using medical dataset. This paper aims to give the perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study on the available datasets comprising CT and X-ray images are presented in this paper, which can be very much useful in the context of deep learning framework for COVID-19 detection. Moreover, various data handling techniques that are very essential in deep learning models are discussed in detail. Advanced data handling techniques and approaches to modify deep learning models are suggested to handle the data insufficiency problem in deep learning based COVID-19 detection.

Babu Caren, O Rahul Manohar, Chandy D Abraham

2022-Aug-03

COVID-19, CT dataset, Chest X-ray dataset, data augmentation, deep learning, transfer learning.

Public Health Public Health

Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health.

In American journal of public health ; h5-index 90.0

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. Published online ahead of print August 4, 2022:e1-e10. https://doi.org/10.2105/AJPH.2022.306917).

Thorpe Lorna E, Chunara Rumi, Roberts Tim, Pantaleo Nicholas, Irvine Caleb, Conderino Sarah, Li Yuruo, Hsieh Pei Yang, Gourevitch Marc N, Levine Shoshanna, Ofrane Rebecca, Spoer Benjamin

2022-Aug-04

Public Health Public Health

Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic.

In Disaster medicine and public health preparedness

OBJECTIVE : Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day.

METHODS : Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.

RESULTS : 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO's pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.

CONCLUSIONS : AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

Martin Nina M, Poirier Lisa, Rosenblum Andrew J, Reznar Melissa M, Gittelsohn Joel, Barnett Daniel J

2022-Aug-04

Food Security, Machine Learning, Natural Language Processing, Topic Modeling, Twitter

General General

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

In Frontiers in immunology ; h5-index 100.0

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Liu Jun, Li Zhi-Bin, Lu Qi-Qi, Yu Yi, Zhang Shan-Qiang, Ke Pei-Feng, Zhang Fan, Li Ji-Cheng

2022

COVID-19, UPLC-MS/MS, machine learning, potential biomarkers, widely targeted metabolites

General General

Argument mining as rapid screening tool of COVID-19 literature quality: Preliminary evidence.

In Frontiers in public health

Background : The COVID-19 pandemic prompted the scientific community to share timely evidence, also in the form of pre-printed papers, not peer reviewed yet.

Purpose : To develop an artificial intelligence system for the analysis of the scientific literature by leveraging on recent developments in the field of Argument Mining.

Methodology : Scientific quality criteria were borrowed from two selected Cochrane systematic reviews. Four independent reviewers gave a blind evaluation on a 1-5 scale to 40 papers for each review. These scores were matched with the automatic analysis performed by an AM system named MARGOT, which detected claims and supporting evidence for the cited papers. Outcomes were evaluated with inter-rater indices (Cohen's Kappa, Krippendorff's Alpha, s* statistics).

Results : MARGOT performs differently on the two selected Cochrane reviews: the inter-rater indices show a fair-to-moderate agreement of the most relevant MARGOT metrics both with Cochrane and the skilled interval scores, with larger values for one of the two reviews.

Discussion and conclusions : The noted discrepancy could rely on a limitation of the MARGOT system that can be improved; yet, the level of agreement between human reviewers also suggests a different complexity between the two reviews in debating controversial arguments. These preliminary results encourage to expand and deepen the investigation to other topics and a larger number of highly specialized reviewers, to reduce uncertainty in the evaluation process, thus supporting the retraining of AM systems.

Brambilla Gianfranco, Rosi Antonella, Antici Francesco, Galassi Andrea, Giansanti Daniele, Magurano Fabio, Ruggeri Federico, Torroni Paolo, Cisbani Evaristo, Lippi Marco

2022

COVID-19, argument mining, artificial intelligence, inter-rater agreement, scientific literature quality assessment

Radiology Radiology

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.

In Radiology. Artificial intelligence

Purpose : To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs.

Materials and Methods : A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists.

Results : Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both).

Conclusion : AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.

Sun Ju, Peng Le, Li Taihui, Adila Dyah, Zaiman Zach, Melton-Meaux Genevieve B, Ingraham Nicholas E, Murray Eric, Boley Daniel, Switzer Sean, Burns John L, Huang Kun, Allen Tadashi, Steenburg Scott D, Gichoya Judy Wawira, Kummerfeld Erich, Tignanelli Christopher J

2022-Jul

Application Domain, Classification, Diagnosis, Infection, Lung

General General

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways.

In medRxiv : the preprint server for health sciences

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10 -14 ), Alzheimer's disease (FDR ≤ 5.46×10 -10 ) and coronary artery disease (FDR ≤ 4.64×10 -2 ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Wang Lihua, Western Dan, Timsina Jigyasha, Repaci Charlie, Song Won-Min, Norton Joanne, Kohlfeld Pat, Budde John, Climer Sharlee, Butt Omar H, Jacobson Daniel, Garvin Michael, Templeton Alan R, Campagna Shawn, O’Halloran Jane, Presti Rachel, Goss Charles W, Mudd Philip A, Ances Beau M, Zhang Bin, Sung Yun Ju, Cruchaga Carlos

2022-Jul-25

oncology Oncology

The Impact of COVID-19 on Physician-Scientist Trainees and Faculty in the United States: A National Survey.

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

PURPOSE : Physician-scientists have long been considered an endangered species, and their extended training pathway is vulnerable to disruptions. This study investigated the effects of COVID-19-related challenges on the personal lives, career activities, stress levels, and research productivity of physician-scientist trainees and faculty.

METHOD : The authors surveyed medical students (MS), graduate students (GS), residents/fellows (R/F), and faculty (F) using a tool distributed to 120 U.S. institutions with MD-PhD programs in April-June 2020. Chi-squared and Fisher's exact tests were used to compare differences between groups. Machine learning was employed to select variables for multivariate logistic regression analyses aimed at identifying factors associated with stress and impaired productivity.

RESULTS : The analyses included 1,929 respondents (MS: n = 679, 35%; GS: n = 676, 35%; R/F: n = 274, 14%; F: n = 300, 16%). All cohorts reported high levels of social isolation, stress from effects of the pandemic, and negative impacts on productivity. R/F and F respondents were more likely than MS and GS respondents to report financial difficulties due to COVID-19. R/F and F respondents with a dual degree expressed more impaired productivity compared to those without a dual degree. Multivariate regression analyses identified impacted research/scholarly activities, financial difficulties, and social isolation as predictors of stress and impaired productivity for both MS and GS cohorts. For both R/F and F cohorts, impacted personal life and research productivity were associated with stress, while dual-degree status, impacted research/scholarly activities, and impacted personal life were predictors of impaired productivity. More female than male respondents reported increased demands at home.

CONCLUSIONS : This national survey of physician-scientist trainees and faculty found a high incidence of stress and impaired productivity related to the COVID-19 pandemic. Understanding the challenges faced and their consequences may improve efforts to support the physician-scientist workforce in the post-pandemic period.

Kwan Jennifer M, Noch Evan, Qiu Yuqing, Toubat Omar, Christophers Briana, Azzopardi Stephanie, Gilmer Gabrielle, Wiedmeier Julia Erin, Daye Dania

2022-Aug-02

General General

Meta-analysis of the microbial biomarkers in the gut - lung crosstalk in COVID-19, community acquired pneumonia and Clostridium difficile infections.

In Letters in applied microbiology

Respiratory infections are the leading causes of mortality and the current pandemic COVID-19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unraveling the correlations and interplay of the human microbiota in the gut- lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID-19 infected gut, COVID-19 infected lungs, Clostridium difficile infected gut and community acquired pneumonia infected lungs. The metagenome datasets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID-19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community acquired pneumonia whereas abundance of Faecalibacterium and Clostridium are associated with the Clostridium difficile infections in gut. A machine learning random forest classifier applied to the datasets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut lung axis during dysbiosis of two anatomically different organs.

Aishwarya S, Gunasekaran K

2022-Aug-03

diversity, gut - lung axis, interplay, microbiota, random forest classifier

General General

A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.

In Applied soft computing

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

Attallah Omneya, Samir Ahmed

2022-Jul-29

COVID-19, Computed tomography (CT), Convolutional neural networks, Deep learning, Discrete wavelet transform (DWT), ResNet

General General

A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles

bioRxiv Preprint

Monkeypox has emerged as a fast-spreading disease around the world and an outbreak has been reported in 42 countries so far. Although the clinical attributes of Monkeypox are similar to that of Smallpox, skin lesions and rashes caused by Monkeypox often resemble that of other pox types, e.g., Chickenpox and Cowpox. This scenario makes an early diagnosis of Monkeypox challenging for the healthcare professional just by observing the visual appearance of lesions and rashes. The rarity of Monkeypox before the current outbreak further created a knowledge gap among healthcare professionals around the world. To tackle this challenging situation, scientists are taking motivation from the success of supervised machine learning in COVID-19 detection. However, the lack of Monkeypox skin image data is making the bottleneck of using machine learning in Monkeypox detection from skin images of patients. Therefore, in this project, we introduce the Monkeypox Skin Image Dataset (MSID), the largest of its kind so far. We used web-scrapping to collect Monkeypox, Chickenpox, Smallpox, Cowpox and Measles infected skin as well as healthy skin images to build a comprehensive image database and made it publicly available. We believe that our database will facilitate the development of baseline machine learning algorithms for early Monkeypox detection in clinical settings. Our dataset is available at the following Kaggle link: https://www.kaggle.com/datasets/arafathussain/monkeypox-skin-image-dataset-2022.

Islam, T.; Hussain, M. A.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-04

General General

Better understanding the behavior of air pollutants at shutdown times - results of a short full lockdown.

In International journal of environmental health research

Numerous studies have evaluated the effects of lockdowns during the COVID-19 pandemic, but most of them have concerned large cities and regions. This study aimed to evaluate the dynamics of air pollutants during and after the implementation of a short lockdown in the medium-sized city of Pelotas, Brazil, using hourly measurements of pollutants. The evaluation period included in this study was between August 9th and 12th, 2020. A machine learning model was used to investigate the expected behavior against what was observed during the study period. All pollutants presented a gradual reduction until a dynamic plateau established 48 hours after the start of the lockdown: NO2 (↓4%), O3 (↓34%), SO2 (↓24%), CO (↓48%), PM10 (↓82%) and PM2.5 (↓82%). At the end of the restriction measures, the PM10 and PM2.5 levels continued to decline beyond expectations. Our findings show that these measures can positively affect the air quality in medium-sized cities.

Tavella Ronan Adler, El Koury Santos Jéssica, de Moura Fernando Rafael, da Silva Júnior Flávio Manoel Rodrigues

2022-Aug-02

Air pollution, Brazil, COVID-19, air quality

General General

Vec4Cred: a model for health misinformation detection in web pages.

In Multimedia tools and applications

Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.

Upadhyay Rishabh, Pasi Gabriella, Viviani Marco

2022-Jul-28

Consumer health, Deep learning, Health misinformation, Machine learning, Natural language processing

Pathology Pathology

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays.

In BioMed research international ; h5-index 102.0

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

Badr Malek, Al-Otaibi Shaha, Alturki Nazik, Abir Tanvir

2022

General General

Real-time assessment of the Ganga river during pandemic COVID-19 and predictive data modeling by machine learning.

In International journal of environmental science and technology : IJEST

** : In this study, four water quality parameters were reviewed at 14 stations of river Ganga in pre-, during and post-lockdown and these parameters were modeled by using different machine learning algorithms. Various mathematical models were used for the computation of water quality parameters in pre-, during and post- lockdown period by using Central Pollution Control Board real-time data. Lockdown resulted in the reduction of Biochemical Oxygen Demand ranging from 55 to 92% with increased concentration of dissolved oxygen at few stations. pH was in range of 6.5-8.5 of during lockdown. Total coliform count declined during lockdown period at some stations. The modeling of oxygen saturation deficit showed supremacy of Thomas Mueller model (R 2 = 0.75) during lockdown over Streeter Phelps (R 2 = 0.57). Polynomial regression and Newton's Divided Difference model predicted possible values of water quality parameters till 30th June, 2020 and 07th August, 2020, respectively. It was found that predicted and real values were close to each other. Genetic algorithm was used to optimize hyperparameters of algorithms like Support Vector Regression and Radical Basis Function Neural Network, which were then employed for prediction of all examined water quality metrics. Computed values from ANN model were found close to the experimental ones (R 2 = 1). Support Vector Regression-Genetic Algorithm Hybrid proved to be very effective for accurate prediction of pH, Biochemical Oxygen Demand, Dissolved Oxygen and Total coliform count during lockdown.

Supplementary Information : The online version contains supplementary material available at 10.1007/s13762-022-04423-1.

Singh J, Swaroop S, Sharma P, Mishra V

2022-Jul-27

Artificial neural network, Biochemical oxygen demand, Dissolved oxygen, Modeling, The Ganga, Total Coliform Count, pH

Surgery Surgery

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.

In Knowledge-based systems

Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centred on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.

Costantini Giovanni, Cesarini Valerio, Robotti Carlo, Benazzo Marco, Pietrantonio Filomena, Di Girolamo Stefano, Pisani Antonio, Canzi Pietro, Mauramati Simone, Bertino Giulia, Cassaniti Irene, Baldanti Fausto, Saggio Giovanni

2022-Jul-28

1E, Vowel /e/ vocal task, 2S, Sentence vocal task, 3C, Cough vocal task, Adaboost, CFS, Correlation-based Feature Selection, CNN, Convolutional Neural Network, COVID-19, Classification, DL, Deep Learning, Deep learning, H, Healthy control subjects, MFCC, Mel-frequency Cepstral Coefficients, ML, Machine Learning, NS, Nasal Swab, P, Positive subjects, PCR, Polymerase Chain Reaction-based molecular swabs, PvsH, Positive versus Healthy subjects comparison, R, Recovered subjects, RF, Random Forest, ROC, Receiver-Operating Curve, ReLu, Rectified Linear Unit, RvsH, Recovered versus Healthy subjects comparison, SVM, Support Vector Machine, Speech processing

General General

Improving performance of classifiers for diagnosis of critical diseases to prevent COVID risk.

In Computers & electrical engineering : an international journal

The risk of developing COVID-19 and its variants may be higher in those with pre-existing health conditions such as thyroid disease, Hepatitis C Virus (HCV), breast tissue disease, chronic dermatitis, and other severe infections. Early and precise identification of these disorders is critical. A huge number of patients in nations like India require early and rapid testing as a preventative measure. The problem of imbalance arises from the skewed nature of data in which the instances from majority class are classified correct, while the minority class is unfortunately misclassified by many classifiers. When it comes to human life, this kind of misclassification is unacceptable. To solve the misclassification issue and improve accuracy in such datasets, we applied a variety of data balancing techniques to several machine learning algorithms. The outcomes are encouraging, with a considerable increase in accuracy. As an outcome of these proper diagnoses, we can make plans and take the required actions to stop patients from acquiring serious health issues or viral infections.

Kumar Vinod, Lalotra Gotam Singh, Kumar Ravi Kant

2022-Sep

COVID-19, Class balancing techniques, Clinical dataset, Machine learning, Multi-class classification

General General

Prediction, scanning and designing of TNF-α inducing epitopes for human and mouse

bioRxiv Preprint

Tumor Necrosis Factor alpha (TNF-) is a pleiotropic pro-inflammatory cytokine that plays a crucial role in controlling signaling pathways within the immune cells. Recent studies reported that the higher expression levels of TNF- is associated with the progression of several diseases including cancers, cytokine release syndrome in COVID-19 and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF- progression in various disease conditions. In the pilot study, we have proposed a host-specific in-silico tool for the prediction, designing and scanning of TNF- inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF- inducing/non-inducing for human and mouse hosts. Firstly, we developed alignment free (machine learning based models using composition of peptides) methods for predicting TNF- inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, alignment based (using BLAST) method has been used for predicting TNF- inducing epitopes. Finally, a hybrid method (combination of alignment free and alignment-based method) has been developed for predicting epitopes. Our hybrid method achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified the potential TNF- inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2 and human insulin. Best models developed in this study has been incorporated in a webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

Dhall, A.; Patiyal, S.; Choudhury, S.; Jain, S.; Narang, K.; Raghava, G. P. S.

2022-08-03

General General

Recognition of Freezing of Gait in Parkinson's Disease Based on Machine Vision.

In Frontiers in aging neuroscience ; h5-index 64.0

Background : Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.

Objective : A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.

Methods : In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.

Results : We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.

Conclusion : A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.

Li Wendan, Chen Xiujun, Zhang Jintao, Lu Jianjun, Zhang Chencheng, Bai Hongmin, Liang Junchao, Wang Jiajia, Du Hanqiang, Xue Gaici, Ling Yun, Ren Kang, Zou Weishen, Chen Cheng, Li Mengyan, Chen Zhonglue, Zou Haiqiang

2022

Parkinson’s disease, XGBoost, freezing of gait, machine learning, machine vision

General General

Sentiment analysis of COVID-19 social media data through machine learning.

In Multimedia tools and applications

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries' economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

Dangi Dharmendra, Dixit Dheeraj K, Bhagat Amit

2022-Jul-25

COVID-19, Decision tree, Logistic regression, Multinomial Naïve Bayes, Random forest, Support vector machine

General General

Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review.

In SN computer science

COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

Siddiqui Shah, Arifeen Murshedul, Hopgood Adrian, Good Alice, Gegov Alexander, Hossain Elias, Rahman Wahidur, Hossain Shazzad, Al Jannat Sabila, Ferdous Rezowan, Masum Shamsul

2022

Computed tomography (CT) images, Coronavirus (COVID-19), Deep learning (DL), Machine learning (ML), RT-PCR, X-ray images

General General

Polarized Citizen Preferences for the Ethical Allocation of Scarce Medical Resources in 20 Countries.

In MDM policy & practice

** : Objective. When medical resources are scarce, clinicians must make difficult triage decisions. When these decisions affect public trust and morale, as was the case during the COVID-19 pandemic, experts will benefit from knowing which triage metrics have citizen support. Design. We conducted an online survey in 20 countries, comparing support for 5 common metrics (prognosis, age, quality of life, past and future contribution as a health care worker) to a benchmark consisting of support for 2 no-triage mechanisms (first-come-first-served and random allocation). Results. We surveyed nationally representative samples of 1000 citizens in each of Brazil, France, Japan, and the United States and also self-selected samples from 20 countries (total N = 7599) obtained through a citizen science website (the Moral Machine). We computed the support for each metric by comparing its usability to the usability of the 2 no-triage mechanisms. We further analyzed the polarizing nature of each metric by considering its usability among participants who had a preference for no triage. In all countries, preferences were polarized, with the 2 largest groups preferring either no triage or extensive triage using all metrics. Prognosis was the least controversial metric. There was little support for giving priority to healthcare workers. Conclusions. It will be difficult to define triage guidelines that elicit public trust and approval. Given the importance of prognosis in triage protocols, it is reassuring that it is the least controversial metric. Experts will need to prepare strong arguments for other metrics if they wish to preserve public trust and morale during health crises.

Highlights : We collected citizen preferences regarding triage decisions about scarce medical resources from 20 countries.We find that citizen preferences are universally polarized.Citizens either prefer no triage (random allocation or first-come-first served) or extensive triage using all common triage metrics, with "prognosis" being the least controversial.Experts will need to prepare strong arguments to preserve or elicit public trust in triage decisions.

Awad Edmond, Bago Bence, Bonnefon Jean-François, Christakis Nicholas A, Rahwan Iyad, Shariff Azim

cross-cultural study, medical ethics, triage preferences

General General

Product pricing solutions using hybrid machine learning algorithm.

In Innovations in systems and software engineering

E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes-where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms.

Namburu Anupama, Selvaraj Prabha, Varsha M

2022-Jul-25

CatBoost, Ensemble algorithms, Product pricing, X-NGBoost, XGBoost

General General

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

In Signal, image and video processing

** : Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters.

Supplementary Information : The online version contains supplementary material available at 10.1007/s11760-022-02302-3.

Rajamani Kumar T, Rani Priya, Siebert Hanna, ElagiriRamalingam Rajkumar, Heinrich Mattias P

2022-Jul-25

Attention mechanism, Attention-augmented convolution, COVID-19, Consolidation, Ground-glass opacities, Segmentation, U-Net

General General

Deep Content Information Retrieval for COVID-19 Detection from Chromatic CT Scans.

In Arabian journal for science and engineering

In this paper, we investigate the role of the chromatic information in CT scans in COVID-19 detection and we aim to confirm the inclusion of the artificial intelligence findings in assisting COVID-19 diagnosis. This paper proposes a freezing-based convolutional neural network learning using a morphological transformation of CT images to classify COVID-19 cohorts to help in prognostication pneumonia disease monitoring. The experiments made on the collected CT images from previous works have proven to be a powerful aid to recognize the lesions in CT images which works at comprehensively greater accuracy and speed. The proposed CNN architecture has reflected the viral proliferation in infected patients and archives an accuracy of 87.56% with an improvement by 3% compared to the baseline method on the available database of CT images.

Sassi Ameni, Ouarda Wael, Amar Chokri Ben

2022-Jul-22

COVID-19, CT scans, Chromatic information, Convolutional neural network, Dilation, Erosion, Image retrieval

General General

Geographic microtargeting of social assistance with high-resolution poverty maps.

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

Hundreds of millions of poor families receive some form of targeted social assistance. Many of these antipoverty programs involve some degree of geographic targeting, where aid is prioritized to the poorest regions of the country. However, policy makers in many low-resource settings lack the disaggregated poverty data required to make effective geographic targeting decisions. Using several independent datasets from Nigeria, this paper shows that high-resolution poverty maps, constructed by applying machine learning algorithms to satellite imagery and other nontraditional geospatial data, can improve the targeting of government cash transfers to poor families. Specifically, we find that geographic targeting relying on machine learning-based poverty maps can reduce errors of exclusion and inclusion relative to geographic targeting based on recent nationally representative survey data. This result holds for antipoverty programs that target both the poor and the extreme poor and for initiatives of varying sizes. We also find no evidence that machine learning-based maps increase targeting disparities by demographic groups, such as gender or religion. Based in part on these findings, the Government of Nigeria used this approach to geographically target emergency cash transfers in response to the COVID-19 pandemic.

Smythe Isabella S, Blumenstock Joshua E

2022-Aug-09

Nigeria, poverty, satellite imagery, targeting

Internal Medicine Internal Medicine

Gastrointestinal sequalae months after severe acute respiratory syndrome corona virus 2 infection: a prospective, observational study.

In European journal of gastroenterology & hepatology

INTRODUCTION : Post-coronavirus disease (post-COVID) symptoms arise mostly from impaired function of respiratory tract although in many patients, the dysfunction of gastrointestinal tract and liver among other organ systems may persist.

METHODS : Primary data collection was based on a short gastrointestinal symptom questionnaire at the initial screening. A brief telephone survey within the patient and control group was performed 5-8 months after the initial screening. R ver. 4.0.5 and imbalanced RandomForest (RF) machine-learning algorithm were used for data explorations and analyses.

RESULTS : A total of 590 patients were included in the study. The general presence of gastrointestinal symptoms 208.2 days (153-230 days) after the initial acute severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection was 19% in patients with moderate-to-serious course of the disease and 7.3% in patients with mild course compared with 3.0% in SARS-CoV-2 negative controls (P < 0.001). Diarrhea and abdominal pain are the most prevalent post-COVID gastrointestinal symptoms. RF machine-learning algorithm identified acute diarrhea and antibiotics administration as the strongest predictors for gastrointestinal sequelae with area under curve of 0.68. Variable importance for acute diarrhea is 0.066 and 0.058 for antibiotics administration.

CONCLUSION : The presence of gastrointestinal sequelae 7 months after the initial SARS-CoV-2 infection is significantly higher in patients with moderate-to-severe course of the acute COVID-19 compared with asymptomatic patients or those with mild course of the disease. The most prevalent post-COVID gastrointestinal symptoms are diarrhea and abdominal pain. The strongest predictors for persistence of these symptoms are antibiotics administration and acute diarrhea during the initial infection.

Liptak Peter, Duricek Martin, Rosolanka Robert, Ziacikova Ivana, Kocan Ivan, Uhrik Peter, Grendar Marian, Hrnciarova Martina, Bucova Patricia, Galo David, Banovcin Peter

2022-Sep-01

Internal Medicine Internal Medicine

Gastrointestinal sequalae months after severe acute respiratory syndrome corona virus 2 infection: a prospective, observational study.

In European journal of gastroenterology & hepatology

INTRODUCTION : Post-coronavirus disease (post-COVID) symptoms arise mostly from impaired function of respiratory tract although in many patients, the dysfunction of gastrointestinal tract and liver among other organ systems may persist.

METHODS : Primary data collection was based on a short gastrointestinal symptom questionnaire at the initial screening. A brief telephone survey within the patient and control group was performed 5-8 months after the initial screening. R ver. 4.0.5 and imbalanced RandomForest (RF) machine-learning algorithm were used for data explorations and analyses.

RESULTS : A total of 590 patients were included in the study. The general presence of gastrointestinal symptoms 208.2 days (153-230 days) after the initial acute severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection was 19% in patients with moderate-to-serious course of the disease and 7.3% in patients with mild course compared with 3.0% in SARS-CoV-2 negative controls (P < 0.001). Diarrhea and abdominal pain are the most prevalent post-COVID gastrointestinal symptoms. RF machine-learning algorithm identified acute diarrhea and antibiotics administration as the strongest predictors for gastrointestinal sequelae with area under curve of 0.68. Variable importance for acute diarrhea is 0.066 and 0.058 for antibiotics administration.

CONCLUSION : The presence of gastrointestinal sequelae 7 months after the initial SARS-CoV-2 infection is significantly higher in patients with moderate-to-severe course of the acute COVID-19 compared with asymptomatic patients or those with mild course of the disease. The most prevalent post-COVID gastrointestinal symptoms are diarrhea and abdominal pain. The strongest predictors for persistence of these symptoms are antibiotics administration and acute diarrhea during the initial infection.

Liptak Peter, Duricek Martin, Rosolanka Robert, Ziacikova Ivana, Kocan Ivan, Uhrik Peter, Grendar Marian, Hrnciarova Martina, Bucova Patricia, Galo David, Banovcin Peter

2022-Jul-28

Cardiology Cardiology

COVID-19 telehealth preparedness: a cross-sectional assessment of cardiology practices in the USA.

In Personalized medicine

Aim: The COVID-19 pandemic forced medical practices to augment healthcare delivery to remote and virtual services. We describe the results of a nationwide survey of cardiovascular professionals regarding telehealth perspectives. Materials & methods: A 31-question survey was sent early in the pandemic to assess the impact of COVID-19 on telehealth adoption & reimbursement. Results: A total of 342 clinicians across 42 states participated. 77% were using telehealth, with the majority initiating usage 2 months after the COVID-19 shutdown. A variety of video-based systems were used. Telehealth integration requirements differed, with electronic medical record integration being mandated in more urban than rural practices (70 vs 59%; p < 0.005). Many implementation barriers surfaced, with over 75% of respondents emphasizing reimbursement uncertainty and concerns for telehealth generalizability given the complexity of cardiovascular diseases. Conclusion: Substantial variation exists in telehealth practices. Further studies and legislation are needed to improve access, reimbursement and the quality of telehealth-based cardiovascular care.

Waldman Carly E, Min Jean H, Wassif Heba, Freeman Andrew M, Rzeszut Anne K, Reilly Jack, Theriot Paul, Soliman Ahmed M, Thamman Ritu, Bhatt Ami, Bhavnani Sanjeev P

2022-Aug-01

COVID-19, access to care, electronic medical record, healthcare access, pandemic, payment parity, reimbursement, telehealth, telemedicine, video-visitations

General General

Association of COVID-19 with New-Onset Alzheimer's Disease.

In Journal of Alzheimer's disease : JAD

An infectious etiology of Alzheimer's disease has been postulated for decades. It remains unknown whether SARS-CoV-2 viral infection is associated with increased risk for Alzheimer's disease. In this retrospective cohort study of 6,245,282 older adults (age ≥65 years) who had medical encounters between 2/2020-5/2021, we show that people with COVID-19 were at significantly increased risk for new diagnosis of Alzheimer's disease within 360 days after the initial COVID-19 diagnosis (hazard ratio or HR:1.69, 95% CI: 1.53-1.72), especially in people age ≥85 years and in women. Our findings call for research to understand the underlying mechanisms and for continuous surveillance of long-term impacts of COVID-19 on Alzheimer's disease.

Wang Lindsey, Davis Pamela B, Volkow Nora D, Berger Nathan A, Kaelber David C, Xu Rong

2022-Jul-29

Alzheimer’s disease, COVID-19, electronic health records, viral etiology

General General

Real-time internet of medical things framework for early detection of Covid-19.

In Neural computing & applications

The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

Yildirim Emre, Cicioğlu Murtaza, Çalhan Ali

2022-Jul-24

Apache spark, Covid-19 diagnosis, Ensemble learning, Machine learning, Real-time analytics

General General

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.

In BMC medical imaging

BACKGROUND : Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module.

METHODS : Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers.

RESULTS : The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%).

CONCLUSIONS : MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.

Wang Wei, Jiang Yongbin, Wang Xin, Zhang Peng, Li Ji

2022-Jul-30

COVID-19, Chest X-Ray images, Convolutional neural network (CNN), Deep learning, MLES-Net

Cardiology Cardiology

Unsupervised machine learning demonstrates the prognostic value of TAPSE/PASP ratio among hospitalized patients with COVID-19.

In Echocardiography (Mount Kisco, N.Y.)

BACKGROUND : The ratio of tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP) is a validated index of right ventricular-pulmonary arterial (RV-PA) coupling with prognostic value. We determined the predictive value of TAPSE/PASP ratio and adverse clinical outcomes in hospitalized patients with COVID-19.

METHODS : Two hundred and twenty-nine consecutive hospitalized racially/ethnically diverse adults (≥18 years of age) admitted with COVID-19 between March and June 2020 with clinically indicated transthoracic echocardiograms (TTE) that included adequate tricuspid regurgitation (TR) velocities for calculation of PASP were studied. The exposure of interest was impaired RV-PA coupling as assessed by TAPSE/PASP ratio. The primary outcome was in-hospital mortality. Secondary endpoints comprised of ICU admission, incident acute respiratory distress syndrome (ARDS), and systolic heart failure.

RESULTS : One hundred and seventy-six patients had both technically adequate TAPSE measurements and measurable TR velocities for analysis. After adjustment for age, sex, BMI, race/ethnicity, diabetes mellitus, and smoking status, log(TAPSE/PASP) had a significantly inverse association with ICU admission (p = 0.015) and death (p = 0.038). ROC analysis showed the optimal cutoff for TAPSE/PASP for death was 0.51 mm mmHg-1 (AUC = 0.68). Unsupervised machine learning identified two groups of echocardiographic function. Of all echocardiographic measures included, TAPSE/PASP ratio was the most significant in predicting in-hospital mortality, further supporting its significance in this cohort.

CONCLUSION : Impaired RV-PA coupling, assessed noninvasively via the TAPSE/PASP ratio, was predictive of need for ICU level care and in-hospital mortality in hospitalized patients with COVID-19 suggesting utility of TAPSE/PASP in identification of poor clinical outcomes in this population both by traditional statistical and unsupervised machine learning based methods.

Jani Vivek, Kapoor Karan, Meyer Joseph, Lu Jim, Goerlich Erin, Metkus Thomas S, Madrazo Jose A, Michos Erin, Wu Katherine, Bavaro Nicole, Kutty Shelby, Hays Allison G, Mukherjee Monica

2022-Jul-30

COVID-19, echocardiography, right ventricular failure

Public Health Public Health

Within-host dynamics of SARS-CoV-2 infection: a systematic review and meta-analysis.

In Transboundary and emerging diseases ; h5-index 40.0

Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between December 1, 2019 and February 10, 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response, or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared, and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/mL), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/mL). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/mL]-1 day-1 ) and 0.92 (95% CI:-0.09, 1.93) day-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p<0.01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions. This article is protected by copyright. All rights reserved.

Du Zhanwei, Wang Shuqi, Bai Yuan, Gao Chao, Lau Eric H Y, Cowling Benjamin J

2022-Jul-30

COVID-19, Review, SARS-CoV-2, Viral dynamic parameters, Within-host model

Pathology Pathology

Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.

In PloS one ; h5-index 176.0

The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

Rashidi Hooman H, Pepper John, Howard Taylor, Klein Karina, May Larissa, Albahra Samer, Phinney Brett, Salemi Michelle R, Tran Nam K

2022

Surgery Surgery

Rapid prediction of in-hospital mortality among adults with COVID-19 disease.

In PloS one ; h5-index 176.0

BACKGROUND : We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission.

METHODS : This retrospective study included 13,190 racially and ethnically diverse adults admitted to one of the New York City Health + Hospitals (NYC H+H) system for COVID-19 illness between March 1 and June 30, 2020. Demographic characteristics, simple vital signs and routine clinical laboratory tests were collected from the electronic medical records. A clinical prediction model to estimate the risk of dying during the hospitalization were developed.

RESULTS : Mean age (interquartile range) was 58 (45-72) years; 5421 (41%) were women, 5258 were Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 were (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables-oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine-that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5-1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5-98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/.

CONCLUSIONS : In a diverse population of hospitalized patients with COVID-19 illness, a clinical prediction model using a few readily available vital signs reflecting the severity of disease may precisely predict in-hospital mortality in diverse populations and can rapidly assist decisions to prioritize admissions and intensive care.

Kim Kyoung Min, Evans Daniel S, Jacobson Jessica, Jiang Xiaqing, Browner Warren, Cummings Steven R

2022

oncology Oncology

A Stress and Pain Self-management mHealth App for Adult Outpatients With Sickle Cell Disease: Protocol for a Randomized Controlled Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : This paper describes the research protocol for a randomized controlled trial of a self-management intervention for adults diagnosed with sickle cell disease (SCD). People living with SCD experience lifelong recurrent episodes of acute and chronic pain, which are exacerbated by stress.

OBJECTIVE : This study aims to decrease stress and improve SCD pain control with reduced opioid use through an intervention with self-management relaxation exercises, named You Cope, We Support (YCWS). Building on our previous findings from formative studies, this study is designed to test the efficacy of YCWS on stress intensity, pain intensity, and opioid use in adults with SCD.

METHODS : A randomized controlled trial of the short-term (8 weeks) and long-term (6 months) effects of YCWS on stress, pain, and opioid use will be conducted with 170 adults with SCD. Patients will be randomized based on 1:1 ratio (stratified on pain intensity [≤5 or >5]) to be either in the experimental (self-monitoring of outcomes, alerts or reminders, and use of YCWS [relaxation and distraction exercises and support]) or control (self-monitoring of outcomes and alerts or reminders) group. Patients will be asked to report outcomes daily. During weeks 1 to 8, patients in both groups will receive system-generated alerts or reminders via phone call, text, or email to facilitate data entry (both groups) and intervention use support (experimental). If the participant does not enter data after 24 hours, the study support staff will contact them for data entry troubleshooting (both groups) and YCWS use (experimental). We will time stamp and track patients' web-based activities to understand the study context and conduct exit interviews on the acceptability of system-generated and staff support. This study was approved by our institutional review board.

RESULTS : This study was funded by the National Institute of Nursing Research of the National Institutes of Health in 2020. The study began in March 2021 and will be completed in June 2025. As of April 2022, we have enrolled 45.9% (78/170) of patients. We will analyze the data using mixed effects regression models (short term and long term) to account for the repeated measurements over time and use machine learning to construct and evaluate prediction models. Owing to the COVID-19 pandemic, the study was modified to allow for mail-in consent process, internet-based consent process via email or Zoom videoconference, devices delivered by FedEx, and training via Zoom videoconference.

CONCLUSIONS : We expect the intervention group to report reductions in pain intensity (primary outcome; 0-10 scale) and in stress intensity (0-10 scale) and opioid use (Wisepill event medication monitoring system), which are secondary outcomes. Our study will contribute to advancing the use of nonopioid therapy such as guided relaxation and distraction techniques for managing SCD pain.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04484272; https://clinicaltrials.gov/ct2/show/NCT04484272.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : PRR1-10.2196/33818.

Ezenwa Miriam O, Yao Yingwei, Mandernach Molly W, Fedele David A, Lucero Robert J, Corless Inge, Dyal Brenda W, Belkin Mary H, Rohatgi Abhinav, Wilkie Diana J

2022-Jul-29

analgesics, intervention, opioid use, pain, protocol, randomized controlled trial, self-management, sickle cell disease, stress, support

General General

Providing Care Beyond Therapy Sessions With a Natural Language Processing-Based Recommender System That Identifies Cancer Patients Who Experience Psychosocial Challenges and Provides Self-care Support: Pilot Study.

In JMIR cancer

BACKGROUND : The negative psychosocial impacts of cancer diagnoses and treatments are well documented. Virtual care has become an essential mode of care delivery during the COVID-19 pandemic, and online support groups (OSGs) have been shown to improve accessibility to psychosocial and supportive care. de Souza Institute offers CancerChatCanada, a therapist-led OSG service where sessions are monitored by an artificial intelligence-based co-facilitator (AICF). The AICF is equipped with a recommender system that uses natural language processing to tailor online resources to patients according to their psychosocial needs.

OBJECTIVE : We aimed to outline the development protocol and evaluate the AICF on its precision and recall in recommending resources to cancer OSG members.

METHODS : Human input informed the design and evaluation of the AICF on its ability to (1) appropriately identify keywords indicating a psychosocial concern and (2) recommend the most appropriate online resource to the OSG member expressing each concern. Three rounds of human evaluation and algorithm improvement were performed iteratively.

RESULTS : We evaluated 7190 outputs and achieved a precision of 0.797, a recall of 0.981, and an F1 score of 0.880 by the third round of evaluation. Resources were recommended to 48 patients, and 25 (52%) accessed at least one resource. Of those who accessed the resources, 19 (75%) found them useful.

CONCLUSIONS : The preliminary findings suggest that the AICF can help provide tailored support for cancer OSG members with high precision, recall, and satisfaction. The AICF has undergone rigorous human evaluation, and the results provide much-needed evidence, while outlining potential strengths and weaknesses for future applications in supportive care.

Leung Yvonne W, Park Bomi, Heo Rachel, Adikari Achini, Chackochan Suja, Wong Jiahui, Alie Elyse, Gancarz Mathew, Kacala Martyna, Hirst Graeme, de Silva Daswin, French Leon, Bender Jacqueline, Mishna Faye, Gratzer David, Alahakoon Damminda, Esplen Mary Jane

2022-Jul-29

artificial intelligence, natural language processing, online support groups, recommender system, supportive care in cancer

Public Health Public Health

Dynamics of Respiratory Infectious Diseases in Incarcerated and Free-Living Populations: A Simulation Modeling Study.

In Medical decision making : an international journal of the Society for Medical Decision Making

BACKGROUND : Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern.

METHODS : We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics.

RESULTS : We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite.

CONCLUSIONS : We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly.

HIGHLIGHTS : We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.

Weyant Christopher, Lee Serin, Andrews Jason R, Alarid-Escudero Fernando, Goldhaber-Fiebert Jeremy D

2022-Jul-29

COVID-19, correctional facility, infectious diseases, meta-model

General General

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

In Frontiers in genetics ; h5-index 62.0

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Hu Yue, Rehawi Ghalia, Moyon Lambert, Gerstner Nathalie, Ogris Christoph, Knauer-Arloth Janine, Bittner Florian, Marsico Annalisa, Mueller Nikola S

2022

COVID-19, machine learning, multi-omic integration, network embedding, network inference, polygenic risk score (PRS)

General General

A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients.

In PloS one ; h5-index 176.0

INTRODUCTION : Identifying COVID-19 patients that are most likely to progress to a severe infection is crucial for optimizing care management and increasing the likelihood of survival. This study presents a machine learning model that predicts severe cases of COVID-19, defined as the presence of Acute Respiratory Distress Syndrome (ARDS) and highlights the different risk factors that play a significant role in disease progression.

METHODS : A cohort composed of 289,351 patients diagnosed with COVID-19 in April 2020 was created using US administrative claims data from Oct 2015 to Jul 2020. For each patient, information about 817 diagnoses, were collected from the medical history ahead of COVID-19 infection. The primary outcome of the study was the presence of ARDS in the 4 months following COVID-19 infection. The study cohort was randomly split into training set used for model development, test set for model evaluation and validation set for real-world performance estimation.

RESULTS : We analyzed three machine learning classifiers to predict the presence of ARDS. Among the algorithms considered, a Gradient Boosting Decision Tree had the highest performance with an AUC of 0.695 (95% CI, 0.679-0.709) and an AUPRC of 0.0730 (95% CI, 0.0676 - 0.0823), showing a 40% performance increase in performance against a baseline classifier. A panel of five clinicians was also used to compare the predictive ability of the model to that of clinical experts. The comparison indicated that our model is on par or outperforms predictions made by the clinicians, both in terms of precision and recall.

CONCLUSION : This study presents a machine learning model that uses patient claims history to predict ARDS. The risk factors used by the model to perform its predictions have been extensively linked to the severity of the COVID-19 in the specialized literature. The most contributing diagnosis can be easily retrieved in the patient clinical history and can be used for an early screening of infected patients. Overall, the proposed model could be a promising tool to deploy in a healthcare setting to facilitate and optimize the care of COVID-19 patients.

Lazzarini Nicola, Filippoupolitis Avgoustinos, Manzione Pedro, Eleftherohorinou Hariklia

2022

Radiology Radiology

A comparison of Covid-19 early detection between convolutional neural networks and radiologists.

In Insights into imaging

BACKGROUND : The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.

METHODS : The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx.

RESULTS : Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx.

CONCLUSION : The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.

Albiol Alberto, Albiol Francisco, Paredes Roberto, Plasencia-Martínez Juana María, Blanco Barrio Ana, Santos José M García, Tortajada Salvador, González Montaño Victoria M, Rodríguez Godoy Clara E, Fernández Gómez Saray, Oliver-Garcia Elena, de la Iglesia Vayá María, Márquez Pérez Francisca L, Rayo Madrid Juan I

2022-Jul-28

Covid-19, Deep learning, Radiology

General General

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis.

In medRxiv : the preprint server for health sciences

Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.

Sambarey Awanti, Smith Kirk, Chung Carolina, Arora Harkirat Singh, Yang Zhenhua, Agarwal Prachi, Chandrasekaran Sriram

2022-Jul-21

General General

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier.

In Digital health

Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including 'not survived', 'recovered', and 'not recovered' based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.

Saad Eysha, Sadiq Saima, Jamil Ramish, Rustam Furqan, Mehmood Arif, Choi Gyu Sang, Ashraf Imran

COVID-19, adverse reactions, machine learning, post-vaccination symptoms

General General

Medical students' intention to integrate digital health into their medical practice: A pre-peri COVID-19 survey study in Canada.

In Digital health

Objective : We aimed to explore the factors that influence medical students' intention to integrate dHealth technologies in their practice and analyze the influence of the COVID-19 pandemic on their perceptions and intention.

Methods : We conducted a two-phased survey study at the University of Montreal's medical school in Canada. The study population consisted of 1367 medical students. The survey questionnaire was administered in two phases, that is, an initial survey (t0) in February 2020, before the Covid-19 pandemic, and a replication survey (t1) in January 2021, during the pandemic. Component-based structural equation modeling (SEM) was used to test seven research hypotheses.

Results : A total of 184 students responded to the survey at t0 (13%), whereas 138 responded to the survey at t1 (10%). Findings reveal that students, especially those who are in their preclinical years, had little occasion to experiment with dHealth technologies during their degree. This lack of exposure may explain why a vast majority felt that dHealth should be integrated into medical education. Most respondents declared an intention to integrate dHealth, including AI-based tools, into their future medical practice. One of the most salient differences observed between t0 and t1 brings telemedicine to the forefront of medical education. SEM results confirm the explanatory power of the proposed research model.

Conclusions : The present study unveils the specific dHealth technologies that could be integrated into existing medical curricula. Formal training would increase students' competencies with these technologies which, in turn, could ease their adoption and effective use in their practice.

Paré Guy, Raymond Louis, Pomey Marie-Pascale, Grégoire Geneviève, Castonguay Alexandre, Ouimet Antoine Grenier

COVID-19, Digital health, artificial intelligence, eHealth, medical education, medical practice, survey

Radiology Radiology

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.

In International journal of molecular sciences ; h5-index 102.0

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted &gt;50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

Flora James, Khan Wasiq, Jin Jennifer, Jin Daniel, Hussain Abir, Dajani Khalil, Khan Bilal

2022-Jul-26

COVID-19, VAERS, adverse events, association rule mining, bipartite graphs, hierarchical clustering, self-organizing maps, vaccine analysis workflow, vaccine development

General General

Bibliometric Analysis of Health Technology Research: 1990~2020.

In International journal of environmental research and public health ; h5-index 73.0

This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016-2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.

Luo Xiaomei, Wu Yuduo, Niu Lina, Huang Lucheng

2022-Jul-25

Citespace, VOSviewer, bibliometrics, emerging research topic, healthy technology, research frontier

General General

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.

In International journal of environmental research and public health ; h5-index 73.0

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Gianquintieri Lorenzo, Brovelli Maria Antonia, Pagliosa Andrea, Dassi Gabriele, Brambilla Piero Maria, Bonora Rodolfo, Sechi Giuseppe Maria, Caiani Enrico Gianluca

2022-Jul-25

COVID-19, emergency medical services, geo-AI, geographic information system, health geomatics, machine learning, resources management, spatial filtering

Radiology Radiology

Deep learning for understanding multilabel imbalanced Chest X-ray datasets

ArXiv Preprint

Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as "multilabel classification problems". A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make the decision.

Helena Liz, Javier Huertas-Tato, Manuel Sánchez-Montañés, Javier Del Ser, David Camacho

2022-07-28

Public Health Public Health

DTLMV2-A real-time deep transfer learning mask classifier for overcrowded spaces.

In Applied soft computing

Through the commencement of the COVID-19 pandemic, the whole globe is in disarray and debating on unique approaches to stop this viral transmission. Masks are being worn by people all around the world as one of the preventative measures to avoid contracting this sickness. Although some people are following and adopting this precaution, others are not, despite official recommendations from the administration and public health organisations has been announced. In this paper DTLMV2 (Deep Transfer Learning MobileNetV2 for the objective of classification) is proposed - A face mask identification model that can reliably determine whether an individual is wearing a mask or not is suggested and implemented in this work. The model architecture employs the peruse of MobileNetV2, a lightweight Convolutional Neural Network (CNN) that requires less computing power and can be readily integrated into computer vision and mobile systems. The computer vision with MobileNet is required to formulate a low-cost mask detection system for a group of people in open spaces that can assist in determining whether a person is wearing a mask or not, as well as function as a surveillance system since it is effective on both real-time pictures and videos. The face recognition model obtained 97.01% accuracy on validation data, 98% accuracy on training data and 97.45% accuracy on testing data.

Gupta Meenu, Chaudhary Gopal, Bansal Dhruvi, Pandey Shashwat

2022-Jul-21

CNN, Computer vision, Covid19, Deep learning, Mask classifier, MobileNetV2, Object detection

General General

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey.

In Sustainable cities and society

Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors' best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.

Himeur Yassine, Al-Maadeed Somaya, Almadeed Noor, Abualsaud Khalid, Mohamed Amr, Khattab Tamer, Elharrouss Omar

2022-Jul-21

Bird’s eye view, Convolutional neural networks, Euclidean distance, Pedestrian detection, Transfer learning, Visual social distancing monitoring

General General

A Hybrid Random Forest Deep learning Classifier Empowered Edge Cloud Architecture for COVID-19 and Pneumonia Detection.

In Expert systems with applications

COVID-19 is a global pandemic that mostly affects patients' respiratory systems, and the only way to protect oneself against the virus at present moment is to diagnose the illness, isolate the patient, and provide immunization. In the present situation, the testing used to predict COVID-19 is inefficient and results in more false positives. This difficulty can be solved by developing a remote medical decision support system that detects illness using CT scans or X-ray images with less manual interaction and is less prone to errors. The state-of-art techniques mainly used complex deep learning architectures which are not quite effective when deployed in resource-constrained edge devices. To overcome this problem, a multi-objective Modified Heat Transfer Search (MOMHTS) optimized hybrid Random Forest Deep learning (HRFDL) classifier is proposed in this paper. The MOMHTS algorithm mainly optimizes the deep learning model in the HRFDL architecture by optimizing the hyperparameters associated with it to support the resource-constrained edge devices. To evaluate the efficiency of this technique, extensive experimentation is conducted on two real-time datasets namely the COVID19 lung CT scan dataset and the Chest X-Ray images (Pneumonia) datasets. The proposed methodology mainly offers increased speed for communication between the IoT devices and COVID-19 detection via the MOMHTS optimized HRFDL classifier is modified to support the resources which can only support minimal computation and handle minimum storage. The proposed methodology offers an accuracy of 99% for both the COVID19 lung CT scan dataset and the Chest X-Ray images (Pneumonia) datasets with minimal computational time, cost, and storage. Based on the simulation outcomes, we can conclude that the proposed methodology is an appropriate fit for edge computing detection to identify the COVID19 and pneumonia with higher detection accuracy.

Hemalatha Murugan

2022-Jul-21

Healthcare industry, Heat Transfer Search Algorithm, Web Services, and Random Forest, and cloud computing, deep learning

General General

Prediction of viral-host interactions of COVID-19 by computational methods.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.

Alakus Talha Burak, Turkoglu Ibrahim

2022-Jul-21

Covid-19, Deep learning, Protein mapping, SARS-CoV-2 virus

General General

REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data

bioRxiv Preprint

In the recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aim is to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.

Ayuso Munoz, A.; Ugarte Carro, E.; Prieto Santamaria, L.; Otero Carrasco, B.; Menasalvas Ruiz, E.; Perez Gallardo, Y.; Rodriguez-Gonzalez, A.

2022-07-27

General General

MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction

ArXiv Preprint

The COVID-19 pandemic has caused devastating economic and social disruption, straining the resources of healthcare institutions worldwide. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform distribution of limited healthcare resources. We respond to one of these calls specific to the pediatric population. To address this challenge, we study two prediction tasks for the pediatric population using electronic health records: 1) predicting which children are more likely to be hospitalized, and 2) among hospitalized children, which individuals are more likely to develop severe symptoms. We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts and incorporates the inter-feature relationships between heterogeneous medical features via graph neural networks (GNN). We evaluate MedML across 143,605 patients for the hospitalization prediction task and 11,465 patients for the severity prediction task using data from the National Cohort Collaborative (N3C) dataset. We also report detailed group-level and individual-level feature importance analyses to evaluate the model interpretability. MedML achieves up to a 7% higher AUROC score and up to a 14% higher AUPRC score compared to the best baseline machine learning models and performs well across all nine national geographic regions and over all three-month spans since the start of the pandemic. Our cross-disciplinary research team has developed a method of incorporating clinical domain knowledge as the framework for a new type of machine learning model that is more predictive and explainable than current state-of-the-art data-driven feature selection methods.

Junyi Gao, Chaoqi Yang, George Heintz, Scott Barrows, Elise Albers, Mary Stapel, Sara Warfield, Adam Cross, Jimeng Sun, the N3C consortium

2022-07-25

General General

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit

ArXiv Preprint

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

Nicky Nirlipta Sahoo, Balamurali Murugesan, Ayantika Das, Srinivasa Karthik, Keerthi Ram, Steffen Leonhardt, Jayaraj Joseph, Mohanasankar Sivaprakasam

2022-07-25

General General

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

In The Lancet. Digital health

BACKGROUND : Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

METHODS : We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.

FINDINGS : The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).

INTERPRETATION : Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.

FUNDING : Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Myall Ashleigh, Price James R, Peach Robert L, Abbas Mohamed, Mookerjee Sid, Zhu Nina, Ahmad Isa, Ming Damien, Ramzan Farzan, Teixeira Daniel, Graf Christophe, Weiße Andrea Y, Harbarth Stephan, Holmes Alison, Barahona Mauricio

2022-Aug

General General

Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019.

In Resuscitation ; h5-index 66.0

BACKGROUND : Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

METHODS : We conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted machine models developed in patients without COVID-19 to the COVID-19 cohort. We also updated the neural network model using transfer learning. Performance was compared between models and the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score.

RESULTS : Among the 4,125 patients with COVID-19 included in the analysis, 484 (12%) patients survived with favorable neurological outcomes. The gradient boosted machine, trained on non-COVID-19 patients was the best performing model for predicting neurological outcomes in COVID-19 patients, significantly better than the CASPRI score (c-statistic: 0.75 vs. 0.67, P < 0.001). While calibration improved for the neural network with transfer learning, it did not surpass the gradient boosted machine in terms of discrimination.

CONCLUSION : Our gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.

Mayampurath Anoop, Bashiri Fereshteh, Hagopian Raffi, Venable Laura, Carey Kyle, Edelson Dana, Churpek Matthew

2022-Jul-19

cardiac arrest, machine learning, neurological outcomes, prediction

General General

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation.

In Computers in biology and medicine

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.

Qi Ailiang, Zhao Dong, Yu Fanhua, Heidari Ali Asghar, Wu Zongda, Cai Zhennao, Alenezi Fayadh, Mansour Romany F, Chen Huiling, Chen Mayun

2022-Jul-13

ACO, Ant colony optimization, COVID-19 X-ray, Image segmentation, Optimization, Swarm intelligence

General General

Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study.

In Computers in biology and medicine

The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.

Nadimi-Shahraki Mohammad H, Zamani Hoda, Mirjalili Seyedali

2022-Jul-16

Binary whale optimization algorithm, COVID-19, Classification, Feature selection, Medical data mining, Transfer functions

Radiology Radiology

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

In Medicine

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Li Matthew D, Arun Nishanth T, Aggarwal Mehak, Gupta Sharut, Singh Praveer, Little Brent P, Mendoza Dexter P, Corradi Gustavo C A, Takahashi Marcelo S, Ferraciolli Suely F, Succi Marc D, Lang Min, Bizzo Bernardo C, Dayan Ittai, Kitamura Felipe C, Kalpathy-Cramer Jayashree

2022-Jul-22

General General

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics.

In Frontiers in bioscience (Landmark edition)

BACKGROUND : The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome play a key role in controlling the pandemic. Recent studies have highlighted the role of point-of-care ultrasound imaging for COVID-19 screening and prognosis, particularly given that it is non-invasive, globally available, and easy-to-sanitize. COVIDx-US Dataset: Motivated by these attributes and the promise of artificial intelligence tools to aid clinicians, we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related ultrasound imaging data. The COVIDx-US dataset was curated from multiple data sources and its current version, i.e., v1.5., consists of 173 ultrasound videos and 21,570 processed images across 147 patients with COVID-19 infection, non-COVID-19 infection, other lung diseases/conditions, as well as normal control cases.

CONCLUSIONS : The COVIDx-US dataset was released as part of a large open-source initiative, the COVID-Net initiative, and will be continuously growing, as more data sources become available. To the best of the authors' knowledge, COVIDx-US is the first and largest open-access fully-curated benchmark lung ultrasound imaging dataset that contains a standardized and unified lung ultrasound score per video file, providing better interpretation while enabling other research avenues such as severity assessment. In addition, the dataset is reproducible, easy-to-use, and easy-to-scale thanks to the well-documented modular design.

Ebadi Ashkan, Xi Pengcheng, MacLean Alexander, Florea Adrian, Tremblay Stéphane, Kohli Sonny, Wong Alexander

2022-Jun-24

COVID-19, artificial intelligence, curated dataset, open-access, ultrasound imaging

General General

The mechanical ventilator of the future: a breath of hope for the viral pandemics to come.

In The Pan African medical journal

Respiratory care for the critically ill is a complex and difficult duty to accomplish. By replicating human knowledge with automated algorithms, artificial intelligence could provide solutions to facilitate this multidisciplinary task in developing countries, especially during humanitarian crisis, as the COVID-19 pandemic. This article provides an overview on the subject, from the emergent nations perspective.

Filho Luiz Alberto Cerqueira Batista

2022

Artificial intelligence, COVID-19, critical care, machine learning, mechanical ventilation

General General

Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

In Computational intelligence and neuroscience

COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID-19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning-coronavirus 19 (Ftl-CoV19) for COVID-19 detection through chest X-rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl-CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.

Singh Tarishi, Saurabh Praneet, Bisen Dhananjay, Kane Lalit, Pathak Mayank, Sinha G R

2022

Radiology Radiology

Human vs Artificial Intelligence-Based Echocardiography Analysis as Predictor of Outcomes: An analysis from the World Alliance Societies of Echocardiography COVID study.

In Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

BACKGROUND : Transthoracic echocardiography (TTE) is the leading cardiac imaging modality for patients admitted with COVID-19 infection, a condition of high short-term mortality. We aimed to test the hypothesis that artificial intelligence (AI) based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert.

METHODS : Patients admitted to 13 hospitals for acute COVID-19 disease who had a TTE were included. Left ventricular (LV) ejection fraction (EF) and LV longitudinal strain (LS) were obtained manually by multiple expert readers and by an automated, AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared.

RESULTS : 870 patients were enrolled, mortality was 27.4% at a follow-up of 230±115 days. AI analysis had lower variability than manual for both LV EF (p=0.003) and LS (p=0.005). AI-derived LV EF and LS were predictors of mortality in univariable and multivariable regression analysis (OR=0.974, 95% CI= 0.956-0.991, p=0.003 for EF; OR=1.060, 95% CI 1.019-1.105, p=0.004 for LS), but LV EF and LS obtained by manual analysis were not. Direct comparison of predictive value of AI vs manual measurements of LV EF and LS was significantly better for AI (p=0.005 and 0.003 respectively). In addition, AI-derived LV EF and LS had more significant and stronger correlations to other objective biomarkers for acute disease than manual reads.

CONCLUSIONS : AI-based analysis of LVEF and LVLS had a similar feasibility to manual analysis, minimized variability and consequently increased the statistical power to predict mortality. AI-based analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.

Asch Federico M, Descamps Tine, Sarwar Rizwan, Karagodin Ilya, Singulane Cristiane Carvalho, Xie Mingxing, Tucay Edwin S, Tude Rodrigues Ana C, Vasquez-Ortiz Zuilma Y, Monaghan Mark J, Ordonez Salazar Bayardo A, Soulat-Dufour Laurie, Alizadehasl Azin, Mostafavi Atoosa, Moreo Antonella, Citro Rodolfo, Narang Akhil, Wu Chun, Addetia Karima, Upton Ross, Woodward Gary M, Lang Roberto M

2022-Jul-18

Artificial Intelligence, COVID-19, Echocardiography, Left Ventricular Function, Machine Learning, Outcomes Prediction, WASE

General General

VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies. Therefore, there is a need to develop a predictive model that can incorporate mutational information to make reasonable predictions about the current pandemic situation.

METHODS : This paper proposes a deep learning prediction framework, VOC-DL, based on Variants Of Concern (VOC). The framework uses slope feature method to process the time series dataset containing VOC variant information, and uses VOC-LSTM, VOC-GRU and VOC-BILSTM prediction models included in the framework to predict the daily newly confirmed cases.

RESULTS : We analyzed daily newly confirmed cases in Italy, South Korea, Russia, Japan and India from April 14th, 2021 to July 3rd, 2021. The experimental results show that all VOC-DL models proposed in this paper can accurately predict the pandemic trend in the medium and long term, and VOC-LSTM model has the best prediction performance, with the highest average determination coefficient R2 of 96.83% in five nations' datasets. The overall prediction has robustness.

CONCLUSIONS : The experimental results show that VOC-LSTM is the best predictor for such a series of data and has higher prediction accuracy in the long run. At the same time, our VOC-DL framework combining VOC variants has reference significance for predicting other variants in the future.

Liao Zhifang, Song Yucheng, Ren Shengbing, Song Xiaomeng, Fan Xiaoping, Liao Zhining

2022-Jun-30

COVID-19, LSTM, Prediction, Time series, VOC-DL model, Variant

General General

Resting-state functional connectome predicts individual differences in depression during COVID-19 pandemic.

In The American psychologist

Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM's ability for capturing the depression-related patterns with individuals' resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mao Yu, Chen Qunlin, Wei Dongtao, Yang Wenjing, Sun Jiangzhou, Yu Yaxu, Zhuang Kaixiang, Wang Xiaoqin, He Li, Feng Tingyong, Lei Xu, He Qinghua, Chen Hong, Duan Shukai, Qiu Jiang

2022-Jul-21

Public Health Public Health

Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals.

In The Journal of international medical research

OBJECTIVES : Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19.

METHODS : This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities.

RESULTS : Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%.

CONCLUSION : Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting.

Ahmed Marwa M, Sayed Amal M, El Abd Dina, El Sayed Inas T, Elkholy Yasmine S, Fares Ahmed H, Fares Samar

2022-Jul

Primary care, coronavirus disease 2019, deep learning, early detection, neural network, severe acute respiratory syndrome coronavirus 2

Public Health Public Health

Predicting a Positive Antibody Response After 2 SARS-CoV-2 mRNA Vaccines in Transplant Recipients: A Machine Learning Approach With External Validation.

In Transplantation ; h5-index 56.0

BACKGROUND : Solid organ transplant recipients (SOTRs) are less likely to mount an antibody response to SARS-CoV-2 mRNA vaccines. Understanding risk factors for impaired vaccine response can guide strategies for antibody testing and additional vaccine dose recommendations.

METHODS : Using a nationwide observational cohort of 1031 SOTRs, we created a machine learning model to explore, identify, rank, and quantify the association of 19 clinical factors with antibody responses to 2 doses of SARS-CoV-2 mRNA vaccines. External validation of the model was performed using a cohort of 512 SOTRs at Houston Methodist Hospital.

RESULTS : Mycophenolate mofetil use, a shorter time since transplant, and older age were the strongest predictors of a negative antibody response, collectively contributing to 76% of the model's prediction performance. Other clinical factors, including transplanted organ, vaccine type (mRNA-1273 versus BNT162b2), sex, race, and other immunosuppressants, showed comparatively weaker associations with an antibody response. This model showed moderate prediction performance, with an area under the receiver operating characteristic curve of 0.79 in our cohort and 0.67 in the external validation cohort. An online calculator based on our prediction model is available at http://transplantmodels.com/covidvaccine/.

CONCLUSIONS : Our machine learning model helps understand which transplant patients need closer follow-up and additional doses of vaccine to achieve protective immunity. The online calculator based on this model can be incorporated into transplant providers' practice to facilitate patient-centric, precision risk stratification and inform vaccination strategies among SOTRs.

Alejo Jennifer L, Mitchell Jonathan, Chiang Teresa P-Y, Chang Amy, Abedon Aura T, Werbel William A, Boyarsky Brian J, Zeiser Laura B, Avery Robin K, Tobian Aaron A R, Levan Macey L, Warren Daniel S, Massie Allan B, Moore Linda W, Guha Ashrith, Huang Howard J, Knight Richard J, Gaber Ahmed Osama, Ghobrial Rafik Mark, Garonzik-Wang Jacqueline M, Segev Dorry L, Bae Sunjae

2022-Jul-21

General General

Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses.

In ACS omega

The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, a machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing a machine learning-based novel coronavirus prediction technique, called COVID-Predictor, where 1000 sequences of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and other viruses are used to train a Naive Bayes classifier so that it can predict any unknown sequences of these viruses. The model has been validated using 10-fold cross-validation in comparison with other machine learning techniques. The results show the superiority of our predictor by achieving an average 99.7% accuracy on an unseen validation set of viruses. The same pre-trained model has been used to design a web-based application where sequences of unknown viruses can be uploaded to predict the novel coronavirus.

Sarkar Jnanendra Prasad, Saha Indrajit, Ghosh Nimisha, Maity Debasree, Plewczynski Dariusz

2022-Jul-12

General General

Review on the COVID-19 pandemic prevention and control system based on AI.

In Engineering applications of artificial intelligence

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article's primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies.

Yi Junfei, Zhang Hui, Mao Jianxu, Chen Yurong, Zhong Hang, Wang Yaonan

2022-Sep

Artificial intelligence, COVID-19 big data, Intelligent equipment and systems, Intelligent robots

General General

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM.

In Health information science and systems

With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the "Tree Hole". The purpose of this article is to support the "Tree Hole" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of "Tree Hole" named "Zou Fan", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of "Tree Hole" messages in multiple time dimensions is positively correlated to emotion. The longer the "Tree Hole" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of "Tree Hole" rescue, volunteers should focus on the long-formed "Tree Hole" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.

Guo Chaohui, Lin Shaofu, Huang Zhisheng, Yao Yahong

2022-Dec

Adversarial training, BERT+BiLSTM, Depression, Sentiment analysis, Time feature

General General

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine.

In BioMed research international ; h5-index 102.0

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Sharma Ashwani, Virmani Tarun, Pathak Vipluv, Sharma Anjali, Pathak Kamla, Kumar Girish, Pathak Devender

2022

General General

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.

In Computational intelligence and neuroscience

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

Khan Muhammad Attique, Azhar Marium, Ibrar Kainat, Alqahtani Abdullah, Alsubai Shtwai, Binbusayyis Adel, Kim Ye Jin, Chang Byoungchol

2022

General General

An AI-enabled pre-trained model-based Covid detection model using chest X-ray images.

In Multimedia tools and applications

The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.

Gupta Rajeev Kumar, Kunhare Nilesh, Pathik Nikhlesh, Pathik Babita

2022-Jul-12

Convolution neural network, Covid-19, InceptionResNetV2, MobileNetV2, Pre-trained model, Resnet50, VGG19

Cardiology Cardiology

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.

In Frontiers in public health

The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.

Ramírez-Del Real Tania, Martínez-García Mireya, Márquez Manlio F, López-Trejo Laura, Gutiérrez-Esparza Guadalupe, Hernández-Lemus Enrique

2022

COVID-19, feature selection, imbalanced data, machine learning, predictive model

General General

A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.

In Frontiers in immunology ; h5-index 100.0

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

Lu Shuai, Li Yuguang, Ma Qiang, Nan Xiaofei, Zhang Shoutao

2022

B-cell epitopes prediction, Bi-LSTM, GCN, SARS-CoV-2, attention, structure-based

General General

Data-Centric Epidemic Forecasting: A Survey

ArXiv Preprint

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.

Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash

2022-07-19

Public Health Public Health

Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study.

In Frontiers in immunology ; h5-index 100.0

Background : Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited.

Objectives : To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively.

Findings : 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests.

Conclusions : Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

Sánchez-Montalvá Adrián, Álvarez-Sierra Daniel, Martínez-Gallo Mónica, Perurena-Prieto Janire, Arrese-Muñoz Iria, Ruiz-Rodríguez Juan Carlos, Espinosa-Pereiro Juan, Bosch-Nicolau Pau, Martínez-Gómez Xavier, Antón Andrés, Martínez-Valle Ferran, Riveiro-Barciela Mar, Blanco-Grau Albert, Rodríguez-Frias Francisco, Castellano-Escuder Pol, Poyatos-Canton Elisabet, Bas-Minguet Jordi, Martínez-Cáceres Eva, Sánchez-Pla Alex, Zurera-Egea Coral, Teniente-Serra Aina, Hernández-González Manuel, Pujol-Borrell Ricardo

2022

CXCL10, SARS-CoV-2 infection, acute phase reactants, chemokines, clinical laboratory tests, cytokines, flow cytometry, predictive risk-profile

General General

Discovering novel systemic biomarkers in photos of the external eye

ArXiv Preprint

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared against baseline models incorporating available clinicodemographic variables (e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on validation set A (a patient population similar to the development sets), where the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7.3-13.2%. Our findings provide further evidence that external eye photos contain important biomarkers of systemic health spanning multiple organ systems. Further work is needed to investigate whether and how these biomarkers can be translated into clinical impact.

Boris Babenko, Ilana Traynis, Christina Chen, Preeti Singh, Akib Uddin, Jorge Cuadros, Lauren P. Daskivich, April Y. Maa, Ramasamy Kim, Eugene Yu-Chuan Kang, Yossi Matias, Greg S. Corrado, Lily Peng, Dale R. Webster, Christopher Semturs, Jonathan Krause, Avinash V. Varadarajan, Naama Hammel, Yun Liu

2022-07-19

Public Health Public Health

Synergy between Public and Private Healthcare Organizations during COVID-19 on Twitter.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Social media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and NGOs for communicating health concerns, new advancements, and potential outbreaks. While the benefits of using them as a tool have been extensively discussed, the online activity of various healthcare organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated.

OBJECTIVE : The purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations.

METHODS : Data was collected from the Twitter handles of five pharmaceutical companies, ten U.S. and Canadian public health agencies, and World Health Organization (WHO) between January 01, 2017 - December 31, 2021. A total of 181,469 tweets were divided into two phases for the analysis: before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using Natural Language Processing (NLP) based topic modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents.

RESULTS : We utilized the topics modeled from the tweets authored by the health organizations chosen for our analysis using Non-Negative Matrix Factorization (NMF) ('c_umass' scores: -3.6530 and -3.7944, before COVID-19 and during COVID-19 respectively). The topics are - 'Chronic Diseases', 'Health Research', 'Community Healthcare', 'Medical Trials', 'COVID-19', 'Vaccination', 'Nutrition and Well-being', and 'Mental Health'. In terms of user impact, WHO (user impact: 4171.24) had the highest impact overall, followed by the public health agencies, CDC (user impact: 2895.87), and NIH (user impact: 891.06). Among pharmaceutical companies, Pfizer's user impact was the highest at 97.79. Furthermore, for sentiment forecasting, ARIMA and SARIMAX models performed best on the majority of the subsets of data (divided as per the health organization and time-period), with Mean Absolute Error (MAE) between 0.027 - 0.084, Mean Squared Error (MSE) between 0.001 - 0.011, and Root Mean Squared Error (RMSE) between 0.031 - 0.105.

CONCLUSIONS : Our findings indicate that people engage more on topics like 'COVID-19' than 'Medical Trials', 'Customer Experience'. Also, there are notable differences in the user engagement levels across organizations. Global organizations, like WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement.

CLINICALTRIAL :

Singhal Aditya, Baxi Manmeet Kaur, Mago Vijay

2022-Jul-15

Pathology Pathology

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

In Journal of proteome research

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

Vanderboom Patrick M, Renuse Santosh, Maus Anthony D, Madugundu Anil K, Kemp Jennifer V, Gurtner Kari M, Singh Ravinder J, Grebe Stefan K, Pandey Akhilesh, Dasari Surendra

2022-Jul-18

COVID-19, antigen detection, limit of detection (LOD), machine learning (ML), parallel reaction monitoring (PRM), sensitivity

General General

Predicting Multiple Sclerosis Outcomes during the COVID-19 Stay-at-Home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.

In JMIR mental health

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has broad negative impact on physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).

OBJECTIVE : We present a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated "stay-at-home" period due to a global pandemic.

METHODS : First, we extract features that capture behavioral changes due to the "stay-at-home" order. Then, we adapt and apply an existing algorithm to these behavioral change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the "stay-at-home" period.

RESULTS : Using data collected between November 2019 and May 2020, algorithm detects depression with an accuracy of 82.5% (65% improvement over baseline; f1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; f1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; f1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; f1-score: 0.84).

CONCLUSIONS : Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics that would cause drastic behavioral changes.

CLINICALTRIAL : Not Applicable.

Chikersal Prerna, Venkatesh Shruthi, Masown Karmen, Walker Elizabeth, Quraishi Danyal, Dey Anind, Goel Mayank, Xia Zongqi

2022-Jul-16

Surgery Surgery

What, Where, When and How of COVID-19 Patents Landscape: A Bibliometrics Review.

In Frontiers in medicine

Two years after COVID-19 came into being, many technologies have been developed to bring highly promising bedside methods to help fight this epidemic disease. However, owing to viral mutation, how far the promise can be realized remains unclear. Patents might act as an additional source of information for informing research and policy and anticipating important future technology developments. A comprehensive study of 3741 COVID-19-related patents (3,543 patent families) worldwide was conducted using the Derwent Innovation database. Descriptive statistics and social network analysis were used in the patent landscape. The number of COVID-19 applications, especially those related to treatment and prevention, continued to rise, accompanied by increases in governmental and academic patent assignees. Although China dominated COVID-19 technologies, this position is worth discussing, especially in terms of the outstanding role of India and the US in the assignee collaboration network as well as the outstanding invention portfolio in Italy. Intellectual property barriers and racist treatment were reduced, as reflected by individual partnerships, transparent commercial licensing and diversified portfolios. Critical technological issues are personalized immunity, traditional Chinese medicine, epidemic prediction, artificial intelligence tools, and nucleic acid detection. Notable challenges include balancing commercial competition and humanitarian interests. The results provide a significant reference for decision-making by researchers, clinicians, policymakers, and investors with an interest in COVID-19 control.

Liu Kunmeng, Zhang Xiaoming, Hu Yuanjia, Chen Weijie, Kong Xiangjun, Yao Peifen, Cong Jinyu, Zuo Huali, Wang Jian, Li Xiang, Wei Benzheng

2022

COVID-19, bibliometric patent analysis, citation network, coronavirus, patent landscape, patent mining, social network analysis

oncology Oncology

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

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

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

Sahebkar Amirhossein, Abbasifard Mitra, Chaibakhsh Samira, Guest Paul C, Pourhoseingholi Mohamad Amin, Vahedian-Azimi Amir, Kesharwani Prashant, Jamialahmadi Tannaz

2022

COVID-19, Chest CT, Computed tomography, Deep learning, Diffuse opacities, Lesion distribution, SARS-CoV-2

General General

Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles.

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

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

Lazari Lucas C, Rosa-Fernandes Livia, Palmisano Giuseppe

2022

Biomarkers, COVID-19, Classification, MALDI-TOF, Machine learning, Prognostic

General General

Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective.

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

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

Guest Paul C, Popovic David, Steiner Johann

2022

Bias, Biomarker discovery, COVID-19, Confounding factor, Machine learning, Multiplex assay, SARS-CoV-2

General General

Users' Feedback on COVID-19 Lockdown Documentary: An Emotion Analysis and Topic Modeling Analysis.

In Frontiers in psychology ; h5-index 92.0

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose "The lockdown: One month in Wuhan" YouTube documentary, and applied emotion analysis as well as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation (LDA) topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust (22.8%), joy (15.4%), and anticipation (17.6%) are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing.

Shi Xiaochuan, Jia Miaoyutian, Li Jia, Chen Quiyi, Liu Guan, Liu Qian

2022

COVID-19, LDA topic modeling, emotion analysis, health measures, lockdown

General General

A Smart Device for a Preliminary Dental Examination Based on the Internet of Things.

In Computational intelligence and neuroscience

The COVID-19 pandemic has threatened the lives of many people, especially the elderly and those with chronic illnesses, as well as threatening the global economy. In response to the pandemic, many medical centers, including dental facilities, have significantly reduced the treatment of patients by limiting clinical practice to exclusively urgent, nondeferred care. Dentists are more vulnerable to contracting COVID-19, due to the necessity of the dentist being close to the patient. One of the precautions that dentists take to avoid transmitting infections is to wear a mask and gloves. However, the basic condition for nontransmission of infection is to leave a safe distance between the patient and the dentist. This system can be implemented by using an Arduino microcontroller, which is designed as a preliminary device by a dentist to examine a patient's teeth so that a safe distance of three meters between the dentist and the patient can be maintained. The project is based on hardware and has been programmed through Arduino. The proposed system uses a small wired camera with a length of five meters that is connected to the dentist's mobile or laptop and is installed on a robotic arm. The dentist can control the movement of the arm in all directions using a joystick at a distance of three meters. The results showed the effectiveness of this system for leaving a safe distance between the patient and the dentist. In our future work, we will control the movement of the arm via Bluetooth, and we will use a wi-fi-based camera.

Wedyan Mohammad, Alturki Ryan, Gazzawe Foziah, Ramadan Enas

2022

General General

The Role of Emerging Technologies to Fight Against COVID-19 Pandemic: An Exploratory Review.

In Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology

Since the end of the year 2019, the whole world is experiencing a global emergency due to the COVID-19 pandemic. The major sectors including industry, economics, education have been affected. Ongoing pandemics confined us to avoid mass gathering and rigorously maintain social distancing to mitigate the spreading of this infectious disease. In this situation emerging technologies including the internet of things (IoT), Artificial Intelligence (AI) is playing a very important role in various fields such as healthcare, economics, educational system, and others to monitoring or tackle the impact of COVID-19 pandemic. Several papers discussed the impact of IoT on the COVID-19 pandemic in various aspects. However, the challenges and designing issues towards the implementation of IoT-based monitoring systems are not deeply investigated. Alongside, the adaptation of IoT and other technologies in the post-covid situation is not addressed properly. Our review article provides an up to date extensive survey on how IoT-enabled technologies are helping to combat the pandemic and to manage industry, education, economic, and medical system. As result, the realization is that IoT and other associated technologies have a great impact on virus detection, tracking, and mitigate the spread. In the face of an expeditiously spreading pandemic, the associated designing issues of the IoT-based framework have been looked into as a part of this review. Alongside, this review highlights the major challenges like privacy, security scalability, etc. facing in using such technologies. Finally, we explore 'The New Normal' and the use of technologies to help in the post-pandemic era.

Mondal Sanjoy, Mitra Priyanjana

2022

AI, COVID-19, Drone, IoMT, IoT, Pandemic

General General

Topological Analysis on Multi-scenario Graphs: Applications Toward Discerning Variability in SARS-CoV-2 and Topic Similarity in Research.

In Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology

A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysis. We focus on two different application areas: analyzing geographical variations in SARS-CoV-2 and studying topic similarity in citation patterns.

Biswas Sourav, Bhattacharyya Malay, Bandyopadhyay Sanghamitra

2022

Citation Network, Graphlet and Graphlet Degree Distribution, Multi-scenario Graphs, SARS-CoV-2, Topic Similarity Network, Topological Analysis

Surgery Surgery

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran.

In Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine

Background : Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19).

Aims and objectives : To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care.

Materials and methods : In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models.

Results : A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors.

Conclusion : In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients' early data, particularly in low- and middle-income countries where their resources are as limited as Iran.

How to cite this article : Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688-695.

Ethics approval : This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

Sabetian Golnar, Azimi Aram, Kazemi Azar, Hoseini Benyamin, Asmarian Naeimehossadat, Khaloo Vahid, Zand Farid, Masjedi Mansoor, Shahriarirad Reza, Shahriarirad Sepehr

2022-Jun

COVID-19, Intensive care, Iran, Machine-learning, Prediction, Regression

General General

Passenger Surveillance Using Deep Learning in Post-COVID-19 Intelligent Transportation System.

In Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology

Intelligent Transport System should be renovated in many aspects in post-pandemic situation like COVID-19. The passenger-count inside a car will be restricted based on the vehicle capacity and the COVID-19 hot-spot zone. Traffic rules will be impacted to align with a similar contagious outbreak. The on-road 'Yellow-Vulture' cameras need to incorporate such surveillance rules to monitor related anomalies for preventing contamination. To maintain safe-distance, an automatic surveillance system will be preferred by the Government very soon. Moreover, facial mask usage during the journey has become an essential habit to stop the spread of the infection. In this article, we have proposed a deep-Learning based framework that employs an augmented image data set to provide proper surveillance in the transport system to maintain the health protocols. Fast and accurate detection of the number of passengers inside a car and their face masks from the traffic inspection camera feed has been demonstrated. We have exploited the advantages of the popular Transfer Learning approach with novel variations of images while performing the training. To the best of our knowledge, this is the first attempt to watch over in-vehicle social-distancing in post-pandemic circumstances through deep-Learning based image analysis. The superiority of the proposed framework has been established over several state-of-the-art techniques using different numerical metrics and visual comparisons along with a support of statistical hypothesis test. Our technique has achieved 98.5 % testing accuracy in various adverse conditions. Zero-shot evaluation has been explored for the Real-Time-Medical-Mask-Detection data set Wang et al. (Real-Time-Medical-Mask-Detection, 2020a https://github.com/TheSSJ2612/Real-Time-Medical-Mask-Detection/, Accessed 14 Nov 2020), where we have attained 96.4 % accuracy that manifests the generalization of the network.

Kundu Srimanta, Maulik Ujjwal

2022-May-26

COVID-19, Inception-V3, Mask detection, Passenger counting, Transfer learning, Vehicle surveillance

Surgery Surgery

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

In Cell reports. Medicine

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Feyaerts Dorien, Hédou Julien, Gillard Joshua, Chen Han, Tsai Eileen S, Peterson Laura S, Ando Kazuo, Manohar Monali, Do Evan, Dhondalay Gopal K R, Fitzpatrick Jessica, Artandi Maja, Chang Iris, Snow Theo T, Chinthrajah R Sharon, Warren Christopher M, Wittman Richard, Meyerowitz Justin G, Ganio Edward A, Stelzer Ina A, Han Xiaoyuan, Verdonk Franck, Gaudillière Dyani K, Mukherjee Nilanjan, Tsai Amy S, Rumer Kristen K, Jacobsen Danielle R, Bjornson-Hooper Zachary B, Jiang Sizun, Saavedra Sergio Fragoso, Valdés Ferrer Sergio Iván, Kelly J Daniel, Furman David, Aghaeepour Nima, Angst Martin S, Boyd Scott D, Pinsky Benjamin A, Nolan Garry P, Nadeau Kari C, Gaudillière Brice, McIlwain David R

2022-Jun-28

COVID-19, CyTOF, Olink, PBMC, SARS-CoV-2, immunophenotyping, mass cytometry, phosphosignaling response, proteomics, stacked generalization

Public Health Public Health

The effect of different degrees of lockdown and self-identified gender on anxiety, depression and suicidality during the COVID-19 pandemic: Data from the international COMET-G study.

In Psychiatry research ; h5-index 64.0

INTRODUCTION : During the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question.

MATERIAL AND METHODS : During the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS : It included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis RESULTS: Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small.

CONCLUSIONS : The current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups.

Fountoulakis Konstantinos N, Karakatsoulis Grigorios N, Abraham Seri, Adorjan Kristina, Ahmed Helal Uddin, Alarcón Renato D, Arai Kiyomi, Auwal Sani Salihu, Berk Michael, Bjedov Sarah, Bobes Julio, Bobes-Bascaran Teresa, Bourgin-Duchesnay Julie, Bredicean Cristina Ana, Bukelskis Laurynas, Burkadze Akaki, Abud Indira Indiana Cabrera, Castilla-Puentes Ruby, Cetkovich Marcelo, Colon-Rivera Hector, Corral Ricardo, Cortez-Vergara Carla, Crepin Piirika, De Berardis Domenico, Delgado Sergio Zamora, De Lucena David, De Sousa Avinash, Stefano Ramona Di, Dodd Seetal, Elek Livia Priyanka, Elissa Anna, Erdelyi-Hamza Berta, Erzin Gamze, Etchevers Martin J, Falkai Peter, Farcas Adriana, Fedotov Ilya, Filatova Viktoriia, Fountoulakis Nikolaos K, Frankova Iryna, Franza Francesco, Frias Pedro, Galako Tatiana, Garay Cristian J, Garcia-Álvarez Leticia, García-Portilla Maria Paz, Gonda Xenia, Gondek Tomasz M, González Daniela Morera, Gould Hilary, Grandinetti Paolo, Grau Arturo, Groudeva Violeta, Hagin Michal, Harada Takayuki, Hasan Tasdik M, Hashim Nurul Azreen, Hilbig Jan, Hossain Sahadat, Iakimova Rossitza, Ibrahim Mona, Iftene Felicia, Ignatenko Yulia, Irarrazaval Matias, Ismail Zaliha, Ismayilova Jamila, Jacobs Asaf, Jakovljević Miro, Jakšić Nenad, Javed Afzal, Kafali Helin Yilmaz, Karia Sagar, Kazakova Olga, Khalifa Doaa, Khaustova Olena, Koh Steve, Kopishinskaia Svetlana, Kosenko Korneliia, Koupidis Sotirios A, Kovacs Illes, Kulig Barbara, Lalljee Alisha, Liewig Justine, Majid Abdul, Malashonkova Evgeniia, Malik Khamelia, Malik Najma Iqbal, Mammadzada Gulay, Mandalia Bilvesh, Marazziti Donatella, Marčinko Darko, Martinez Stephanie, Matiekus Eimantas, Mejia Gabriela, Memon Roha Saeed, Martínez Xarah Elenne Meza, Mickevičiūtė Dalia, Milev Roumen, Mohammed Muftau, Molina-López Alejandro, Morozov Petr, Muhammad Nuru Suleiman, Mustač Filip, Naor Mika S, Nassieb Amira, Navickas Alvydas, Okasha Tarek, Pandova Milena, Panfil Anca-Livia, Panteleeva Liliya, Papava Ion, Patsali Mikaella E, Pavlichenko Alexey, Pejuskovic Bojana, Da Costa Mariana Pinto, Popkov Mikhail, Popovic Dina, Raduan Nor Jannah Nasution, Ramírez Francisca Vargas, Rancans Elmars, Razali Salmi, Rebok Federico, Rewekant Anna, Flores Elena Ninoska Reyes, Rivera-Encinas María Teresa, Saiz Pilar, de Carmona Manuel Sánchez, Martínez David Saucedo, Saw Jo Anne, Saygili Görkem, Schneidereit Patricia, Shah Bhumika, Shirasaka Tomohiro, Silagadze Ketevan, Sitanggang Satti, Skugarevsky Oleg, Spikina Anna, Mahalingappa Sridevi Sira, Stoyanova Maria, Szczegielniak Anna, Tamasan Simona Claudia, Tavormina Giuseppe, Tavormina Maurilio Giuseppe Maria, Theodorakis Pavlos N, Tohen Mauricio, Tsapakis Eva Maria, Tukhvatullina Dina, Ullah Irfan, Vaidya Ratnaraj, Vega-Dienstmaier Johann M, Vrublevska Jelena, Vukovic Olivera, Vysotska Olga, Widiasih Natalia, Yashikhina Anna, Prezerakos Panagiotis E, Smirnova Daria

2022-Jul-01

COVID-19, Depression, Mental health, lockdown, anxiety, mental health history, Suicidality

Public Health Public Health

A Deep Learning Approach for Spatio-Temporal Forecasting of New Cases and New Hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy.

In Journal of biomedical informatics ; h5-index 55.0

BACKGROUND : Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km x 12 km), including satellite information.

METHODS : We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)2. We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model. Findings Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags. Interpretation ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)2, getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.

Sciannameo Veronica, Goffi Alessia, Maffeis Giuseppe, Gianfreda Roberta, Jahier Pagliari Daniele, Filippini Tommaso, Mancuso Pamela, Giorgi-Rossi Paolo, Alberto Dal Zovo Leonardo, Corbari Angela, Vinceti Marco, Berchialla Paola

2022-Jul-11

COVID-19, ConvLSTM, SARS-CoV-2, deep learning, forecasting, spatio-temporal

General General

Federated Learning in Risk Prediction: A Primer and Application to COVID-19-Associated Acute Kidney Injury.

In Nephron

BACKGROUND : Modern machine learning and deep learning algorithms require large amounts of data; however, data sharing between multiple healthcare institutions is limited by privacy and security concerns.

SUMMARY : Federated learning provides a functional alternative to the single-institution approach while avoiding the pitfalls of data sharing. In cross-silo federated learning, the data do not leave a site. The raw data are stored at the site of collection. Models are created at the site of collection and are updated locally to achieve a learning objective. We demonstrate a use case with COVID-19-associated AKI. We showed that federated models outperformed their local counterparts, even when evaluated on local data in the test dataset, and performance was like those being used for pooled data. Increases in performance at a given hospital were inversely proportional to dataset size at a given hospital, which suggests that hospitals with smaller datasets have significant room for growth with federated learning approaches.

KEY MESSAGES : This short article provides an overview of federated learning, gives a use case for COVID-19-associated acute kidney injury, and finally details the issues along with some potential solutions.

Gulamali Faris F, Nadkarni Girish N

2022-Jul-14

Acute renal failure, Acute renal injury, Kidney

General General

Feature-level ensemble approach for COVID-19 detection using chest X-ray images.

In PloS one ; h5-index 176.0

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.

Ho Thi Kieu Khanh, Gwak Jeonghwan

2022

Radiology Radiology

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.

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

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.

Venkataramana Lokeswari, Prasad D Venkata Vara, Saraswathi S, Mithumary C M, Karthikeyan R, Monika N

2022-Jul-14

Convolutional neural network, Data augmentation, Data balancing, Feature selection, Multi-level classification, Normalization

Public Health Public Health

Human-annotated dataset for social media sentiment analysis for Albanian language.

In Data in brief

Social media was a heavily used platform by people in different countries to express their opinions about different crises, especially during the Covid-19 pandemics. This dataset is created through collecting people's comments in the news items on the official Facebook site of the National Institute of Public Health of Kosovo. The dataset contains a total of 10,132 comments that are human-annotated in the Albanian language as a low-resource language. The dataset was collected from March 12, 2020, and this coincides with the emergence of the first confirmed Covid-19 case in Kosovo until August 31, 2020, when the second wave started. Due to the scarcity of labeled data for low-resource languages, the dataset can be used by the research community in the field of machine learning, information retrieval, affective computing, as well as by the public agencies and decision makers.

Kadriu Fatbardh, Murtezaj Doruntina, Gashi Fatbardh, Ahmedi Lule, Kurti Arianit, Kastrati Zenun

2022-Aug

Affective computing, Machine/deep learning, NLP, Sentiment analysis, Text classification

Cardiology Cardiology

Incidence, risk factors, natural history, and hypothesised mechanisms of myocarditis and pericarditis following covid-19 vaccination: living evidence syntheses and review.

In BMJ (Clinical research ed.)

OBJECTIVES : To synthesise evidence on incidence rates and risk factors for myocarditis and pericarditis after use of mRNA vaccination against covid-19, clinical presentation, short term and longer term outcomes of cases, and proposed mechanisms.

DESIGN : Living evidence syntheses and review.

DATA SOURCES : Medline, Embase, and the Cochrane Library were searched from 6 October 2020 to 10 January 2022; reference lists and grey literature (to 13 January 2021). One reviewer completed screening and another verified 50% of exclusions, using a machine learning program to prioritise records. A second reviewer verified all exclusions at full text, extracted data, and (for incidence and risk factors) risk of bias assessments using modified Joanna Briggs Institute tools. Team consensus determined certainty of evidence ratings for incidence and risk factors using GRADE (Grading of Recommendations, Assessment, Development and Evaluation).

ELIGIBILITY CRITERIA FOR SELECTING STUDIES : Large (>10 000 participants) or population based or multisite observational studies and surveillance data (incidence and risk factors) reporting on confirmed myocarditis or pericarditis after covid-19 mRNA vaccination; case series (n≥5, presentation, short term clinical course and longer term outcomes); opinions, letters, reviews, and primary studies focused on describing or supporting hypothesised mechanisms.

RESULTS : 46 studies were included (14 on incidence, seven on risk factors, 11 on characteristics and short term course, three on longer term outcomes, and 21 on mechanisms). Incidence of myocarditis after mRNA vaccines was highest in male adolescents and male young adults (age 12-17 years, range 50-139 cases per million (low certainty); 18-29 years, 28-147 per million (moderate certainty)). For girls and boys aged 5-11 years and women aged 18-29 years, incidence of myocarditis after vaccination with BNT162b2 (Pfizer/BioNTech) could be fewer than 20 cases per million (low certainty). Incidence after a third dose of an mRNA vaccine had very low certainty evidence. For individuals of 18-29 years, incidence of myocarditis is probably higher after vaccination with mRNA-1273 (Moderna) compared with Pfizer (moderate certainty). Among individuals aged 12-17, 18-29, or 18-39 years, incidence of myocarditis or pericarditis after dose two of an mRNA vaccine for covid-19 might be lower when administered ≥31 days compared with ≤30 days after dose one (low certainty). Data specific to men aged 18-29 years indicated that the dosing interval might need to increase to ≥56 days to substantially drop myocarditis or pericarditis incidence. For clinical course and short term outcomes, only one small case series (n=8) was found for 5-11 year olds. In adolescents and adults, most (>90%) myocarditis cases involved men of a median 20-30 years of age and with symptom onset two to four days after a second dose (71-100%). Most people were admitted to hospital (≥84%) for a short duration (two to four days). For pericarditis, data were limited but more variation than myocarditis has been reported in patient age, sex, onset timing, and rate of admission to hospital. Three case series with longer term (3 months; n=38) follow-up suggested persistent echocardiogram abnormalities, as well as ongoing symptoms or a need for drug treatments or restriction from activities in >50% of patients. Sixteen hypothesised mechanisms were described, with little direct supporting or refuting evidence.

CONCLUSIONS : These findings indicate that adolescent and young adult men are at the highest risk of myocarditis after mRNA vaccination. Use of a Pfizer vaccine over a Moderna vaccine and waiting for more than 30 days between doses might be preferred for this population. Incidence of myocarditis in children aged 5-11 years is very rare but certainty was low. Data for clinical risk factors were very limited. A clinical course of mRNA related myocarditis appeared to be benign, although longer term follow-up data were limited. Prospective studies with appropriate testing (eg, biopsy and tissue morphology) will enhance understanding of mechanism.

Pillay Jennifer, Gaudet Lindsay, Wingert Aireen, Bialy Liza, Mackie Andrew S, Paterson D Ian, Hartling Lisa

2022-Jul-13

General General

Tchebichef Transform Domain-Based Deep Learning Architecture for Image Super-Resolution.

In IEEE transactions on neural networks and learning systems

Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low-frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.

Kumar Ahlad, Singh Harsh Vardhan, Khare Vijeta

2022-Jul-13

General General

Development and integration of VGG and dense transfer-learning systems supported with diverse lung images for discovery of the Coronavirus identity.

In Informatics in medicine unlocked

The contagious SARS-CoV-2 has had a tremendous impact on the life and health of many communities. It was first rampant in early 2019 and so far, 539 million cases of COVID-19 have been reported worldwide. This is reminiscent of the 1918 influenza pandemic. However, we can detect the infected cases of COVID-19 by analysing either X-rays or CT, which are presumably considered the least expensive methods. In the existence of state-of-the-art convolutional neural networks (CNNs), which integrate image pre-processing techniques with fully connected layers, we can develop a sophisticated AI system contingent on various pre-trained models. Each pre-trained model we involved in our study assumed its role in extracting some specific features from different chest image datasets in many verified sources, such as (Mendeley, Kaggle, and GitHub). First, for CXR datasets associated with the CNN trained model from the beginning, whereby is comprised of four layers beginning with the Conv2D layer, which comprises 32 filters, followed by the MaxPooling and afterwards, we reiterated similarly. We used two techniques to avoid overgeneralization, the early stopping and the Dropout techniques. After all, the output was one neuron to classify both cases of 0 or 1, followed by a sigmoid function; in addition, we used the Adam optimizer owing to the more improved outcomes than what other optimizers conducted; ultimately, we referred to our findings by using a confusion matrix, classification report (Recall & Precision), sensitivity and specificity; in this approach, we achieved a classification accuracy of 96%. Our three integrated pre-trained models (VGG16, DenseNet201, and DenseNet121) yielded a remarkable test accuracy of 98.81%. Besides, our merged models (VGG16, DenseNet201) trained on CT images with the utmost effort; this model held an accurate test of 99.73% for binary classification with the (Normal/Covid-19) scenario. Comparing our results with related studies shows that our proposed models were superior to the previous CNN machine learning models in terms of various performance metrics. Our pre-trained model associated with the CT dataset achieved 100% of the F1score and the loss value was approximately 0.00268.

Hamwi Wael Abdalsalam, Almustafa Muhammad Mazen

2022-Jul-08

Artificial intelligence, CXR&CT chest COVID-19 images integration of three pre-trained CNN models Fine-tuning, Image processing, Performance evaluation

General General

Detection of COVID-19 using deep learning techniques and classification methods.

In Information processing & management

Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID 19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID 19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.

Oğuz Çinare, Yağanoğlu Mete

2022-Jul-08

Classification, Covid-19, Deep learning, ResNet

General General

Post-vaccination infection rates and modification of COVID-19 symptoms in vaccinated UK school-aged children and adolescents: A prospective longitudinal cohort study.

In The Lancet regional health. Europe

Background : We aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and adolescents (CA) in the UK during periods of Delta and Omicron variant predominance.

Methods : In this prospective longitudinal cohort study, we analysed data from 115,775 CA aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CA with post-vaccination SARS-CoV-2 infection, compared to unvaccinated CA, and post-vaccination side-effects.

Findings : Between August 5, 2021 and February 14, 2022, 25,971 UK CA aged 12-17 years received one dose of BNT162b2 vaccine. The probability of testing positive for infection diverged soon after vaccination, and was lower in CA with prior SARS-CoV-2 infection. Vaccination reduced proxy-reported infection risk (-80·4% (95% CI -0·82 -0·78) and -53·7% (95% CI -0·62 -0·43) at 14-30 days with Delta and Omicron variants respectively, and -61·5% (95% CI -0·74 -0·44) and -63·7% (95% CI -0·68 -0.59) after 61-90 days). Vaccinated CA who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CA; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CA. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved within few days (3 days in most cases).

Interpretation : One dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CA aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CA also had generally mild disease. Overall, vaccination was well-tolerated.

Funding : UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer's Society, and ZOE Limited.

Molteni Erika, Canas Liane S, Kläser Kerstin, Deng Jie, Bhopal Sunil S, Hughes Robert C, Chen Liyuan, Murray Benjamin, Kerfoot Eric, Antonelli Michela, Sudre Carole H, Pujol Joan Capdevila, Polidori Lorenzo, May Anna, Hammers Prof Alexander, Wolf Jonathan, Spector Prof Tim D, Steves Claire J, Ourselin Prof Sebastien, Absoud Michael, Modat Marc, Duncan Prof Emma L

2022-Aug

BNT162b2 vaccine effectiveness, BNT162b2, Comirnaty SARS-CoV-2 vaccine (BioNTech, Pfizer), CA, Children and adolescents, COVID-19 vaccination, “KCL, Kings College London”, LFAT, Lateral flow antigen test, OR, Odds Ratio, PCR, Polymerase chain reaction, Paediatrics, SARS-CoV-2 vaccination, SARS-CoV-2 vaccination in children, SARS-CoV-2, Severe acute respiratory syndrome‐related coronavirus 2, UK, United Kingdom of Great Britain and Northern Ireland

General General

Exploring the Development of Chinese Digital Resources under Lightweight Deep Learning.

In Computational intelligence and neuroscience

From 2019, countries worldwide have been negatively affected by the corona virus disease 2019 (COVID-19) in all aspects of social life. The high-tech digital industry represented by emerging digital technologies is still vigorous, and correspondingly, the digital economy has become an important force to promote the stable recovery and re-prosperity of the national economy. The digital economy plays a memorable role in preventing and controlling COVID-19, the resumption of work and production, and the creation of new business formats and models. Urban big data (UBD) involves a wide range of dynamic and static data with high dimensions, but there are no mature and clear data classification and grading standards. Currently, it is urgent to strengthen the security protection of high-value datasets. Therefore, a UBD classification and grading method is proposed based on the lightweight (LWT) deep learning (DL) clustering algorithm. It uses a semi-intelligent path based on partial artificial to form data classification (DC) and hierarchical thesaurus, corpus, rule base, and model base. Subsequently, a big data analysis system is built for unstructured and structured data association analysis based on deep learning, spatiotemporal correlation, and big data technology to improve data value and adapt to multiscenario applications. Meanwhile, with the help of data and graphics processing tool Tableau, the present work analyzes the development status and existing problems of digital resources in China. The results show that although China's digital infrastructure is the top in the world, the trading infrastructure is still only 41.65 percentage points. This shows that China's digital economy still has a lot of room for growth in distribution and trading. The analysis of the ownership of data resources indicates that the scores of China's digital economy in accounting, privacy, and security are very low, only 2.4 points, 5.1 points, and 11 points, respectively. This study has solved the problems of distribution and trade in China's digital economy through research and put forward corresponding suggestions for the current development of China's digital economy market. Hence, a preliminary summary and suggestions are made on the development of China's data resources, to promote the open sharing of data, strengthen the management of data quality, activate the data resource market, strengthen data security, and enhance the vitality of the market economy.

Song Bai

2022

General General

Short term Markov corrector for building load forecasting system - Concept and case study of day-ahead load forecasting under the impact of the COVID-19 pandemic.

In Energy and buildings

In this paper, we present the concept and formulation of a short-term Markov corrector to an underlying day-ahead building load forecasting model. The models and the correctors are then integrated to the building supervision, control and data acquisition system to automate the self-updating and retraining processes. The proposed Markov corrector is experimentally proven to significantly improve the reactivity of the forecasting models with respect to untaught variations. Developed in a discrete manner over a continuous forecasting model, the corrector also helps to capture better the consumption peaks during the activity days. A proof-of-concept is demonstrated via the case study of the GreenER building, where the impact of the Markov correctors to the performance of the existing day-ahead load forecasting system (based on Prophet model) was analyzed during the 2021/2022 winter, under the influences of the Omicron wave of the COVID-19 pandemic.

Nguyen Van Hoa, Besanger Yvon

2022-Sep-01

Building SCADA, Building load forecasting, Markov corrector, Prophet, Self-updating machine learning

Public Health Public Health

Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies.

In Communications medicine

Background : Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity.

Methods : Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues.

Results : Automated analysis showed substantial agreement with human experts (Cohen's kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets).

Conclusions : Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.

Wong Nathan C K, Meshkinfamfard Sepehr, Turbé Valérian, Whitaker Matthew, Moshe Maya, Bardanzellu Alessia, Dai Tianhong, Pignatelli Eduardo, Barclay Wendy, Darzi Ara, Elliott Paul, Ward Helen, Tanaka Reiko J, Cooke Graham S, McKendry Rachel A, Atchison Christina J, Bharath Anil A

2022

Databases, Public health

General General

What changed in the cyber-security after COVID-19?

In Computers & security

This paper examines the transition in the cyber-security discipline induced by the ongoing COVID-19 pandemic. Using the classical information retrieval techniques, a more than twenty thousand documents are analyzed for the cyber content. In particular, we build the topic models using the Latent Dirichlet Allocation (LDA) unsupervised machine learning algorithm. The literature corpus is build through a uniform keyword search process made on the scholarly and the non-scholarly platforms filtered through the years 2010-2021. To qualitatively know the impact of COVID-19 pandemic on cyber-security, and perform a trend analysis of key themes, we organize the entire corpus into various (combination of) categories based on time period and whether the literature has undergone peer review process. Based on the weighted distribution of keywords in the aggregated corpus, we identify the key themes. While in the pre-COVID-19 period, the topics of cyber-threats to technology, privacy policy, blockchain remain popular, in the post-COVID-19 period, focus has shifted to challenges directly or indirectly brought by the pandemic. In particular, we observe post-COVID-19 cyber-security themes of privacy in healthcare, cyber insurance, cyber risks in supply chain gaining recognition. Few cyber-topics such as of malware, control system security remain important in perpetuity. We believe our work represents the evolving nature of the cyber-security discipline and reaffirms the need to tailor appropriate interventions by noting the key trends.

Kumar Rajesh, Sharma Siddharth, Vachhani Chirag, Yadav Nitish

2022-Jul-05

COVID-19 pandemic, Cyber-security trends, Latent Dirichlet Allocation, Topic modeling, Trend analysis, Unsupervised machine learning

General General

Factors Influencing the Utilization of Diabetes Complication Tests Under the COVID-19 Pandemic: Machine Learning Approach.

In Frontiers in endocrinology ; h5-index 55.0

Objective : There are still not enough studies on the prediction of non-utilization of a complication test or a glycated hemoglobin test for preventing diabetes complications by using large-scale community-based big data. This study identified the ratio of not taking a diabetes complication test (fundus examination and microprotein urination test) among adult diabetic patients over 19 years using a national survey conducted in South Korea and developed a model for predicting the probability of not taking a diabetes complication test based on it.

Methods : This study analyzed 25,811 subjects who responded that they had been diagnosed with diabetes by a doctor in the 2020 Community Health Survey. Outcome variables were defined as the utilization of the microprotein urination test and the fundus examination during the past year. This study developed a model for predicting the utilization of a diabetes complication test using logistic regression analysis and nomogram to understand the relationship of predictive factors on the utilization of a diabetes complication test.

Results : The results of this study confirmed that age, education level, the recognition of own blood glucose level, current diabetes treatment, diabetes management education, not conducting the glycated hemoglobin test in the past year, smoking, single-person household, subjectively good health, and living in the rural area were independently related to the non-utilization of diabetes complication test after the COVID-19 pandemic.

Conclusion : Additional longitudinal studies are required to confirm the causality of the non-utilization of diabetes complication screening tests.

Byeon Haewon

2022

COVID-19 pandemic, complication test, diabetes, fundus examination, microprotein urination test

General General

Anti-social behaviour in the coronavirus pandemic.

In Crime science

Anti-social behaviour recorded by police more than doubled early in the coronavirus pandemic in England and Wales. This was a stark contrast to the steep falls in most types of recorded crime. Why was ASB so different? Was it changes in 'traditional' ASB such as noisy neighbours, or was it ASB records of breaches of COVID-19 regulations? Further, why did police-recorded ASB find much larger early-pandemic increases than the Telephone Crime Survey for England and Wales? This study uses two approaches to address the issues. The first is a survey of police forces, via Freedom of Information requests, to determine whether COVID-regulation breaches were recorded as ASB. The second is natural language processing (NLP) used to interrogate the text details of police ASB records. We find police recording practice varied greatly between areas. We conclude that the early-pandemic increases in recorded ASB were primarily due to breaches of COVID regulations but around half of these also involved traditional forms of ASB. We also suggest that the study offers proof of concept that NLP may have significant general potential to exploit untapped police text records in ways that inform policing and crime policy.

Halford Eric, Dixon Anthony, Farrell Graham

2022

Anti-social behaviour, Antisocial behavior, Artificial intelligence, COVID-19, Natural language processing, Policing

General General

Improving medical decision-making for COVID-19 cases using clustering-based self-organizing map neural network and K-means algorithms.

In Informatics in medicine unlocked

In this study, we utilized unsupervised machine learning techniques to examine the relationship between different symptoms in cases who died of COVID-19 and cases who recovered from it. In the preprocessing phase, our data was cleared of redundancies, and the ten most important variables were selected using a filter-based technique (extra-tree classifier). In the processing step, we calculated the Silhouette, Davis Boldin (DB), and the mean intra-cluster distance measures to select the optimal number of clusters, then clustered the data using both the K-means and hierarchical clustering based on Self Organizing Map (SOM) neural network. Our results revealed that patient who died of COVID-19 had high mean values in different symptoms, but not all patients with this characteristic necessarily died. Besides, our result indicated that the patient's age is directly related to the hospital duration, and elderly patients are more likely to be assigned to the intensive care unit (ICU), but the patient's sex has the same distribution in different groups and does not correlate with other symptoms. In conclusion, our results confirmed past research. Also, this study helps physicians improve medical services by considering other important factors for treating different groups of COVID-19 patients.

Ilbeigipour Sadegh, Albadvi Amir, Akhondzadeh Noughabi Elham

2022-Jul-05

COVID-19, Clustering, Neural network, Self-organizing map, Unsupervised machine learning

General General

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers.

In Frontiers in public health

In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.

Song Xianbin, Zhu Jiangang, Tan Xiaoli, Yu Wenlong, Wang Qianqian, Shen Dongfeng, Chen Wenyu

2022

COVID-19, XGBoost, diagnostic markers, machine learning, principal component analysis

General General

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.

In Frontiers in public health

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.

Band Shahab S, Ardabili Sina, Yarahmadi Atefeh, Pahlevanzadeh Bahareh, Kiani Adiqa Kausar, Beheshti Amin, Alinejad-Rokny Hamid, Dehzangi Iman, Chang Arthur, Mosavi Amir, Moslehpour Massoud

2022

COVID-19, Internet of Things (IoT), big data, coronavirus, deep learning, information systems, internet of medical things, machine learning

General General

Insights from Incorporating Quantum Computing into Drug Design Workflows

bioRxiv Preprint

While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD.

Lau, B.; Emani, P. S.; Chapman, J.; Yao, L.; Lam, T.; Merrill, P.; Warrell, J.; Gerstein, M. B.; Lam, H.

2022-07-12

General General

AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.

In Briefings in bioinformatics

In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug-target interaction prediction. Our proposed model is inspired by sentence classification models in the field of Natural Language Processing, where the drug-target complex is treated as a sentence with relational meaning between its biochemical entities a.k.a. protein pockets and drug molecule. AttentionSiteDTI enables interpretability by identifying the protein binding sites that contribute the most toward the drug-target interaction. Results on three benchmark datasets show improved performance compared with the current state-of-the-art models. More significantly, unlike previous studies, our model shows superior performance, when tested on new proteins (i.e. high generalizability). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict the binding interactions between some candidate compounds and a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our method as an effective pre-screening tool in drug repurposing applications.

Yazdani-Jahromi Mehdi, Yousefi Niloofar, Tayebi Aida, Kolanthai Elayaraja, Neal Craig J, Seal Sudipta, Garibay Ozlem Ozmen

2022-Jul-12

Binding Sites, DTI database, DTI software, Deep learning, Machine learning, SARS-CoV-2, Self-Attention, drug–target interaction

General General

A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review.

In Chemosphere

Respiratory viruses are a serious threat to human wellbeing that can cause pandemic disease. As a result, it is critical to identify virus in a timely, sensitive, and precise manner. The present novel coronavirus-2019 (COVID-19) disease outbreak has increased these concerns. The research of developing various methods for COVID-19 virus identification is one of the most rapidly growing research areas. This review article compares and addresses recent improvements in conventional and advanced electroanalytical approaches for detecting COVID-19 virus. The popular conventional methods such as polymerase chain reaction (PCR), loop mediated isothermal amplification (LAMP), serology test, and computed tomography (CT) scan with artificial intelligence require specialized equipment, hours of processing, and specially trained staff. Many researchers, on the other hand, focused on the invention and expansion of electrochemical and/or bio sensors to detect SARS-CoV-2, demonstrating that they could show a significant role in COVID-19 disease control. We attempted to meticulously summarize recent advancements, compare conventional and electroanalytical approaches, and ultimately discuss future prospective in the field. We hope that this review will be helpful to researchers who are interested in this interdisciplinary field and desire to develop more innovative virus detection methods.

Ganesh Pattan-Siddappa, Kim Sang-Youn

2022-Jul-08

Detection, Electrochemical biosensor, Modified electrode, Point of care, Polymerase chain reaction, SARS-CoV-2

Surgery Surgery

Artificial intelligence and tele-otoscopy: A window into the future of pediatric otology.

In International journal of pediatric otorhinolaryngology ; h5-index 35.0

Telehealth in otolaryngology is gaining popularity as a potential tool for increased access for rural populations, decreased specialist wait times, and overall savings to the healthcare system. The adoption of telehealth has been dramatically increased by the COVID-19 pandemic limiting patients' physical access to hospitals and clinics. One of the key challenges to telehealth in general otolaryngology and otology specifically is the limited physical examination possible on the ear canal and middle ear. This is compounded in pediatric populations who commonly present with middle ear pathologies which can be challenging to diagnose even in the clinic. To address this need, various otoscopes have been designed to allow patients, their parents, or primary care providers to image the tympanic membrane and middle ear, and send data to otolaryngologists for review. Furthermore, the ability of these devices to capture images in digital format has opened the possibility of using artificial intelligence for quick and reliable diagnostic workup. In this manuscript, we provide a concise review of the literature regarding the efficacy of remote otoscopy, as well as recent efforts on the use of artificial intelligence in aiding otologic diagnoses.

Ezzibdeh Rami, Munjal Tina, Ahmad Iram, Valdez Tulio A

2022-Jul-06

Artificial intelligence, Machine learning, Otoscopy, Remote, Telehealth

General General

Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT.

In IEEE journal of biomedical and health informatics

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers' attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient's health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices'. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system's integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy ( 99.31 %) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.

Singh Parminder, Gaba Gurjot Singh, Kaur Avinash, Hedabou Mustapha, Gurtov Andrei

2022-Jul-11

General General

The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding.

In JMIR infodemiology

Background : Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake.

Objective : The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy.

Methods : We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of "influentialness" of Twitter accounts and identifying the "influencers," followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities.

Results : Twitter vaccine conversations were highly polarized, with different actors occupying separate "clusters." The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with "trust" in vaccines being manipulated to the political advantage of partisan actors.

Conclusions : These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process.

Hagen Loni, Fox Ashley, O’Leary Heather, Dyson DeAndre, Walker Kimberly, Lengacher Cecile A, Hernandez Raquel

COVID-19, vaccine hesitancy, social media, influential actors, Twitter, influencer

General General

An accessible infrastructure for artificial intelligence using a docker-based Jupyterlab in Galaxy

bioRxiv Preprint

Artificial intelligence (AI) programs that train on a large amount of data require powerful compute infrastructure. Jupyterlab notebook provides an excellent framework for developing AI programs but it needs to be hosted on a powerful infrastructure to enable AI programs to train on large data. An open-source, docker-based, and GPU-enabled jupyterlab notebook infrastructure has been developed that runs on the public compute infrastructure of Galaxy Europe for rapid prototyping and developing end-to-end AI projects. Using such a notebook, long-running AI model training programs can be executed remotely. Trained models, represented in a standard open neural network exchange (ONNX) format, and other resulting datasets are created in Galaxy. Other features include GPU support for faster training, git integration for version control, the option of creating and executing pipelines of notebooks, and the availability of multiple dashboards for monitoring compute resources. These features make the jupyterlab notebook highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions of COVID-19 CT scan images is reproduced using multiple features of this notebook. In addition, colabfold, a faster implementation of alphafold2, can also be accessed in this notebook to predict the 3D structure of protein sequences. Jupyterlab notebook is accessible in two ways - first as an interactive Galaxy tool and second by running the underlying docker container. In both ways, long-running training can be executed on Galaxy's compute infrastructure.

Kumar, A.; Cuccuru, G.; Gruening, B.; Backofen, R.

2022-07-11

General General

Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients.

In Mathematical biosciences and engineering : MBE

By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.

Rahman Muhammad Zia Ur, Raza Ali Hassan, AlSanad Abeer Abdulaziz, Akbar Muhammad Azeem, Liaquat Rabia, Riaz Muhammad Tanveer, AlSuwaidan Lulwah, Al-Alshaikh Halah Abdulaziz, Alsagri Hatoon S

2022-May-23

** Blynk IoT platform , SpO2 , artificial intelligence , electrocardiogram (ECG) , emergency condition , internet of things (IoT) **

General General

An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms.

In Frontiers in public health

Background : Artificial intelligence-based disease prediction models have a greater potential to screen COVID-19 patients than conventional methods. However, their application has been restricted because of their underlying black-box nature.

Objective : To addressed this issue, an explainable artificial intelligence (XAI) approach was developed to screen patients for COVID-19.

Methods : A retrospective study consisting of 1,737 participants (759 COVID-19 patients and 978 controls) admitted to San Raphael Hospital (OSR) from February to May 2020 was used to construct a diagnosis model. Finally, 32 key blood test indices from 1,374 participants were used for screening patients for COVID-19. Four ensemble learning algorithms were used: random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). Feature importance from the perspective of the clinical domain and visualized interpretations were illustrated by using local interpretable model-agnostic explanations (LIME) plots.

Results : The GBDT model [area under the curve (AUC): 86.4%; 95% confidence interval (CI) 0.821-0.907] outperformed the RF model (AUC: 85.7%; 95% CI 0.813-0.902), AdaBoost model (AUC: 85.4%; 95% CI 0.810-0.899), and XGBoost model (AUC: 84.9%; 95% CI 0.803-0.894) in distinguishing patients with COVID-19 from those without. The cumulative feature importance of lactate dehydrogenase, white blood cells, and eosinophil counts was 0.145, 0.130, and 0.128, respectively.

Conclusions : Ensemble machining learning (ML) approaches, mainly GBDT and LIME plots, are efficient for screening patients with COVID-19 and might serve as a potential tool in the auxiliary diagnosis of COVID-19. Patients with higher WBC count, higher LDH level, or higher EOT count, were more likely to have COVID-19.

Gong Houwu, Wang Miye, Zhang Hanxue, Elahe Md Fazla, Jin Min

2022

COVID-19, artificial intelligence, disease prediction, ensemble learning, explainable

General General

Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans.

In Computational intelligence and neuroscience

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.

Kaushik Bhavana, Koundal Deepika, Goel Neelam, Zaguia Atef, Belay Assaye, Turabieh Hamza

2022

General General

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology.

In Environmental research ; h5-index 67.0

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Rauch Wolfgang, Schenk Hannes, Insam Heribert, Markt Rudolf, Kreuzinger Norbert

2022-Jul-04

Data modelling, Forecast, Regression, SARS-CoV-2, Smoothing, Wastewater-based epidemiology

Internal Medicine Internal Medicine

Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients.

In Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

OBJECTIVES : Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result.

METHODS : A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels.

RESULTS : Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively.

CONCLUSIONS : To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.

Fatima Noreen, Mento Federico, Zanforlin Alessandro, Smargiassi Andrea, Torri Elena, Perrone Tiziano, Demi Libertario

2022-Jul-07

COVID-19, SARS-CoV-2, interobserver agreement, lung ultrasound

General General

The Severity of Depression, Anxiety, and Stress: Recommendations From Joint Work of Research Center and Psychology Clinics in COVID-19 Pandemic.

In Frontiers in psychiatry

The COVID-19 pandemic has significantly affected the psychological stability of general population of Pakistan. However, research on the severity of COVID-19 induced depression, anxiety, and stress (DAS) in Pakistan is scarce. This paper thereby investigates the severity of COVID-19 induced DAS based on demographic, socioeconomic, and personal feeling variables by modeling DAS. Snowball sampling strategy was adopted to conduct online survey from July 03, 2021 to July 09, 2021. Out of 2,442, 2,069 responses from Karachi were included. Descriptive and inferential statistics (binary and multinomial logistic regression analysis) were performed using SPSS V21 (IBM, 2013) to identify significant determinants and their association with DAS severity. The result of this study indicates 27.8, 21.7, and 18.3% respondents suffer from severe and extremely severe states of depression, anxiety, and stress, respectively. Binary logistic regression revealed that age is a significant determinant with odds of having 4.72 (95% CI = 1.86-11.97) and 5.86 (95% CI = 2.26-15.2) times greater depression, and stress for respondents aged 19-26 years. Moreover, gender-based difference is also observed with females 1.34 (95% CI = 1.08-1.68) and 1.75 (95% CI = 1.40-2.20) times more likely to exhibit anxiety and stress than males. Furthermore, marital status is a significant determinant of depression with odds of having depression is 0.67 (95% CI = 0.48-0.93) times greater for married population. Multinomial logistic regression revealed that those who believe COVID-19 pandemic has affected them mentally, fear new COVID-19 cases and deaths, depressed due to imposition of lockdown, believe they will not survive COVID-19 infection, and spend more time on social media gathering COVID-19 updates suffer from extremely severe state of depression (OR mental-effect-of-pandemic = 3.70, OR new-COVID-19-cases-and-deaths = 2.20, OR imposition-of-lockdown = 17.77, OR survival-probability = 8.17, OR time-on-social-media = 9.01), anxiety (OR mental-effect-of-pandemic = 4.78, OR new-COVID-19-cases-and-deaths = 3.52, OR imposition-of-lockdown = 5.06, OR survival-probability = 8.86, OR time-on-social-media = 5.12) and stress (OR mental-effect-of-pandemic = 6.07, OR imposition-of-lockdown = 11.38, OR survival-probability = 15.66, OR time-on-social-media = 4.39). Information regarding DAS severity will serve as a platform for research centers and psychological clinics, to work collectively and provide technology-based treatment to reduce the burden on the limited number of psychologist and psychotherapist.

Shahid Hira, Hasan Muhammad Abul, Ejaz Osama, Khan Hashim Raza, Idrees Muhammad, Ashraf Mishal, Aftab Sobia, Qazi Saad Ahmed

2022

COVID-19, anxiety, depression, mental health, stress

Public Health Public Health

Lost in machine translation: The promises and pitfalls of machine translation for multilingual group work in global health education.

In Discover education

The rapid adoption of online technologies to deliver postsecondary education amid the COVID-19 pandemic has highlighted the potential for online learning, as well as important equity gaps to be addressed. For over ten years, McMaster University has delivered graduate global health education through a blended-learning approach. In partnership with universities in the Netherlands, India, Thailand, Norway, Colombia, and Sudan, experts from across the Consortium deliver lectures online to students around the world. In 2020, two courses were piloted with small groups of students from Canada and Colombia using machine translation supported by bilingual tutors. Students met weekly via video conferencing software, speaking in English and Spanish and relying on machine translation software to transcribe and translate for group members. Qualitative semi-structured interviews were conducted with students, tutors, and instructors to explore how artificial intelligence can be harnessed to integrate multilingual group work into course offerings, challenging the dominant use of English as the principal language of instruction in global health education. Findings highlight the potential for machine translation to bridge language divides, while also underscoring several key limitations of currently available technology. Further research is needed to investigate the potential for machine translation in facilitating multilingual online education as a pathway to more equitable and inclusive online learning environments.

Hill David C, Gombay Christy, Sanchez Otto, Woappi Bethel, Romero Vélez Andrea S, Davidson Stuart, Richardson Emma Z L

2022

General General

Content-based medical image retrieval system for lung diseases using deep CNNs.

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

Content-based image retrieval (CBIR) systems are designed to retrieve images that are relevant, based on detailed analysis of latent image characteristics, thus eliminating the dependency of natural language tags, text descriptions, or keywords associated with the images. A CBIR system maintains high-level image visuals in the form of feature vectors, which the retrieval engine leverages for similarity-based matching and ranking for a given query image. In this paper, a CBIR system is proposed for the retrieval of medical images (CBMIR) for enabling the early detection and classification of lung diseases based on lung X-ray images. The proposed CBMIR system is built on the predictive power of deep neural models for the identification and classification of disease-specific features using transfer learning based models trained on standard COVID-19 Chest X-ray image datasets. Experimental evaluation on the standard dataset revealed that the proposed approach achieved an improvement of 49.71% in terms of precision, averaging across various distance metrics. Also, an improvement of 26.55% was observed in the area under precision-recall curve (AUPRC) values across all subclasses.

Agrawal Shubham, Chowdhary Aastha, Agarwala Saurabh, Mayya Veena, Kamath S Sowmya

2022-Jun-30

COVID-19, Content-based image retrieval, Deep learning, Disease classification

General General

Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform.

In Biocybernetics and biomedical engineering

The COVID-19 epidemic has been causing a global problem since December 2019. COVID-19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is essential. The progression of COVID-19 lung illness has been demonstrated to be aided by chest imaging. The respiratory system is the most vulnerable component of the human body to the COVID virus. COVID can be diagnosed promptly and accurately using images from a chest X-ray and a computed tomography scan. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist venous entry, and pinpoint any new heart problems. The traditional and trending tools are physical, time-inefficient, and not more accurate. Many techniques for detecting COVID utilizing CT scan images have recently been developed, yet none of them can efficiently detect COVID at an early stage. We proposed a two-dimensional Flexible analytical wavelet transform (FAWT) based on a novel technique in this work. This method is decomposed pre-processed images into sub-bands. Then statistical-based relevant features are extracted, and principal component analysis (PCA) is used to identify robust features. After that, robust features are ranked with the help of the Student's t-value algorithm. Finally, features are applied to Least Square-SVM (RBF) for classification. According to the experimental outcomes, our model beat state-of-the-art approaches for COVID classification. This model attained better classification accuracy of 93.47%, specificity 93.34%, sensitivity 93.6% and F1-score 0.93 using tenfold cross-validation.

Patel Rajneesh Kumar, Kashyap Manish

2022-Jul-01

COVID-19, FAWT based image decomposition, Feature extraction, Image classification, Machine learning, Medical imaging

Public Health Public Health

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.

In International journal of forecasting

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.

Ray Evan L, Brooks Logan C, Bien Jacob, Biggerstaff Matthew, Bosse Nikos I, Bracher Johannes, Cramer Estee Y, Funk Sebastian, Gerding Aaron, Johansson Michael A, Rumack Aaron, Wang Yijin, Zorn Martha, Tibshirani Ryan J, Reich Nicholas G

2022-Jul-01

COVID-19, Ensemble, Epidemiology, Health forecasting, Quantile combination

General General

Infodemic and fake news - A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review.

In International journal of disaster risk reduction : IJDRR

The spread of fake news increased dramatically during the COVID-19 pandemic worldwide. This study aims to synthesize the extant literature to understand the magnitude of this phenomenon in the wake of the pandemic in 2021, focusing on the motives and sociodemographic profiles, Artificial Intelligence (AI)-based tools developed, and the top trending topics related to fake news. A scoping review was adopted targeting articles published in five academic databases (January 2021-November 2021), resulting in 97 papers. Most of the studies were empirical in nature (N = 69) targeting the general population (N = 26) and social media users (N = 13), followed by AI-based detection tools (N = 27). Top motives for fake news sharing include low awareness, knowledge, and health/media literacy, Entertainment/Pass Time/Socialization, Altruism, and low trust in government/news media, whilst the phenomenon was more prominent among those with low education, males and younger. Machine and deep learning emerged to be the widely explored techniques in detecting fake news, whereas top topics were related to vaccine, virus, cures/remedies, treatment, and prevention. Immediate intervention and prevention efforts are needed to curb this anti-social behavior considering the world is still struggling to contain the spread of the COVID-19 virus.

Balakrishnan Vimala, Ng Wei Zhen, Soo Mun Chong, Han Gan Joo, Lee Choon Jiat

2022-Aug

COVID-19, Detection, Fake news, Motives, Scoping review, Topic

Dermatology Dermatology

Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm

ArXiv Preprint

While artificial intelligence (AI) holds promise for supporting healthcare providers and improving the accuracy of medical diagnoses, a lack of transparency in the composition of datasets exposes AI models to the possibility of unintentional and avoidable mistakes. In particular, public and private image datasets of dermatological conditions rarely include information on skin color. As a start towards increasing transparency, AI researchers have appropriated the use of the Fitzpatrick skin type (FST) from a measure of patient photosensitivity to a measure for estimating skin tone in algorithmic audits of computer vision applications including facial recognition and dermatology diagnosis. In order to understand the variability of estimated FST annotations on images, we compare several FST annotation methods on a diverse set of 460 images of skin conditions from both textbooks and online dermatology atlases. We find the inter-rater reliability between three board-certified dermatologists is comparable to the inter-rater reliability between the board-certified dermatologists and two crowdsourcing methods. In contrast, we find that the Individual Typology Angle converted to FST (ITA-FST) method produces annotations that are significantly less correlated with the experts' annotations than the experts' annotations are correlated with each other. These results demonstrate that algorithms based on ITA-FST are not reliable for annotating large-scale image datasets, but human-centered, crowd-based protocols can reliably add skin type transparency to dermatology datasets. Furthermore, we introduce the concept of dynamic consensus protocols with tunable parameters including expert review that increase the visibility of crowdwork and provide guidance for future crowdsourced annotations of large image datasets.

Matthew Groh, Caleb Harris, Roxana Daneshjou, Omar Badri, Arash Koochek

2022-07-06

Radiology Radiology

Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis

ArXiv Preprint

Coronavirus Disease 2019 (COVID-19) has spread globally and become a health crisis faced by humanity since first reported. Radiology imaging technologies such as computer tomography (CT) and chest X-ray imaging (CXR) are effective tools for diagnosing COVID-19. However, in CT and CXR images, the infected area occupies only a small part of the image. Some common deep learning methods that integrate large-scale receptive fields may cause the loss of image detail, resulting in the omission of the region of interest (ROI) in COVID-19 images and are therefore not suitable for further processing. To this end, we propose a deep spatial pyramid pooling (D-SPP) module to integrate contextual information over different resolutions, aiming to extract information under different scales of COVID-19 images effectively. Besides, we propose a COVID-19 infection detection (CID) module to draw attention to the lesion area and remove interference from irrelevant information. Extensive experiments on four CT and CXR datasets have shown that our method produces higher accuracy of detecting COVID-19 lesions in CT and CXR images. It can be used as a computer-aided diagnosis tool to help doctors effectively diagnose and screen for COVID-19.

Hongyan Xu, Dadong Wang, Arcot Sowmya

2022-07-04

General General

Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients.

In Annals of hematology ; h5-index 39.0

Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3-6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna® mRNA-1273 (74%) followed by Pfizer-BioNTech® BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin's lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna® mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.

Piñana José Luis, Rodríguez-Belenguer Pablo, Caballero Dolores, Martino Rodrigo, Lopez-Corral Lucia, Terol María-José, Vazquez Lourdes, Calabuig Marisa, Sanz-Linares Gabriela, Marin-Jimenez Francisca, Alonso Carmen, Montoro Juan, Ferrer Elena, Facal Ana, Pascual María-Jesús, Rodriguez-Fernandez Alicia, Olave María T, Cascales-Hernandez Almudena, Gago Beatriz, Hernández-Rivas José-Ángel, Villalon Lucia, Corona Magdalena, Roldán-Pérez Alicia, Ribes-Amoros Julia, González-Santillana Clara, Garcia-Sanz Ramon, Navarro David, Serrano-López Antonio J, Cedillo Ángel, Soria-Olivas Emilio, Sureda Anna, Solano Carlos

2022-Jul-02

Allogeneic stem cell transplantation, Autologous stem cell transplantation, Bayesian Networks, CAR-T therapy, COVID-19, Chronic lymphocytic leukemia, Hematological malignancies, Immunocompromised patients, Moderna mRNA-1273, Non-Hodgkin lymphoma, Pfizer-BioNTech BNT162b2, Probabilistic graphical models, Respiratory virus, SARS-CoV-2 vaccines, mRNA vaccine

Radiology Radiology

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.

In Computers in biology and medicine

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.

Liu Jia, Qi Jing, Chen Wei, Nian Yongjian

2022-Jun-15

Auxiliary learning, Deep learning, Feature fusion, Multi-task learning, Pneumonia

Radiology Radiology

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

In European radiology ; h5-index 62.0

OBJECTIVES : While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR.

METHODS : A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases.

RESULTS : RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001).

CONCLUSION : An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR.

KEY POINTS : • An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

Kuo Michael D, Chiu Keith W H, Wang David S, Larici Anna Rita, Poplavskiy Dmytro, Valentini Adele, Napoli Alessandro, Borghesi Andrea, Ligabue Guido, Fang Xin Hao B, Wong Hing Ki C, Zhang Sailong, Hunter John R, Mousa Abeer, Infante Amato, Elia Lorenzo, Golemi Salvatore, Yu Leung Ho P, Hui Christopher K M, Erickson Bradley J

2022-Jul-02

Artificial intelligence, COVID-19, Public health, Radiology, Thoracic

General General

Sequence-assignment validation in cryo-EM models with checkMySequence.

In Acta crystallographica. Section D, Structural biology

The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.

Chojnowski Grzegorz

2022-Jul-01

checkMySequence, cryo-EM, model validation, register shifts, sequence assignment

Public Health Public Health

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing (NLP)-based pipeline to understand public perceptions of and stances on COVID-19-related drugs on Twitter across time.

METHODS : This retrospective study included 609,189 US-based tweets between January 29th, 2020 and November 30th, 2021 on four drugs that gained wide public attention during the COVID-19 pandemic: 1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and 2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people's stances on each drug.

RESULTS : Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the two major US political parties was significantly different (p < 0.001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%).

CONCLUSION : Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design "targeted" strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.

Hua Yining, Jiang Hang, Lin Shixu, Yang Jie, Plasek Joseph M, Bates David W, Zhou Li

2022-Jul-01

COVID-19, Deep Learning, Drug Safety, Natural Language Processing, Public Health, Social Media

General General

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning.

In Medical decision making : an international journal of the Society for Medical Decision Making

BACKGROUND : Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information.

METHODS : We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth.

RESULTS : Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond.

LIMITATIONS : The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control.

CONCLUSIONS : The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy.

HIGHLIGHTS : We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000.We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era.We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19.We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.

Guo Xudong, Chen Peiyu, Liang Shihao, Jiao Zengtao, Li Linfeng, Yan Jun, Huang Yadong, Liu Yi, Fan Wenhui

2022-Jul-01

COVID-19, SARS-CoV-2, agent-based modeling, artificial intelligence, epidemic simulation, health policy, infectious disease, nonpharmaceutical interventions, pandemic control, reinforcement learning

Public Health Public Health

Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter.

In Studies in health technology and informatics ; h5-index 23.0

In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving library. Without TorchServe, inference time was three times faster when the model was loaded once in memory compared when the model was loaded each time. The preloaded model without TorchServe presented comparable inference time with the TorchServe instance. However, using a PyTorch preloaded model in a web application without TorchServe would necessitate to implement functionalities already present in TorchServe.

Guerdoux Guillaume, Tiffet Théophile, Bousquet Cedric

2022-Jun-29

Artificial Intelligence, COVID-19, MLOps, Social Media, Vaccines

Public Health Public Health

Comparison of Non-AI and AI-Enabled M-Health Platforms for COVID-19 Self Screening in Indonesia.

In Studies in health technology and informatics ; h5-index 23.0

This study aimed to analyze and differentiate the role of AI and no AI-supported m-health platforms for COVID-19 self-screening in Indonesia. We utilized a mysterious shopping method to develop four standardized cases with various severity levels of COVID-19 tested in Indonesia's most popular mHealth platforms. We selected seven apps from the top 200 free mHealth apps in the "Medical" category in the Google Play Store equipped with COVID-19 symptom checkers. A total of 36 teleconsultations were performed in four chatbot-based, two apps supported with AI combined with a human-based approach, and three apps with the human-based process. Teleconsultations were recorded, classified, and analyzed compared with the COVID-19 guideline by the MoH of Indonesia. The study indicated that most of the self-screening provided questions that had consistently led to the COVID-19 condition such as cough, fever, and shortness of breath and followed the guideline from the national health authority.

Andriani Sekar Putri, Adhyanacarira Padmanaba, Fuad Anis, Pertiwi Ariani Arista Putri

2022-Jun-29

COVID-19, artificial intelligence, mHealth apps, self-screening

General General

Health-Related Content in Transformer-Based Deep Neural Network Language Models: Exploring Cross-Linguistic Syntactic Bias.

In Studies in health technology and informatics ; h5-index 23.0

This paper explores a methodology for bias quantification in transformer-based deep neural network language models for Chinese, English, and French. When queried with health-related mythbusters on COVID-19, we observe a bias that is not of a semantic/encyclopaedical knowledge nature, but rather a syntactic one, as predicted by theoretical insights of structural complexity. Our results highlight the need for the creation of health-communication corpora as training sets for deep learning.

Samo Giuseppe, Bonan Caterina, Si Fuzhen

2022-Jun-29

COVID-19, Corpora, Knowledge Reproduction, Language Models, Natural Language Processing

General General

Federated Learning and Internet of Medical Things - Opportunities and Challenges.

In Studies in health technology and informatics ; h5-index 23.0

The recent advancements in artificial intelligence (AI) and the Internet of Medical Things (IoMT) have opened new horizons for healthcare technology. AI models, however, rely on large data that must be shared with the centralized entity developing the model. Data sharing leads to privacy preservation and legal issues. Federated Learning (FL) enables the training of AI models on distributed data. Hence, a large amount of IoMT data can be put into use without the need for sharing the data. This paper presents the opportunities offered by FL for privacy preservation in IoMT data. With FL, the complicated dynamics and agreements for data-sharing can be avoided. Furthermore, it describes the use cases of FL in facilitating collaborative efforts to develop AI for COVID-19 diagnosis. Since handling data from multiple sites poses its challenges, the paper also highlights the critical challenges associated with FL developments for IoMT data. Addressing these challenges will lead to gaining maximum benefit from data-driven AI technologies in IoMT.

Ali Hazrat, Alam Tanvir, Househ Mowafa, Shah Zubair

2022-Jun-29

Artificial Intelligence, COVID-19, Federated Learning, Healthcare, Internet of Medical Things, Privacy

Public Health Public Health

The Impact of COVID-19 on Mental Health Services in Scotland, UK.

In Studies in health technology and informatics ; h5-index 23.0

There is a global emergency in relation to mental health (MH) and healthcare. In the UK each year, 1 in 4 people will experience MH problems. Healthcare services are increasingly oversubscribed, and COVID-19 has deepened the healthcare gap. We investigated the effect of COVID-19 on waiting times for MH services in Scotland. We used national registers of MH services provided by Public Health Scotland. The results show that waiting times for adults and children increased drastically during the pandemic. This was seen nationally and across most of the administrative regions of Scotland. We find, however, that child and adolescent services were comparatively less impacted by the pandemic than adult services. This is potentially due to prioritisation of paediatric patients, or due to an increasing demand on adult services triggered by the pandemic itself.

Cooke Elizabeth A, Lemanska Agnieszka, Livings Jennifer, Thomas Spencer A

2022-Jun-29

COVID-19, Deep Learning, Mental Health, Visualization

General General

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review.

In Studies in health technology and informatics ; h5-index 23.0

Covid-19 is one of the most significant infectious diseases that have faced humanity in the past century from clinical, economic, and social perspectives. Although the role of infectious diseases in human history has been vicious and is well known to humanity, Covid-19 is a special case since it is the first worldwide outbreak in the era of advanced computing and telecommunications. For this reason, it was only logical to see Artificial Intelligence (AI) and Machine Learning (ML) on the top of the list of controls to compact the spread of Covid-19. This paper goes through the applications of AI and ML that were reported in some of the major literature indexes and can be related to the main issues that face healthcare providers during the Covid-19 pandemic. This paper also discusses the applicability of these applications to healthcare organizations and points out the main prerequisites before they can be adopted.

Aldhoayan Mohammed D

2022-Jun-29

Artificial Intelligence, Covid-19, Healthcare, Machine Learning

General General

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.

In Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Ahmed Faheem, Soomro Afaque Manzoor, Chethikkattuveli Salih Abdul Rahim, Samantasinghar Anupama, Asif Arun, Kang In Suk, Choi Kyung Hyun

2022-Jun-28

AI on networks, Deep learning, Drug repurposing, Machine learning, Network analysis, Network diffusion, Network proximity

General General

PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

In Database : the journal of biological databases and curation

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.

Le Tuan-Dung, Nguyen Phuong D, Korkin Dmitry, Thieu Thanh

2022-Jun-30

General General

iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model.

In Briefings in bioinformatics

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.

Kurata Hiroyuki, Tsukiyama Sho, Manavalan Balachandran

2022-Jul-01

anti-coronavirus peptide, bioinformatics, deep learning, random forest, transformer, word2vec

General General

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data.

In PloS one ; h5-index 176.0

COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people's perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as 'recovered', 'not recovered', and 'survived'. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

Saad Eysha, Sadiq Saima, Jamil Ramish, Rustam Furqan, Mehmood Arif, Choi Gyu Sang, Ashraf Imran

2022

Pathology Pathology

A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels.

In IEEE transactions on medical imaging ; h5-index 74.0

The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.

Hochberg Dana Cohen, Greenspan Hayit, Giryes Raja

2022-Jun-29

General General

Source code Optimized Parallel Inception: A fast COVID-19 screening software.

In Software impacts

COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. These two viruses have the same symptoms and occur at a collision timeline. Optimized Parallel Inception (OPI) presents a new strategy to screen the COVID-19 from H1N1 with use of only symptoms. In this paper the process of preprocessing, screening, and specifying feature importance by OPI and particle swarm optimization is presented. Experimental results indicate 98.88 accuracy for screening COVID-19, H1N1, and Neither COVID-19 Nor H1N1.

Tavakolian Alireza, Hajati Farshid, Rezaee Alireza, Fasakhodi Amirhossein Oliaei, Uddin Shahadat

2022-Jun-22

COVID-19, Coronavirus, Deep learning, H1N1 virus, Outbreak, Screening

General General

Forecasting new diseases in low-data settings using transfer learning.

In Chaos, solitons, and fractals

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a synthetic approach. Using data from Brazil, we compare how well different machine learning models transfer knowledge between two different dataset pairs: case counts of (i) dengue and Zika, and (ii) influenza and COVID-19. In the synthetic analysis, we generate data with an SIR model using different transmission and recovery rates, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers for pandemic response.

Roster Kirstin, Connaughton Colm, Rodrigues Francisco A

2022-Aug

COVID-19, Epidemic forecasting, Machine learning, Transfer learning, Zika

Public Health Public Health

COVID-19 severity detection using machine learning techniques from CT-images.

In Evolutionary intelligence

COVID-19 has spread worldwide and the World Health Organization was forced to list it as a Public Health Emergency of International Concern. The disease has severely impacted most of the people because it affects the lung and causes severe breathing problems and lung infections. Differentiating other lung ailments from COVID-19 infection and determining the severity is a challenging process. Doctors can give vital life-saving services and support patients' lives only if the severity of their condition is determined. This work proposed a two-step approach for detecting the COVID-19 infection from the lung CT images and determining the severity of the patient's illness. To extract the features, pre-trained models are used, and by analyzing them, integrated the features from AlexNet, DenseNet-201, and ResNet-50. The COVID-19 detection is carried out by using an Artificial Neural Network(ANN) model. After the COVID-19 infection has been identified, severity detection is performed. For that, image features are combined with the clinical data and is classified as High, Moderate, Low with the help of Cubic Support Vector Machine(SVM). By considering three severity levels, patients with high risk can be given more attention. The method was tested on a publicly available dataset and obtained an accuracy of 92.0%, sensitivity of 96.0%, and an F1-Score of 91.44% for COVID-19 detection and got overall accuracy of 90.0% for COVID-19 severity detection for three classes.

Aswathy A L, Anand Hareendran S, Chandra S S Vinod

2022-Jun-24

AlexNet, Computed tomography, DenseNet-201, Neural network, ResNet-50, Transfer learning

General General

A Semi-Supervised Learning Approach for COVID-19 Detection from Chest CT Scans.

In Neurocomputing

COVID-19 has spread rapidly all over the world and has infected more than 200 countries and regions. Early screening of suspected infected patients is essential for preventing and combating COVID-19. Computed Tomography (CT) is a fast and efficient tool which can quickly provide chest scan results. To reduce the burden on doctors of reading CTs, in this article, a high precision diagnosis algorithm of COVID-19 from chest CTs is designed for intelligent diagnosis. A semi-supervised learning approach is developed to solve the problem when only small amount of labelled data is available. While following the MixMatch rules to conduct sophisticated data augmentation, we introduce a model training technique to reduce the risk of model over-fitting. At the same time, a new data enhancement method is proposed to modify the regularization term in MixMatch. To further enhance the generalization of the model, a convolutional neural network based on an attention mechanism is then developed that enables to extract multi-scale features on CT scans. The proposed algorithm is evaluated on an independent CT dataset of the chest from COVID-19 and achieves the area under the receiver operating characteristic curve (AUC) value of 0.932, accuracy of 90.1%, sensitivity of 91.4%, specificity of 88.9%, and F1-score of 89.9%. The results show that the proposed algorithm can accurately diagnose whether a chest CT belongs to a positive or negative indication of COVID-19, and can help doctors to diagnose rapidly in the early stages of a COVID-19 outbreak.

Zhang Yong, Su Li, Liu Zhenxing, Tan Wei, Jiang Yinuo, Cheng Cheng

2022-Jun-23

Attention mechanisms, COVID-19, Computed tomography, Deep learning, Semi-supervised learning

General General

Lung Ultrasound to Assist ICU Admission Decision-Making Process of COVID-19 Patients With Acute Respiratory Failure.

In Critical care explorations

** : There is only low-certainty evidence on the use of predictive models to assist COVID-19 patient's ICU admission decision-making process. Accumulative evidence suggests that lung ultrasound (LUS) assessment of COVID-19 patients allows accurate bedside evaluation of lung integrity, with the added advantage of repeatability, absence of radiation exposure, reduced risk of virus dissemination, and low cost. Our goal is to assess the performance of a quantified indicator resulting from LUS data compared with standard clinical practice model to predict critical respiratory illness in the 24 hours following hospital admission.

DESIGN : Prospective cohort study.

SETTING : Critical Care Unit from University Hospital Purpan (Toulouse, France) between July 2020 and March 2021.

PATIENTS : Adult patients for COVID-19 who were in acute respiratory failure (ARF), defined as blood oxygen saturation as measured by pulse oximetry less than 90% while breathing room air or respiratory rate greater than or equal to 30 breaths/min at hospital admission. Linear multivariate models were used to identify factors associated with critical respiratory illness, defined as death or mild/severe acute respiratory distress syndrome (Pao2/Fio2 < 200) in the 24 hours after patient's hospital admission.

INTERVENTION : LUS assessment.

MEASUREMENTS AND MAIN RESULTS : One hundred and forty COVID-19 patients with ARF were studied. This cohort was split into two independent groups: learning sample (first 70 patients) and validation sample (last 70 patients). Interstitial lung water, thickening of the pleural line, and alveolar consolidation detection were strongly associated with patient's outcome. The LUS model predicted more accurately patient's outcomes than the standard clinical practice model (DeLong test: Testing: z score = 2.50, p value = 0.01; Validation: z score = 2.11, p value = 0.03).

CONCLUSIONS : LUS assessment of COVID-19 patients with ARF at hospital admission allows a more accurate prediction of the risk of critical respiratory illness than standard clinical practice. These results hold the promise of improving ICU resource allocation process, particularly in the case of massive influx of patients or limited resources, both now and in future anticipated pandemics.

Aguersif Amazigh, Sarton Benjamine, Bouharaoua Sihem, Gaillard Lucien, Standarovski Denis, Faucoz Orphée, Martin Blondel Guillaume, Khallel Hatem, Thalamas Claire, Sommet Agnes, Riu Béatrice, Morand Eric, Bataille Benoit, Silva Stein

2022-Jun

COVID-19, acute respiratory distress syndrome, acute respiratory failure, intensive care unit admission decision-making, lung ultrasound, machine learning

General General

Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19.

In Applied soft computing

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.

Mar-Cupido Ricardo, García Vicente, Rivera Gilberto, Sánchez J Salvador

2022-Jun-23

COVID-19, Deep learning, Face mask, Recognition, Transfer learning

General General

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables.

In ERJ open research

Background : Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs.

Methods : This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.

Results : 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (F iO2 ) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, F iO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19.

Conclusions : This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU.

Munera Nicolás, Garcia-Gallo Esteban, Gonzalez Álvaro, Zea José, Fuentes Yuli V, Serrano Cristian, Ruiz-Cuartas Alejandra, Rodriguez Alejandro, Reyes Luis F

2022-Apr

General General

Custom Pretrainings and Adapted 3D-ConvNeXt Architecture for COVID Detection and Severity Prediction

ArXiv Preprint

Since COVID strongly affects the respiratory system, lung CT scans can be used for the analysis of a patients health. We introduce an neural network for the prediction of the severity of lung damage and the detection of infection using three-dimensional CT-scans. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we introduce different pretraining methods specifically adjusted to improve the models ability to handle three-dimensional CT-data. In order to test the performance of our model, we participate in the 2nd COV19D Competition for severity prediction and infection detection.

Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart

2022-06-30

General General

De Novo design of potential inhibitors against SARS-CoV-2 Mpro.

In Computers in biology and medicine

The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds.

Li Shimeng, Wang Lianxin, Meng Jinhui, Zhao Qi, Zhang Li, Liu Hongsheng

2022-Jun-15

Deep learning, Molecular dynamics simulation, Transfer learning, Virtual screening, de novo drug design

Radiology Radiology

Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study.

In Journal of cardiovascular medicine (Hagerstown, Md.)

BACKGROUND : Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission.

METHODS AND RESULTS : We studied an Italian cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 who were hospitalized in 13 cardiology units during Spring 2020. The Lasso procedure was used to select the most relevant covariates. The dataset was randomly divided into a training set containing 80% of the data, used for estimating the model, and a test set with the remaining 20%. A Random Forest modeled in-hospital mortality with the selected set of covariates: its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity and related 95% confidence interval (CI). This model was then compared with the one obtained by the Gradient Boosting Machine (GBM) and with logistic regression. Finally, to understand if each model has the same performance in the training and test set, the two AUCs were compared using the DeLong's test. Among 701 patients enrolled (mean age 67.2 ± 13.2 years, 69.5% male individuals), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9-24) days. Variables selected by the Lasso procedure were: age, oxygen saturation, PaO2/FiO2, creatinine clearance and elevated troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, lower creatinine clearance levels and higher prevalence of elevated troponin (all P < 0.001). The best performance out of the samples was provided by Random Forest with an AUC of 0.78 (95% CI: 0.68-0.88) and a sensitivity of 0.88 (95% CI: 0.58-1.00). Moreover, Random Forest was the unique model that provided similar performance in sample and out of sample (DeLong test P = 0.78).

CONCLUSION : In a large COVID-19 population, we showed that a customizable machine learning-based score derived from clinical variables is feasible and effective for the prediction of in-hospital mortality.

Vezzoli Marika, Inciardi Riccardo Maria, Oriecuia Chiara, Paris Sara, Murillo Natalia Herrera, Agostoni Piergiuseppe, Ameri Pietro, Bellasi Antonio, Camporotondo Rita, Canale Claudia, Carubelli Valentina, Carugo Stefano, Catagnano Francesco, Danzi Giambattista, Dalla Vecchia Laura, Giovinazzo Stefano, Gnecchi Massimiliano, Guazzi Marco, Iorio Anita, La Rovere Maria Teresa, Leonardi Sergio, Maccagni Gloria, Mapelli Massimo, Margonato Davide, Merlo Marco, Monzo Luca, Mortara Andrea, Nuzzi Vincenzo, Pagnesi Matteo, Piepoli Massimo, Porto Italo, Pozzi Andrea, Provenzale Giovanni, Sarullo Filippo, Senni Michele, Sinagra Gianfranco, Tomasoni Daniela, Adamo Marianna, Volterrani Maurizio, Maroldi Roberto, Metra Marco, Lombardi Carlo Mario, Specchia Claudia

2022-Jul-01

General General

Diagnostic and surgical innovations in otolaryngology for adult and paediatric patients during the COVID-19 era.

In Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale

** : During the Coronavirus Disease 2019 (COVID-19) pandemic, otolaryngology has been shown to be a high-risk specialty due to the exposure to aerosol-generating physical examinations, procedures and surgical interventions on the head and neck area, both in adult and paediatric patients. This has prompted the issue of updating the guidelines by International Health Authorities in the Ear Nose and Throat (ENT) field and, at the same time, has stimulated engineers and healthcare professionals to develop new devices and technologies with the aim of reducing the risk of contamination for physicians, nurses and patients.

Methods : A review of the literature published on PubMed, Ovid/Medline and Scopus databases was performed from January 01, 2020 to December 31, 2021.

Results : 73 articles were eligible to be included, which were subdivided into 4 categories: ("Artificial Intelligence (AI)"; "Personal Protective Equipment (PPE)"; "Diagnostic tools"; "Surgical tools").

Conclusions : All of the innovations that have been developed during the COVID-19 pandemic have laid the foundation for a radical technological change of society, not only in medicine but also from a social, political and economical points of view that will leave its mark in the coming decades.

Petrone Paolo, Birocchi Emanuela, Miani Cesare, Anzivino Roberta, Sciancalepore Pasqua Irene, Di Mauro Antonio, Dalena Paolo, Russo Cosimo, De Ceglie Vincenzo, Masciavè Maurizio, Fiorella Maria Luisa

2022-Apr

COVID-19, SARS-CoV-2, adult, otolaryngology, pediatric, technology

General General

What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning.

In AI & society

It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle. To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources. The conducted analysis not only does attest to the completeness of our knowledge concerning the drivers of undeclared work but also paves the way for wide usage of artificial intelligence in monitoring and confronting this detrimental practice. The study, however, exposes the necessity of having at disposal considerably larger datasets compared to those currently available if successful real-world applications of machine learning are to be achieved in this field. Alongside the apparent theoretical contribution, this paper is thus also expected to be of particular importance for policymakers, whose efforts to tackle tax evasion will have to be expedited in the period after the COVID-19 pandemic.

Franic Josip

2022-Jun-23

Artificial intelligence, EU, Informal economy, Machine learning, Tax evasion, Undeclared work

General General

Predicting the antigenic evolution of SARS-COV-2 with deep learning

bioRxiv Preprint

The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution via in silico directed evolution. As demonstrated by existing SARS-COV-2 variants, MLEAP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLEAP are also found in immunocompromised covid patients and newly emerging variants, like BA. 4/5. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants.

Han, W.; Chen, N.; Sun, S.; Gao, X.

2022-06-29

General General

A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter

ArXiv Preprint

Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. Fast and accurate grasp of public attitudes toward vaccination is critical to address vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualisation of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes of public COVID-19 vaccination attitudes.

Ninghan Chen, Xihui Chen, Jun Pang

2022-06-27

General General

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

In Computers in biology and medicine

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal monitoring increased the number of preventable deaths or pregnancy complications. One solution is using Artificial Intelligence to help the medical personnel establish the diagnosis in a faster and more accurate manner. Deep learning is the state-of-the-art solution for image classification. Researchers manually design the structure of fix deep learning neural networks structures and afterwards verify their performance. The goal of this paper is to propose a potential method for learning deep network architectures automatically. As the number of networks architectures increases exponentially with the number of convolutional layers in the network, we propose a differential evolution algorithm to traverse the search space. At first, we propose a way to encode the network structure as a candidate solution of fixed-length integer array, followed by the initialization of differential evolution method. A set of random individuals is generated, followed by mutation, recombination, and selection. At each generation the individuals with the poorest loss values are eliminated and replaced with more competitive individuals. The model has been tested on three cancer datasets containing MRI scans and histopathological images and two maternal-fetal screening ultrasound images. The novel proposed method has been compared and statistically benchmarked to four state-of-the-art deep learning networks: VGG16, ResNet50, Inception V3, and DenseNet169. The experimental results showed that the model is competitive to other state-of-the-art models, obtaining accuracies between 78.73% and 99.50% depending on the dataset it had been applied on.

Belciug Smaranda

2022-Jul

Cancer MRI scan, Cancer histopathological image, Deep learning, Differential evolution, Maternal-fetal ultrasound, Statistical assessment

Radiology Radiology

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

In Computers in biology and medicine

BACKGROUND : COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.

METHOD : ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.

RESULTS : Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.

CONCLUSIONS : Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

Agarwal Mohit, Agarwal Sushant, Saba Luca, Chabert Gian Luca, Gupta Suneet, Carriero Alessandro, Pasche Alessio, Danna Pietro, Mehmedovic Armin, Faa Gavino, Shrivastava Saurabh, Jain Kanishka, Jain Harsh, Jujaray Tanay, Singh Inder M, Turk Monika, Chadha Paramjit S, Johri Amer M, Khanna Narendra N, Mavrogeni Sophie, Laird John R, Sobel David W, Miner Martin, Balestrieri Antonella, Sfikakis Petros P, Tsoulfas George, Misra Durga Prasanna, Agarwal Vikas, Kitas George D, Teji Jagjit S, Al-Maini Mustafa, Dhanjil Surinder K, Nicolaides Andrew, Sharma Aditya, Rathore Vijay, Fatemi Mostafa, Alizad Azra, Krishnan Pudukode R, Yadav Rajanikant R, Nagy Frence, Kincses Zsigmond Tamás, Ruzsa Zoltan, Naidu Subbaram, Viskovic Klaudija, Kalra Manudeep K, Suri Jasjit S

2022-Jul

AI, COVID-19, Deep learning, Glass ground opacities, Hounsfield units, Lung CT, Lung segmentation, Pruning

General General

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.

In Computers in biology and medicine

OBJECTIVE : To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

MATERIAL AND METHODS : Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

RESULTS : The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

CONCLUSION : The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

de Fátima Cobre Alexandre, Surek Monica, Stremel Dile Pontarolo, Fachi Mariana Millan, Lobo Borba Helena Hiemisch, Tonin Fernanda Stumpf, Pontarolo Roberto

2022-Jul

Biomarker, COVID 19, Diagnosis, Fatality, Machine learning, Severity

General General

One-step colorimetric isothermal detection of COVID-19 with AI-assisted automated result analysis: A platform model for future emerging point-of-care RNA/DNA disease diagnosis.

In Talanta

Colorimetric loop-mediated DNA isothermal amplification-based assays have gained momentum in the diagnosis of COVID-19 owing to their unmatched feasibility in low-resource settings. However, the vast majority of them are restricted to proprietary pH-sensitive dyes that limit downstream assay optimization or hinder efficient result interpretation. To address this problem, we developed a novel dual colorimetric RT-LAMP assay using in-house pH-dependent indicators to maximize the visual detection and assay simplicity, and further integrated it with the artificial intelligence (AI) operated tool (RT-LAMP-DETR) to enable a more precise and rapid result analysis in large scale testing. The dual assay leverages xylenol orange (XO) and a newly formulated lavender green (LG) dye for distinctive colorimetric readouts, which enhance the test accuracy when performed and analyzed simultaneously. Our RT-LAMP assay has a detection limit of 50 viral copies/reaction with the cycle threshold (Ct) value ≤ 39.7 ± 0.4 determined by the WHO-approved RT-qPCR assay. RT-LAMP-DETR exhibited a complete concordance with the results from naked-eye observation and RT-qPCR, achieving 100% sensitivity, specificity, and accuracy that altogether render it suitable for ultrasensitive point-of-care COVID-19 screening efforts. From the perspective of pandemic preparedness, our method offers a simpler, faster, and cheaper (∼$8/test) approach for COVID-19 testing and other emerging pathogens with respect to RT-qPCR.

Jaroenram Wansadaj, Chatnuntawech Itthi, Kampeera Jantana, Pengpanich Sukanya, Leaungwutiwong Pornsawan, Tondee Benyatip, Sirithammajak Sarawut, Suvannakad Rapheephat, Khumwan Pakapreud, Dangtip Sirintip, Arunrut Narong, Bantuchai Sirasate, Nguitragool Wang, Wongwaroran Suchawit, Khanchaitit Paisan, Sattabongkot Jetsumon, Teerapittayanon Surat, Kiatpathomchai Wansika

2022-Mar-10

Colorimetric RT-LAMP, LAMP-DETR, Machine learning, SARS-CoV-2

General General

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review.

In IEEE reviews in biomedical engineering

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decision-making. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.

Giuste Felipe, Shi Wenqi, Zhu Yuanda, Naren Tarun, Isgut Monica, Sha Ying, Tong Li, Gupte Mitali, D Wang May

2022-Jun-23

General General

Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning.

In Tomography (Ann Arbor, Mich.)

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass opacity (GGO) and pulmonary infiltrates (PIs) in CT image sequences of COVID-19 cases. Convolutional neural networks (CNNs) were used to classify the disease progression of pneumonia. Each GGO and PI pattern was highlighted with saliency map fusion, and the resulting map was used to train and test a CNN classification scheme with three classes. In addition to patients, this information was shared between the respiratory triage/radiologist and the COVID-19 multidisciplinary teams with the application so that the severity of the disease could be understood through CT and medical diagnosis. The three-class, disease-level COVID-19 classification results exhibited a macro-precision of more than 94.89% in a two-fold cross-validation. Both the segmentation and classification results were comparable to those made by a medical specialist.

Tello-Mijares Santiago, Woo Fomuy

2022-Jun-20

COVID-19, SARS-CoV-2, artificial intelligence, computed tomography, medical diagnostic imaging

General General

In Silico Virtual Screening of Marine Aldehyde Derivatives from Seaweeds against SARS-CoV-2.

In Marine drugs ; h5-index 62.0

Coronavirus disease 2019, caused by the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global pandemic that poses an unprecedented threat to the global economy and human health. Several potent inhibitors targeting SARS-CoV-2 have been published; however, most of them have failed in clinical trials. This study aimed to assess the therapeutic compounds among aldehyde derivatives from seaweeds as potential SARS-CoV-2 inhibitors using a computer simulation protocol. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of the compounds were analyzed using a machine learning algorithm, and the docking simulation of these compounds to the 3C-like protease (Protein Data Bank (PDB) ID: 6LU7) was analyzed using a molecular docking protocol based on the CHARMm algorithm. These compounds exhibited good drug-like properties following the Lipinski and Veber rules. Among the marine aldehyde derivatives, 4-hydroxybenzaldehyde, 3-hydroxybenzaldehyde, 3,4-dihydroxybenzaldehyde, and 5-bromoprotocatechualdehyde were predicted to have good absorption and solubility levels and non-hepatotoxicity in the ADME/Tox prediction. 3-hydroxybenzaldehyde and 3,4-dihydroxybenzaldehyde were predicted to be non-toxic in TOPKAT prediction. In addition, 3,4-dihydroxybenzaldehyde was predicted to exhibit interactions with the 3C-like protease, with binding energies of -71.9725 kcal/mol. The computational analyses indicated that 3,4-dihydroxybenzaldehyde could be regarded as potential a SARS-CoV-2 inhibitor.

Kang Nalae, Heo Seong-Yeong, Cha Seon-Heui, Ahn Ginnae, Heo Soo-Jin

2022-Jun-16

Coronavirus disease 2019, SARS-CoV-2, aldehyde derivatives, in silico, seaweed, virtual screening

General General

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor.

In Biosensors

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.

Fortunati Simone, Giliberti Chiara, Giannetto Marco, Bolchi Angelo, Ferrari Davide, Donofrio Gaetano, Bianchi Valentina, Boni Andrea, De Munari Ilaria, Careri Maria

2022-Jun-17

COVID-19, IoT-WiFi, SARS-CoV-2, electrochemical immunosensor, gold nanoparticles, machine learning, point of care testing

General General

Smartphone-Based SARS-CoV-2 and Variants Detection System using Colorimetric DNAzyme Reaction Triggered by Loop-Mediated Isothermal Amplification (LAMP) with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR).

In ACS nano ; h5-index 203.0

Coronavirus disease (COVID-19) has affected people for over two years. Moreover, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has raised concerns regarding its accurate diagnosis. Here, we report a colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR), referred to as DAMPR assay for detecting SARS-CoV-2 and variants genes with attomolar sensitivity within an hour. The CRISPR-associated protein 9 (Cas9) system eliminated false-positive signals of LAMP products, improving the accuracy of DAMPR assay. Further, we fabricated a portable DAMPR assay system using a three-dimensional printing technique and developed a machine learning (ML)-based smartphone application to routinely check diagnostic results of SARS-CoV-2 and variants. Among blind tests of 136 clinical samples, the proposed system successfully diagnosed COVID-19 patients with a clinical sensitivity and specificity of 100% each. More importantly, the D614G (variant-common), T478K (delta-specific), and A67V (omicron-specific) mutations of the SARS-CoV-2 S gene were detected selectively, enabling the diagnosis of 70 SARS-CoV-2 delta or omicron variant patients. The DAMPR assay system is expected to be employed for on-site, rapid, accurate detection of SARS-CoV-2 and its variants gene and employed in the diagnosis of various infectious diseases.

Song Jayeon, Cha Baekdong, Moon Jeong, Jang Hyowon, Kim Sunjoo, Jang Jieun, Yong Dongeun, Kwon Hyung-Jun, Lee In-Chul, Lim Eun-Kyung, Jung Juyeon, Park Hyun Gyu, Kang Taejoon

2022-Jun-23

CRISPR-Cas9, SARS-CoV-2, machine learning, smartphone, variants

General General

MLBioIGE: integration and interplay of machine learning and bioinformatics approach to identify the genetic effect of SARS-COV-2 on idiopathic pulmonary fibrosis patients.

In Biology methods & protocols

SARS-CoV-2, the virus that causes COVID-19, is a current concern for people worldwide. The virus has recently spread worldwide and is out of control in several countries, putting the outbreak into a terrifying phase. Machine learning with transcriptome analysis has advanced in recent years. Its outstanding performance in several fields has emerged as a potential option to find out how SARS-CoV-2 is related to other diseases. Idiopathic pulmonary fibrosis (IPF) disease is caused by long-term lung injury, a risk factor for SARS-CoV-2. In this article, we used a variety of combinatorial statistical approaches, machine learning, and bioinformatics tools to investigate how the SARS-CoV-2 affects IPF patients' complexity. For this study, we employed two RNA-seq datasets. The unique contributions include common genes identification to identify shared pathways and drug targets, PPI network to identify hub-genes and basic modules, and the interaction of transcription factors (TFs) genes and TFs-miRNAs with common differentially expressed genes also placed on the datasets. Furthermore, we used gene ontology and molecular pathway analysis to do functional analysis and discovered that IPF patients have certain standard connections with the SARS-CoV-2 virus. A detailed investigation was carried out to recommend therapeutic compounds for IPF patients affected by the SARS-CoV-2 virus.

Tanzir Mehedi Sk, Ahmed Kawsar, Bui Francis M, Rahaman Musfikur, Hossain Imran, Tonmoy Tareq Mahmud, Limon Rakibul Alam, Ibrahim Sobhy M, Moni Mohammad Ali

2022

COVID-19, SARS-CoV-2, differentially expressed genes, gene ontology, idiopathic pulmonary fibrosis, machine learning

General General

COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls.

In Frontiers in public health

Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible.

Aslan Muhammet Fatih, Hasikin Khairunnisa, Yusefi Abdullah, Durdu Akif, Sabanci Kadir, Azizan Muhammad Mokhzaini

2022

COVID-19, HOG, SegNet, Support Vector Machine, UAV, semantic segmentation

General General

Predicting the antigenic evolution of SARS-COV-2 with deep learning

bioRxiv Preprint

The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution via in silico directed evolution. As demonstrated by existing SARS-COV-2 variants, MLEAP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLEAP are also found in immunocompromised covid patients and newly emerging variants, like BA. 4/5. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants.

Han, W.; Chen, N.; Wang, Y.; Sun, S.; Gao, X.

2022-06-24

General General

Machine learning shows that the Covid-19 pandemic is impacting U.S. public companies unequally by changing risk structures.

In PloS one ; h5-index 176.0

Covid-19 has impacted the U.S. economy and business organizations in multiple ways, yet its influence on company fundamentals and risk structures have not been fully elucidated. In this paper, we apply LDA, a mainstream topic model, to analyze the risk factor section from SEC filings (10-K and 10-Q), and describe risk structure change over the past two years. The results show that Covid-19 has transformed the risk structures U.S. companies face in the short run, exerting excessive stress on international interactions, operations, and supply chains. However, this shock has been waning since the second quarter of 2020. Our model shows that risk structure change (measured by topic distribution) from Covid-19 is a significant predictor of lower performance, but smaller companies tend to be stricken harder.

Cao Likun, Ren Jie

2022

oncology Oncology

A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity.

In La Radiologia medica

INTRODUCTION : According to the Chinese Health Commission guidelines, coronavirus disease 2019 (COVID-19) severity is classified as mild, moderate, severe, or critical. The mortality rate of COVID-19 is higher among patients with severe and critical diseases; therefore, early identification of COVID-19 prevents disease progression and improves patient survival. Computed tomography (CT) radiomics, as a machine learning method, provides an objective and mathematical evaluation of COVID-19 pneumonia. As CT-based radiomics research has recently focused on COVID-19 diagnosis and severity analysis, this meta-analysis aimed to investigate the predictive power of a CT-based radiomics model in determining COVID-19 severity.

MATERIALS AND METHODS : This study followed the diagnostic version of PRISMA guidelines. PubMed, Embase databases and the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews were searched to identify relevant articles in the meta-analysis from inception until July 16, 2021. The sensitivity and specificity were analyzed using forest plots. The overall predictive power was calculated using the summary receiver operating characteristic curve. The bias was evaluated using a funnel plot. The quality of the included literature was assessed using the radiomics quality score and quality assessment of diagnostic accuracy studies tool.

RESULTS : The radiomics quality scores ranged from 7 to 16 (achievable score: 2212 8 to 36). The pooled sensitivity and specificity were 0.800 (95% confidence interval [CI] 0.662-0.891) and 0.874 (95% CI 0.773-0.934), respectively. The pooled area under the receiver operating characteristic curve was 0.908. The quality assessment tool showed favorable results.

CONCLUSION : This meta-analysis demonstrated that CT-based radiomics models might be helpful for predicting the severity of COVID-19 pneumonia.

Kao Yung-Shuo, Lin Kun-Te

2022-Jun-22

COVID-19, Computed tomography, Meta-analysis, Radiomics, Textural

General General

Immersive media experience: a survey of existing methods and tools for human influential factors assessment.

In Quality and user experience

Virtual reality (VR) applications, especially those where the user is untethered to a computer, are becoming more prevalent as new hardware is developed, computational power and artificial intelligence algorithms are available, and wireless communication networks are becoming more reliable, fast, and providing higher reliability. In fact, recent projections show that by 2022 the number of VR users will double, suggesting the sector was not negatively affected by the worldwide COVID-19 pandemic. The success of any immersive communication system is heavily dependent on the user experience it delivers, thus now more than ever has it become crucial to develop reliable models of immersive media experience (IMEx). In this paper, we survey the literature for existing methods and tools to assess human influential factors (HIFs) related to IMEx. In particular, subjective, behavioural, and psycho-physiological methods are covered. We describe tools available to monitor these HIFs, including the user's sense of presence and immersion, cybersickness, and mental/affective states, as well as their role in overall experience. Special focus is placed on psycho-physiological methods, as it was found that such in-depth evaluation was lacking from the existing literature. We conclude by touching on emerging applications involving multiple-sensorial immersive media and provide suggestions for future research directions to fill existing gaps. It is hoped that this survey will be useful for researchers interested in building new immersive (adaptive) applications that maximize user experience.

Moinnereau Marc-Antoine, de Oliveira Alcyr Alves, Falk Tiago H

2022

Cybersickness, Immersive media experience, Mulsemedia, Quality of experience, Virtual reality

General General

COVID-19 vaccine hesitancy: a social media analysis using deep learning.

In Annals of operations research

Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.

Nyawa Serge, Tchuente Dieudonné, Fosso-Wamba Samuel

2022-Jun-16

COVID-19, Deep learning, LSTM, Neural network, Text classification, Twitter, Vaccine hesitancy

General General

SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning

bioRxiv Preprint

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1b, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the RBD domain of SARS-CoV-2. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

Shashkova, T. I.; Umerenkov, D.; Salnikov, M.; Strashnov, P. V.; Konstantinova, A. V.; Lebed, I.; Shcherbinin, D. N.; Asatryan, M. N.; Kardymon, O. L.; Ivanisenko, N. V.

2022-06-21

Radiology Radiology

A Self-Guided Framework for Radiology Report Generation

ArXiv Preprint

Automatic radiology report generation is essential to computer-aided diagnosis. Through the success of image captioning, medical report generation has been achievable. However, the lack of annotated disease labels is still the bottleneck of this area. In addition, the image-text data bias problem and complex sentences make it more difficult to generate accurate reports. To address these gaps, we pre-sent a self-guided framework (SGF), a suite of unsupervised and supervised deep learning methods to mimic the process of human learning and writing. In detail, our framework obtains the domain knowledge from medical reports with-out extra disease labels and guides itself to extract fined-grain visual features as-sociated with the text. Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through compar-ative practice. Extensive experiments demonstrate the utility of our SGF in the majority of cases, showing its superior performance over state-of-the-art meth-ods. Our results highlight the capacity of the proposed framework to distinguish fined-grained visual details between words and verify its advantage in generating medical reports.

Jun Li, Shibo Li, Ying Hu, Huiren Tao

2022-06-19

General General

De novo design of site-specific protein interactions with learned surface fingerprints

bioRxiv Preprint

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed four de novo protein binders to engage three protein targets: SARS-CoV-2 spike, PD-1, and PD-L1. The designs bound the target sites with nanomolar affinity upon experimental optimization, structural and mutational characterization showed highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Gainza, P.; Wehrle, S.; Van Hall-Beauvais, A.; Marchand, A.; Scheck, A.; Harteveld, Z.; Ni, D.; Tan, S.; Sverrisson, F.; Goverde, C.; Turelli, P.; Raclot, C.; Teslenko, A.; Pacesa, M.; Rosset, S.; Georgeon, S.; Marsden, J.; Petruzzella, A.; Liu, K.; Xu, Z.; Chai, Y.; Han, P.; Gao, G. F.; Oricchio, E.; Fierz, B.; Trono, D.; Stahlberg, H.; Bronstein, M.; Correia, B. E.

2022-06-17

General General

Granzyme K+ CD8 T cells form a core population in inflamed human tissue.

In Science translational medicine ; h5-index 138.0

T cell-derived pro-inflammatory cytokines are a major driver of rheumatoid arthritis (RA) pathogenesis. Although these cytokines have traditionally been attributed to CD4 T cells, we have found that CD8 T cells are notably abundant in synovium and make more interferon (IFN)-γ and nearly as much tumor necrosis factor (TNF) as their CD4 T cell counterparts. Furthermore, using unbiased high-dimensional single-cell RNA-seq and flow cytometric data, we found that the vast majority of synovial tissue and synovial fluid CD8 T cells belong to an effector CD8 T cell population characterized by high expression of granzyme K (GzmK) and low expression of granzyme B (GzmB) and perforin. Functional experiments demonstrate that these GzmK+ GzmB+ CD8 T cells are major cytokine producers with low cytotoxic potential. Using T cell receptor repertoire data, we found that CD8 GzmK+ GzmB+ T cells are clonally expanded in synovial tissues and maintain their granzyme expression and overall cell state in blood, suggesting that they are enriched in tissue but also circulate. Using GzmK and GzmB signatures, we found that GzmK-expressing CD8 T cells were also the major CD8 T cell population in the gut, kidney, and coronavirus disease 2019 (COVID-19) bronchoalveolar lavage fluid, suggesting that they form a core population of tissue-associated T cells across diseases and human tissues. We term this population tissue-enriched expressing GzmK or TteK CD8 cells. Armed to produce cytokines in response to both antigen-dependent and antigen-independent stimuli, CD8 TteK cells have the potential to drive inflammation.

Jonsson A Helena, Zhang Fan, Dunlap Garrett, Gomez-Rivas Emma, Watts Gerald F M, Faust Heather J, Rupani Karishma Vijay, Mears Joseph R, Meednu Nida, Wang Runci, Keras Gregory, Coblyn Jonathan S, Massarotti Elena M, Todd Derrick J, Anolik Jennifer H, McDavid Andrew, Wei Kevin, Rao Deepak A, Raychaudhuri Soumya, Brenner Michael B

2022-Jun-15

General General

Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes.

OBJECTIVE : The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients.

METHODS : The electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia, and COVID-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic curve. Heat maps to visualize attention scores were generated to provide interpretability to the neural networks. Finally, cluster analysis was performed to identify potential phenotypic subgroups of patients with ARDS.

RESULTS : The single RNN model trained to classify 13 outputs outperformed the individual XGBoost models for ARDS prediction, achieving an area under the receiver operating characteristic curve of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in improved performance. Earlier prediction of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations.

CONCLUSIONS : The RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with the means to take early action.

Lam Carson, Thapa Rahul, Maharjan Jenish, Rahmani Keyvan, Tso Chak Foon, Singh Navan Preet, Casie Chetty Satish, Mao Qingqing

2022-Jun-15

ARDS, COVID-19, clinical decision support, deep learning, diagnostic criteria, electronic health record, health care, multitask learning, neural networks, prediction model, recurrent neural network, respiratory distress, risk outcome

General General

Machine learning applications for COVID-19 outbreak management.

In Neural computing & applications

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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

2022-Jun-10

Applications, COVID-19, Machine learning, Medical imaging, Outbreak

General General

A novel drone-based system for accurate human temperature measurement and disease symptoms detection using thermography and AI.

In Remote sensing applications : society and environment

The world continues to witness several waves of COVID-19 spread due to the emergence of new variants of the SARS-CoV-2 virus. Stopping the spread requires synergistic efforts that include the use of technologies such as unmanned aerial vehicles and machine learning. This paper presents a novel system for detecting disease symptoms from a distance using unmanned aerial vehicles equipped with thermal and visual image sensors. A hardware/software system that uses thermography to accurately calculate the skin temperature of targeted individuals using thermal cameras is developed. In addition, machine vision algorithms are developed to recognize human actions such as coughing and sneezing, which are paramount symptoms of respiratory infections. The proposed system is implemented and tested in outdoor environments. The results of experiments showed that the system can determine the skin temperature of multiple targeted individuals simultaneously with an error of less than 1 °C. The field experiments showed that the developed system is capable of simultaneously measuring the temperature of more than 10 individuals in less than 5 seconds. Just to give a perspective, it takes at least 3 seconds to measure one individual's temperature if this was done using traditional methods. Furthermore, the results showed that the system has accurately detected actions such as coughing and sneezing with almost 96% accuracy at a real-time performance of 28 frames/second.

Al Maashri Ahmed, Saleem Ashraf, Bourdoucen Hadj, Eldirdiry Omer, Al Ghadani Ahmed

2022-Jun-09

General General

A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study.

In EClinicalMedicine

Background : Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies.

Methods : We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688).

Findings : The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores.

Interpretation : Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials.

Funding : This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.

Wang Ziyue, Cryar Adam, Lemke Oliver, Tober-Lau Pinkus, Ludwig Daniela, Helbig Elisa Theresa, Hippenstiel Stefan, Sander Leif-Erik, Blake Daniel, Lane Catherine S, Sayers Rebekah L, Mueller Christoph, Zeiser Johannes, Townsend StJohn, Demichev Vadim, Mülleder Michael, Kurth Florian, Sirka Ernestas, Hartl Johannes, Ralser Markus

2022-Jul

Biomarker, COVID-19, Clinical disease progression, Disease prognosis, LC-MS/MS, Machine learning, SARS-CoV2, Severity stratification, Targeted proteomics

General General

Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.

In medRxiv : the preprint server for health sciences

Background : Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the extent of data drift and provide insights about its characteristics for sepsis onset prediction. This study will help elucidate the nature of data drift for prediction of sepsis and similar diseases. This may aid with the development of more effective patient monitoring systems that can stratify risk for dynamic disease states in hospitals.

Methods : We devise a series of simulations that measure the effects of data drift in patients with sepsis. We simulate multiple scenarios in which data drift may occur, namely the change in the distribution of the predictor variables (covariate shift), the change in the statistical relationship between the predictors and the target (concept shift), and the occurrence of a major healthcare event (major event) such as the COVID-19 pandemic. We measure the impact of data drift on model performances, identify the circumstances that necessitate model retraining, and compare the effects of different retraining methodologies and model architecture on the outcomes. We present the results for two different MLAs, eXtreme Gradient Boosting (XGB) and Recurrent Neural Network (RNN).

Results : Our results show that the properly retrained XGB models outperform the baseline models in all simulation scenarios, hence signifying the existence of data drift. In the major event scenario, the area under the receiver operating characteristic curve (AUROC) at the end of the simulation period is 0.811 for the baseline XGB model and 0.868 for the retrained XGB model. In the covariate shift scenario, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.853 and 0.874 respectively. In the concept shift scenario and under the mixed labeling method, the retrained XGB models perform worse than the baseline model for most simulation steps. However, under the full relabeling method, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.852 and 0.877 respectively. The results for the RNN models were mixed, suggesting that retraining based on a fixed network architecture may be inadequate for an RNN. We also present the results in the form of other performance metrics such as the ratio of observed to expected probabilities (calibration) and the normalized rate of positive predictive values (PPV) by prevalence, referred to as lift, at a sensitivity of 0.8.

Conclusion : Our simulations reveal that retraining periods of a couple of months or using several thousand patients are likely to be adequate to monitor machine learning models that predict sepsis. This indicates that a machine learning system for sepsis prediction will probably need less infrastructure for performance monitoring and retraining compared to other applications in which data drift is more frequent and continuous. Our results also show that in the event of a concept shift, a full overhaul of the sepsis prediction model may be necessary because it indicates a discrete change in the definition of sepsis labels, and mixing the labels for the sake of incremental training may not produce the desired results.

Rahmani Keyvan, Thapa Rahul, Tsou Peiling, Chetty Satish Casie, Barnes Gina, Lam Carson, Tso Chak Foon

2022-Jun-07

General General

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

In Journal of molecular biology ; h5-index 65.0

MOTIVATION : Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications.The sheer plurality ofvariants and huge scale of genomic data have added to the challenges of tracing themutations/variants and their relationship to infection severity (if any).

RESULTS : We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199519 outcome-traced genomes, representing45,625 nucleotide-mutations, were employed. Among these, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97±0.01, Europe:0.94±0.01, N.America:0.92±0.02, Africa:0.94±0.07, S.America:0.93±0). Although virus-genotype alone could enable high predictivity (0.97±0.01, 0.89±0.02, 0.86±0.04, 0.95±0.06, 0.9±0.04), the performance was not found to be consistent since the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (> 0.87±0.03, 0.91±0.01, 0.87±0.03, 0.84±0.08, 0.89±0.05). High-performance models were employed for inferring theimportant mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learningcan play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

Nagpal Sunil, Kumar Pinna Nishal, Pant Namrata, Singh Rohan, Srivastava Divyanshu, Mande Sharmila S

2022-Jun-11

Genome classification, Machine learning, Mutation identification, Predictive prognosis, SARS-CoV-2

General General

Modeling Trust in COVID-19 Contact-Tracing Apps Using the Human-Computer Trust Scale: Online Survey Study.

In JMIR human factors

BACKGROUND : The COVID-19 pandemic has caused changes in technology use worldwide, both socially and economically. This pandemic crisis has brought additional measures such as contact-tracing apps (CTAs) to help fight against spread of the virus. Unfortunately, the low adoption rate of these apps affected their success. There could be many reasons for the low adoption, including concerns of security and privacy, along with reported issues of trust in CTAs. Some concerns are related with how CTAs could be used as surveillance tools or their potential threats to privacy as they involve health data. For example, in Estonia, the CTA named HOIA had approximately 250,000 downloads in the middle of January 2021. However, in 2021, only 4.7% of the population used HOIA as a COVID-19 CTA. The reasons for the low adoption include lack of competency, and privacy and security concerns. This lower adoption and the lack of trustworthiness persist despite efforts of the European Union in building ethics and trustworthy artificial intelligence (AI)-based apps.

OBJECTIVE : The aim of this study was to understand how to measure trust in health technologies. Specifically, we assessed the usefulness of the Human-Computer Trust Scale (HCTS) to measure Estonians' trust in the HOIA app and the causes for this lack of trust.

METHODS : The main research question was: Can the HCTS be used to assess citizens' perception of trust in health technologies? We established four hypotheses that were tested with a survey. We used a convenience sample for data collection, including sharing the questionnaire on social network sites and using the snowball method to reach all potential HOIA users in the Estonian population.

RESULTS : Among the 78 respondents, 61 had downloaded the HOIA app with data on usage patterns. However, 20 of those who downloaded the app admitted that it was never opened despite most claiming to regularly use mobile apps. The main reasons included not understanding how it works, and privacy and security concerns. Significant correlations were found between participants' trust in CTAs in general and their perceived trust in the HOIA app regarding three attributes: competency (P<.001), risk perception (P<.001), and reciprocity (P=.01).

CONCLUSIONS : This study shows that trust in the HOIA app among Estonian residents did affect their predisposition to use the app. Participants did not generally believe that HOIA could help to control the spread of the virus. The result of this work is limited to HOIA and health apps that use similar contact-tracing methods. However, the findings can contribute to gaining a broader understanding and awareness of the need for designing trustworthy technologies. Moreover, this work can help to provide design recommendations that ensure trustworthiness in CTAs, and the ability of AI to use highly sensitive data and serve society.

Sousa Sonia, Kalju Tiina

2022-Jun-13

COVID-19, Estonia, app, artificial intelligence, awareness, case study, contact-tracing, covid, design, human factors, human-computer interaction, mHealth, mobile app, mobile health, monitoring, perspective, safety, surveillance, trust, trustworthy AI

Ophthalmology Ophthalmology

Mandating Limits on Workload, Duty, and Speed in Radiology.

In Radiology ; h5-index 91.0

Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.

Alexander Robert, Waite Stephen, Bruno Michael A, Krupinski Elizabeth A, Berlin Leonard, Macknik Stephen, Martinez-Conde Susana

2022-Jun-14

Public Health Public Health

Optimal Social Distancing Policy for COVID-19 Control in Korea: A Model-Based Analysis.

In Journal of Korean medical science

BACKGROUND : Since March 2020, when coronavirus disease 2019 (COVID-19) was declared a pandemic, many countries have applied unprecedented restrictive measures to contain the spread of the virus. This study aimed to explore the optimal social distancing policy for COVID-19 control in South Korea to safely reopen the society.

METHODS : We developed an age-specific, deterministic compartment epidemic model to examine the COVID-19 control decision-making process, including the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 July 2021 and 30 December 2022. The model consists of the natural history of COVID-19, testing performance, vaccinations, and social distancing enforcement measures to detect and control SARS-CoV-2. We modelled potential intervention scenarios with three distinct components: 1) social distancing duration and level; 2) testing intensity; and 3) vaccination uptake rate. The primary and secondary outcomes were COVID-19 incidence and prevalence of severe patients requiring intensive care unit (ICU) care.

RESULTS : Four (or more) months of social distancing (that can reduce 40-60% transmission) may mitigate epidemic resurgence and ICU demand in the future and keep the cases below the capacity limit if the testing intensity and vaccination rate remain constant or increase by 20% (with respect to the current level). In contrast, two months of strict social distancing enforcement may also successfully mitigate future epidemic surge and ICU demand as long as testing intensity and vaccination rates are increased by 20%.

CONCLUSION : In South Korea, given the relatively high vaccination coverage and low incidence, four or more months of social distancing enforcement can effectively mitigate epidemic resurgence after lifting the social distancing measures. In addition, increasing the testing intensity and vaccination rate may help reduce necessary social distancing levels and duration to prevent a future epidemic resurgence and mitigate social and economic damage.

Jo Youngji, Shrestha Sourya, Radnaabaatar Munkhzul, Park Hojun, Jung Jaehun

2022-Jun-13

COVID-19, ICU, Pandemic, SARS-CoV-2, Social Distancing Policy

General General

Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images.

In Pattern recognition

The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity.

Sharma Ajay, Mishra Pramod Kumar

2022-Nov

Chest X-ray, Covid-19, Deep learning, Explainable AI, Infection segmentation, Lung segmentation, Transfer learning

General General

COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens.

In Annals of operations research

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.

Jana Rabin K, Ghosh Indranil, Jawadi Fredj, Uddin Gazi Salah, Sousa Ricardo M

2022-Jun-08

COVID-19, Co-integration, Detrended cross-correlation analysis, Machine learning, The US equity market

General General

A transfer learning based deep learning model to diagnose covid-19 CT scan images.

In Health and technology

To save the life of human beings during the pandemic conditions we need an effective automated method to deal with this situation. In pandemic conditions when the available resources becomes insufficient to handle the patient's load, then we needed some fast and reliable method which analyse the patient medical data with high efficiency and accuracy within time limitations. In this manuscript, an effective and efficient method is proposed for exact diagnosis of the patient whether it is coronavirus disease-2019 (covid-19) positive or negative with the help of deep learning. To find the correct diagnosis with high accuracy we use pre-processed segmented images for the analysis with deep learning. In the first step the X-ray image or computed tomography (CT) of a covid-19 infected person is analysed with various schemes of image segmentation like simple thresholding at 0.3, simple thresholding at 0.6, multiple thresholding (between 26-230) and Otsu's algorithm. On comparative analysis of all these methods, it is found that the Otsu's algorithm is a simple and optimum scheme to improve the segmented outcome of binary image for the diagnosis point of view. Otsu's segmentation scheme gives more precise values in comparison to other methods on the scale of various image quality parameters like accuracy, sensitivity, f-measure, precision, and specificity. For image classification here we use Resnet-50, MobileNet and VGG-16 models of deep learning which gives accuracy 70.24%, 72.95% and 83.18% respectively with non-segmented CT scan images and 75.08%, 80.12% and 99.28% respectively with Otsu's segmented CT scan images. On a comparative study we find that the VGG-16 models with CT scan image segmented with Otsu's segmentation gives very high accuracy of 99.28%. On the basis of the diagnosis of the patient firstly we go for an arterial blood gas (ABG) analysis and then on the behalf of this diagnosis and ABG report, the severity level of the patient can be decided and according to this severity level, proper treatment protocols can be followed immediately to save the patient's life. Compared with the existing works, our deep learning based novel method reduces the complexity, takes much less time and has a greater accuracy for exact diagnosis of coronavirus disease-2019 (covid-19).

Pandey Sanat Kumar, Bhandari Ashish Kumar, Singh Himanshu

2022-Jun-09

Arterial blood gas analysis, Complete blood count, Image segmentation, Otsu’s, Thresholding

General General

FCF: Feature complement fusion network for detecting COVID-19 through CT scan images.

In Applied soft computing

COVID-19 spreads and contracts people rapidly, to diagnose this disease accurately and timely is essential for quarantine and medical treatment. RT-PCR plays a crucial role in diagnosing the COVID-19, whereas computed tomography (CT) delivers a faster result when combining artificial assistance. Developing a Deep Learning classification model for detecting the COVID-19 through CT images is conducive to assisting doctors in consultation. We proposed a feature complement fusion network (FCF) for detecting COVID-19 through lung CT scan images. This framework can extract both local features and global features by CNN extractor and ViT extractor severally, which successfully complement the deficiency problem of the receptive field of the other. Due to the attention mechanism in our designed feature complement Transformer (FCT), extracted local and global feature embeddings achieve a better representation. We combined a supervised with a weakly supervised strategy to train our model, which can promote CNN to guide the VIT to converge faster. Finally, we got a 99.34% accuracy on our test set, which surpasses the current state-of-art popular classification model. Moreover, this proposed structure can easily extend to other classification tasks when changing other proper extractors.

Liang Shu, Nie Rencan, Cao Jinde, Wang Xue, Zhang Gucheng

2022-Jun-06

COVID-19 detecting, Deep Learning, Feature complement fusion, Weakly supervised learning

Public Health Public Health

A novel multi-scale based deep convolutional neural network for detecting COVID-19 from X-rays.

In Applied soft computing

The COVID-19 pandemic has posed an unprecedented threat to the global public health system, primarily infecting the airway epithelial cells in the respiratory tract. Chest X-ray (CXR) is widely available, faster, and less expensive therefore it is preferred to monitor the lungs for COVID-19 diagnosis over other techniques such as molecular test, antigen test, antibody test, and chest computed tomography (CT). As the pandemic continues to reveal the limitations of our current ecosystems, researchers are coming together to share their knowledge and experience in order to develop new systems to tackle it. In this work, an end-to-end IoT infrastructure is designed and built to diagnose patients remotely in the case of a pandemic, limiting COVID-19 dissemination while also improving measurement science. The proposed framework comprises six steps. In the last step, a model is designed to interpret CXR images and intelligently measure the severity of COVID-19 lung infections using a novel deep neural network (DNN). The proposed DNN employs multi-scale sampling filters to extract reliable and noise-invariant features from a variety of image patches. Experiments are conducted on five publicly available databases, including COVIDx, COVID-19 Radiography, COVID-XRay-5K, COVID-19-CXR, and COVIDchestxray, with classification accuracies of 96.01 %, 99.62 %, 99.22 %, 98.83 %, and 100 %, and testing times of 0.541, 0.692, 1.28, 0.461, and 0.202 s, respectively. The obtained results show that the proposed model surpasses fourteen baseline techniques. As a result, the newly developed model could be utilized to evaluate treatment efficacy, particularly in remote locations.

Karnati Mohan, Seal Ayan, Sahu Geet, Yazidi Anis, Krejcar Ondrej

2022-Jun-06

COVID-19, Chest X-ray, Deep neural network, Internet of things

General General

Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey.

In Frontiers in artificial intelligence

Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.

M V Manoj Kumar, Atalla Shadi, Almuraqab Nasser, Moonesar Immanuel Azaad

2022

COVID financial management, COVID-19 diagnosis, artificial intelligence, chest CT, chest X-ray, deep learning, insurance

Public Health Public Health

CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.

In Biomedical signal processing and control

The coronavirus disease 2019 (COVID-19) epidemic had a significant impact on daily life in many nations and global public health. COVID's quick spread has become one of the biggest disruptive calamities in the world. In the fight against COVID-19, it's critical to keep a close eye on the initial stage of infection in patients. Furthermore, early COVID-19 discovery by precise diagnosis, especially in patients with no evident symptoms, may reduce the patient's death rate and can stop the spread of COVID-19. When compared to CT images, chest X-ray (CXR) images are now widely employed for COVID-19 diagnosis since CXR images contain more robust features of the lung. Furthermore, radiologists can easily diagnose CXR images because of its operating speed and low cost, and it is promising for emergency situations and therapy. This work proposes a tri-stage CXR image based COVID-19 classification model using deep learning convolutional neural networks (DLCNN) with an optimal feature selection technique named as enhanced grey-wolf optimizer with genetic algorithm (EGWO-GA), which is denoted as CXGNet. The proposed CXGNet is implemented as multiple classes, such as 4-class, 3-class, and 2-class models based on the diseases. Extensive simulation outcome discloses the superiority of the proposed CXGNet model with enhanced classification accuracy of 94.00% for the 4-class model, 97.05% of accuracy for the 3-class model, and 100% accuracy for the 2-class model as compared to conventional methods.

Gopatoti Anandbabu, Vijayalakshmi P

2022-Aug

COVID-19, Chest X-ray image classification, Convolutional neural networks, Deep learning, Grey-wolf optimization

General General

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties.

In Sustainable cities and society

A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database. A hybrid feature-selection model composed of four machine-learning algorithms is developed to emphasize the regional impact of community features on the case fatality rate (CFR). A COVID-19 vulnerability index (COVULin) is proposed to measure the county's vulnerability, the effects of model's parameters on mortality, and the efficiency of control policies. The results showed that the dense counties in which minority groups represent more than 45% of the population and those with poverty rates greater than 24% were the most vulnerable counties during the first and the last pandemic peaks, respectively. Highly-correlated CFR and COVULin scores indicated a close agreement between the model outcomes and COVID-19 impacts. Counties with higher poverty and uninsured rates were the most resistant to government intervention. It is anticipated that the proposed model can play an essential role in identifying vulnerable communities and help reduce damages during long-term alike disasters.

Moosazadeh Mohammad, Ifaei Pouya, Tayerani Charmchi Amir Saman, Asadi Somayeh, Yoo ChangKyoo

2022-Aug

COVID-19 control policies, Data analysis, Machine learning, Social vulnerability, The U.S. cities

General General

A framework for predicting academic orientation using supervised machine learning.

In Journal of ambient intelligence and humanized computing

School guidance is declared an integral part of the education and training process, as it accompanies students in their educational and professional choices. Accordingly, the current situation in light of the Covid-19 epidemic requires a reconsideration of school guidance together with the methods of accompanying the student to choose the field that suits his/her personality, knowledge qualifications, perceptual and intellectual skills in order to achieve an excellent educational level that enables the learner to work in future professions. The current study aims to predict a student's potential and provide support for academic guidance. This paper emphasizes the importance of supervised machine learning and classification algorithms to predict the personality type based on student traits. Based on the information gathered, the results of this study indicate that it contributes significantly to providing a comprehensive approach to support academic self-orientation.

El Mrabet Hicham, Ait Moussa Abdelaziz

2022-Jun-06

Classification algorithms, Machine learning, Smart school guidance

General General

Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach.

In Phenomics

** : The clinical manifestations of COVID-19, caused by the SARS-CoV-2, define a large spectrum of symptoms that are mainly dependent on the human host conditions. In Costa Rica, more than 169,000 cases and 2185 deaths were reported during the year 2020, the pre-vaccination period. To describe the clinical presentations at the time of diagnosis of SARS-CoV-2 infection in Costa Rica during the pre-vaccination period, we implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive cases. Profiles were compared based on symptoms, risk factors, viral load, and genomic features of the SARS-CoV-2 sequence. A total of 18 symptoms at time of diagnosis of SARS-CoV-2 infection were reported with a frequency > 1%, and those were used to identify seven clinical profiles with a specific composition of clinical manifestations. In the comparison between clusters, a lower viral load was found for the asymptomatic group, while the risk factors and the SARS-CoV-2 genomic features were distributed among all the clusters. No other distribution patterns were found for age, sex, vital status, and hospitalization. In conclusion, during the pre-vaccination time in Costa Rica, the symptoms at the time of diagnosis of SARS-CoV-2 infection were described in clinical profiles. The host co-morbidities and the SARS-CoV-2 genotypes are not specific of a particular profile, rather they are present in all the groups, including asymptomatic cases. In addition, this information can be used for decision-making by the local healthcare institutions (first point of contact with health professionals, case definition, or infrastructure). In further analyses, these results will be compared against the profiles of cases during the vaccination period.

Supplementary Information : The online version contains supplementary material available at 10.1007/s43657-022-00058-x.

Molina-Mora Jose Arturo, González Alejandra, Jiménez-Morgan Sergio, Cordero-Laurent Estela, Brenes Hebleen, Soto-Garita Claudio, Sequeira-Soto Jorge, Duarte-Martínez Francisco

2022-Jun-07

COVID-19, Clinical profiles, Costa Rica, Diagnosis, Machine learning, SARS-CoV-2

General General

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform.

In Frontiers in public health

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Li Yanhan, Zhao Hongyun, Gan Tian, Liu Yang, Zou Lian, Xu Ting, Chen Xuan, Fan Cien, Wu Meng

2022

COVID-19, computer aided diagnosis, deep learning, multi-modal, multi-view

Public Health Public Health

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

In Current medical imaging

BACKGROUND : The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to developing a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method.

METHOD : Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning, and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/Pneumonia).

RESULT : Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVID- ensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multicategory classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images, and that the performance of this model is superior to that of several existing methods.

CONCLUSION : Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection, but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.

Liu Xiangbin, Wu Wenqian, Chun-Wei Lin Jerry, Liu Shuai

2022-Jun-10

COVID-19, Deep learning, X-ray, ensemble learning, pneumonia, transfer learning

General General

Digital health education: the need for a digitally ready workforce.

In Archives of disease in childhood. Education and practice edition

Digital health education develops an understanding of the pragmatic use of digital technologies, including health apps, artificial intelligence and wearables, in the National Health Service (NHS). Staff should feel confident accessing up-to-date, quality-assured digital health solutions.Digital health is a high priority in government, NHS organisations and Royal Colleges. However, there is a gap between what is expected and the education of staff or medical students to enable implementation.Digital health education needs to be up to date and universally included within training, continuing professional development activities and medical school curriculums.During COVID-19, more families across the UK became digitally enabled with school, council, charities and governments providing access to devices, WiFi and mobile data for those that needed it. Improved digital access brings equalities in access to health information and healthcare professionals. Health app use sharply rose during COVID-19, as patients self-managed and took control of their conditions, but most health apps do not reach NHS standards.Paediatricians are well positioned to advise on appropriate health app use and advocate for improved patient access to solutions.Many paediatricians adopted remote video consultations during the COVID-19 pandemic but could soon adopt more digital health strategies to remotely track, monitor and manage conditions remotely.Patient management now includes remote consultations and digital health solutions; therefore, medical histories should capture digital access, environments and literacy.This article explains the importance of digital health education, lists accessible resources and provides examples of health apps that can be recommended.

Holland Brown Tamsin Mary, Bewick Mike

2022-Jun-13

Covid-19, Technology

General General

Chronic lung lesions in COVID-19 survivors: predictive clinical model.

In BMJ open

OBJECTIVE : This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection.

DESIGN : This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT.

SETTING : A tertiary hospital in Sao Paulo, Brazil.

PARTICIPANTS : 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years.

PRIMARY OUTCOME MEASURE : A predictive clinical model for lung lesion detection on chest CT.

RESULTS : There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07).

CONCLUSION : A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.

Carvalho Carlos Roberto Ribeiro, Chate Rodrigo Caruso, Sawamura Marcio Valente Yamada, Garcia Michelle Louvaes, Lamas Celina Almeida, Cardenas Diego Armando Cardona, Lima Daniel Mario, Scudeller Paula Gobi, Salge João Marcos, Nomura Cesar Higa, Gutierrez Marco Antonio

2022-Jun-13

COVID-19, chest imaging, respiratory medicine (see thoracic medicine)

Surgery Surgery

A finger in every pie - the versatility of chemokines.

In Biomedical journal

In this issue of Biomedical Journal we encounter the chemokine superfamily and its clinical potential. The time course from 56 days zero COVID-19 to a resurgence in cases is presented, as well as a possible solution to overcome rejection in vascularized composite allotransplantation. We are shown the opportunity deep learning (DL) offers in the case of tracking single cells and particles, and also use of DL to bring all hands on deck to counter the current challenge of the COVID-19 pandemic. This issue contains articles about the effect of low energy shock waves in cystitis; the negative effect of high fructose on aortic valve stenosis; a study about the outcome of fecal microbiota transplantation in case of refractory Clostridioides difficile infection; a novel long non-coding RNA that could serve in treating triple-negative breast cancer; the benefits of acupressure in patients with restless leg syndrome; and Filamin A mutations in abnormal neuronal migration development. Finally, a link between jaw surgery and the psychological impact on the patient is explored; a method presented that allows identification of cervical characteristics associated with difficult embryo transfer; and a letter suggesting new parameters to evaluate the use of bone-substitute augmentation in the treatment of osteoporotic intertrochanteric fractures.

Kattner Aila Akosua

2022-Jun-10

COVID-19, allotransplant, chemokine, deep learning, restless leg syndrome

General General

Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody.

In Biosensors & bioelectronics

Currently, vaccination is the most effective medical measure to improve group immunity and prevent the rapid spread of COVID-19. Since the individual difference of vaccine effectiveness is inevitable, it is necessary to evaluate the vaccine effectiveness of every vaccinated person to ensure the appearance of herd immunity. Here, we developed an artificial intelligent (AI)-assisted colorimetric polydopamine nanoparticle (PDA)-based lateral flow immunoassay (LFIA) platform for the sensitive and accurate quantification of neutralizing antibodies produced from vaccinations. The platform integrates PDA-based LFIA and a smartphone-based reader to test the neutralizing antibodies in serum, where an AI algorithm is also developed to accurately and quantitatively analyze the results. The developed platform achieved a quantitative detection with 160 ng/mL of detection limit and 625-10000 ng/mL of detection range. Moreover, it also successfully detected totally 50 clinical serum samples, revealing a great consistency with the commercial ELISA kit. Comparing with commercial gold nanoparticle-based LFIA, our PDA-based LFIA platform showed more accurate quantification ability for the clinical serum. Therefore, we envision that the AI-assisted PDA-based LFIA platform with sensitive and accurate quantification ability is of great significance for large-scale evaluation of vaccine effectiveness and other point-of-care immunoassays.

Tong Haoyang, Cao Chaoyu, You Minli, Han Shuang, Liu Zhe, Xiao Ying, He Wanghong, Liu Chang, Peng Ping, Xue Zhenrui, Gong Yan, Yao Chunyan, Xu Feng

2022-Jun-08

Artificial intelligence, COVID-19, Lateral flow immunoassay, Neutralizing antibody, Polydopamine

General General

Multiphysical graph neural network (MP-GNN) for COVID-19 drug design.

In Briefings in bioinformatics

Graph neural networks (GNNs) are the most promising deep learning models that can revolutionize non-Euclidean data analysis. However, their full potential is severely curtailed by poorly represented molecular graphs and features. Here, we propose a multiphysical graph neural network (MP-GNN) model based on the developed multiphysical molecular graph representation and featurization. All kinds of molecular interactions, between different atom types and at different scales, are systematically represented by a series of scale-specific and element-specific graphs with distance-related node features. From these graphs, graph convolution network (GCN) models are constructed with specially designed weight-sharing architectures. Base learners are constructed from GCN models from different elements at different scales, and further consolidated together using both one-scale and multi-scale ensemble learning schemes. Our MP-GNN has two distinct properties. First, our MP-GNN incorporates multiscale interactions using more than one molecular graph. Atomic interactions from various different scales are not modeled by one specific graph (as in traditional GNNs), instead they are represented by a series of graphs at different scales. Second, it is free from the complicated feature generation process as in conventional GNN methods. In our MP-GNN, various atom interactions are embedded into element-specific graph representations with only distance-related node features. A unique GNN architecture is designed to incorporate all the information into a consolidated model. Our MP-GNN has been extensively validated on the widely used benchmark test datasets from PDBbind, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016. Our model can outperform all existing models as far as we know. Further, our MP-GNN is used in coronavirus disease 2019 drug design. Based on a dataset with 185 complexes of inhibitors for severe acute respiratory syndrome coronavirus (SARS-CoV/SARS-CoV-2), we evaluate their binding affinities using our MP-GNN. It has been found that our MP-GNN is of high accuracy. This demonstrates the great potential of our MP-GNN for the screening of potential drugs for SARS-CoV-2. Availability: The Multiphysical graph neural network (MP-GNN) model can be found in https://github.com/Alibaba-DAMO-DrugAI/MGNN. Additional data or code will be available upon reasonable request.

Li Xiao-Shuang, Liu Xiang, Lu Le, Hua Xian-Sheng, Chi Ying, Xia Kelin

2022-Jun-14

Drug design, Ensemble learning, Graph neural network, Graph representation and featurization, Protein–ligand binding

General General

Practices and attitudes of Bavarian stakeholders regarding the secondary-use of health data for research purposes during the COVID-19 pandemic: a qualitative interview study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic is a threat to global health and requires collaborative health research efforts across organizations and countries to address it. Although routinely collected digital health data is a valuable source of information for researchers, benefiting from this data requires accessing and sharing the data. Health care organizations focusing on individual risk minimization threatens to undermine COVID-19 research efforts, and it has been argued that there is an ethical obligation to use the European Union's General Data Protection Regulation (GDPR) scientific research exemption during the COVID-19 pandemic to support collaborative health research.

OBJECTIVE : This study aimed to explore the practices and attitudes of stakeholders in the German federal state of Bavaria regarding the secondary-use of health data for research purposes during the COVID-19 pandemic, with a specific focus on the GDPR scientific research exemption.

METHODS : Individual semi-structured qualitative interviews were conducted between December 2020 and January 2021 with a purposive sample of 17 stakeholders from three different groups in Bavaria: 1) researchers involved in COVID-19 research (n=5), 2) data protection officers (n=6), and 3) research ethics committee representatives (n=6). Transcripts were analysed using conventional content analysis.

RESULTS : Participants identified systemic challenges in conducting collaborative secondary-use health data research in Bavaria; secondary health data research generally only happens when patient consent had been obtained, or the data had been fully anonymized. The GDPR research exemption has not played a significant role during the pandemic, and is currently used very seldom and restrictively. Participants identified three key groups of barriers that led to difficulties: 1) the wider ecosystem at many Bavarian health care organisations, 2) legal uncertainty that is leading to risk adverse approaches, and 3) ethical positions that patient consent ought to be obtained whenever possible to respect patient autonomy. To improve health data research in Bavaria and across all of Germany, participants wanted greater legal certainty regarding the use of pseudonymized data for research purposes without the patient's consent.

CONCLUSIONS : The current balance between enabling the positive goals of health data research and avoiding associated data protection risks is heavily skewed towards avoiding risks; so much so that it makes reaching the goals of health data research extremely difficult. This is important because it is widely recognised that there is an ethical imperative to use health data to improve care. The current approach also creates a problematic conflict with Germany´s, and the federal state of Bavaria´s, ambitions to be a leader in artificial intelligence. A recent development in the field of German public administration known as "norm screening" (Normenscreening) could potentially provide a systematic approach to minimize legal barriers. This approach would likely be beneficial to other countries.

CLINICALTRIAL :

McLennan Stuart, Rachut Sarah, Lange Johannes, Fiske Amelia, Heckmann Dirk, Buyx Alena

2022-May-29

General General

Host protease activity classifies pneumonia etiology.

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

Community-acquired pneumonia (CAP) has been brought to the forefront of global health priorities due to the COVID-19 pandemic. However, classification of viral versus bacterial pneumonia etiology remains a significant clinical challenge. To this end, we have engineered a panel of activity-based nanosensors that detect the dysregulated activity of pulmonary host proteases implicated in the response to pneumonia-causing pathogens and produce a urinary readout of disease. The nanosensor targets were selected based on a human protease transcriptomic signature for pneumonia etiology generated from 33 unique publicly available study cohorts. Five mouse models of bacterial or viral CAP were developed to assess the ability of the nanosensors to produce etiology-specific urinary signatures. Machine learning algorithms were used to train diagnostic classifiers that could distinguish infected mice from healthy controls and differentiate those with bacterial versus viral pneumonia with high accuracy. This proof-of-concept diagnostic approach demonstrates a way to distinguish pneumonia etiology based solely on the host proteolytic response to infection.

Anahtar Melodi, Chan Leslie W, Ko Henry, Rao Aditya, Soleimany Ava P, Khatri Purvesh, Bhatia Sangeeta N

2022-Jun-21

bacterial infections, diagnostics, nanoparticles, pneumonia, viral infections

General General

CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images.

In Biomedical signal processing and control

The Coronavirus (COVID-19) pandemic has created havoc on humanity by causing millions of deaths and adverse physical and mental health effects. To prepare humankind for the fast and efficient detection of the virus and its variants shortly, COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. To detect COVID-19, there are numerous publicly accessible datasets of Chest X-rays that the researchers have combined to solve the problem of inadequate data. The cause for concern here is that in combining two or more datasets, some of the images might be duplicates, so a curated dataset has been used in this study, taken from an author's paper. This dataset consists of 1281 COVID-19, 3270 Normal X-rays, and 1656 viral-pneumonia infected Chest X-ray images. Dataset has been pre-processed and divided carefully to ensure that there are no duplicate images. A comparative study on many traditional pre-trained models was performed, analyzing top-performing models. Fine-tuned InceptionV3, Modified EfficientNet B0&B1 produced an accuracy of 99.78% on binary classification, i.e., covid-19 infected and normal Chest X-ray image. ResNetV2 had a classification accuracy of 97.90% for 3-class classification i.e., covid-19 infected, normal, and pneumonia. Furthermore, a trailblazing custom CNN-based model, CoviXNet, has been proposed consisting of 15 layers that take efficiency into account. The proposed model CoviXNet exhibited a 10-fold accuracy of 99.47% on binary classification and 96.61% on 3-class. CoviXNet has shown phenomenal performance with exceptional accuracy and minimum computational cost. We anticipate that this comparative study, along with the proposed model CoviXNet, can assist medical centers with the efficient real-life detection of Coronavirus.

Srivastava Gaurav, Chauhan Aninditaa, Jangid Mahesh, Chaurasia Sandeep

2022-Jun-08

COVID-19, Chest X-Ray images, Convolutional neural networks, CoviXNet, Deep transfer learning

General General

Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study.

In Heliyon

Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.

Larabi-Marie-Sainte Souad, Alhalawani Sawsan, Shaheen Sara, Almustafa Khaled Mohamad, Saba Tanzila, Khan Fatima Nayer, Rehman Amjad

2022-Jun

COVID-19, Drift, Exponential smoothing, Forecasting, Holt, Linear regression, Time-series

General General

A Methodological Framework for the Comparative Evaluation of Multiple Imputation Methods: Multiple Imputation of Race, Ethnicity and Body Mass Index in the U.S. National COVID Cohort Collaborative

ArXiv Preprint

While electronic health records are a rich data source for biomedical research, these systems are not implemented uniformly across healthcare settings and significant data may be missing due to healthcare fragmentation and lack of interoperability between siloed electronic health records. Considering that the deletion of cases with missing data may introduce severe bias in the subsequent analysis, several authors prefer applying a multiple imputation strategy to recover the missing information. Unfortunately, although several literature works have documented promising results by using any of the different multiple imputation algorithms that are now freely available for research, there is no consensus on which MI algorithm works best. Beside the choice of the MI strategy, the choice of the imputation algorithm and its application settings are also both crucial and challenging. In this paper, inspired by the seminal works of Rubin and van Buuren, we propose a methodological framework that may be applied to evaluate and compare several multiple imputation techniques, with the aim to choose the most valid for computing inferences in a clinical research work. Our framework has been applied to validate, and extend on a larger cohort, the results we presented in a previous literature study, where we evaluated the influence of crucial patients' descriptors and COVID-19 severity in patients with type 2 diabetes mellitus whose data is provided by the National COVID Cohort Collaborative Enclave.

Elena Casiraghi, Rachel Wong, Margaret Hall, Ben Coleman, Marco Notaro, Michael D. Evans, Jena S. Tronieri, Hannah Blau, Bryan Laraway, Tiffany J. Callahan, Lauren E. Chan, Carolyn T. Bramante, John B. Buse, Richard A. Moffitt, Til Sturmer, Steven G. Johnson, Yu Raymond Shao, Justin Reese, Peter N. Robinson, Alberto Paccanaro, Giorgio Valentini, Jared D. Huling, Kenneth Wilkins, :, Tell Bennet, Christopher Chute, Peter DeWitt, Kenneth Gersing, Andrew Girvin, Melissa Haendel, Jeremy Harper, Janos Hajagos, Stephanie Hong, Emily Pfaff, Jane Reusch, Corneliu Antoniescu, Kimberly Robaski

2022-06-13

Public Health Public Health

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records.

In The Lancet. Digital health

BACKGROUND : Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.

METHODS : In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.

FINDINGS : Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1.

INTERPRETATION : Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.

FUNDING : British Heart Foundation Data Science Centre, led by Health Data Research UK.

Thygesen Johan H, Tomlinson Christopher, Hollings Sam, Mizani Mehrdad A, Handy Alex, Akbari Ashley, Banerjee Amitava, Cooper Jennifer, Lai Alvina G, Li Kezhi, Mateen Bilal A, Sattar Naveed, Sofat Reecha, Torralbo Ana, Wu Honghan, Wood Angela, Sterne Jonathan A C, Pagel Christina, Whiteley William N, Sudlow Cathie, Hemingway Harry, Denaxas Spiros

2022-Jun-08

General General

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization.

In Computers in biology and medicine

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).

Su Hang, Zhao Dong, Elmannai Hela, Heidari Ali Asghar, Bourouis Sami, Wu Zongda, Cai Zhennao, Gui Wenyong, Chen Mayun

2022-May-18

COVID-19, Meta-heuristic, Multi-threshold image segmentation, Multi-verse optimization, Novel coronavirus pneumonia, Optimization

General General

Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity.

In Cell systems

The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets.

Zhang Sai, Cooper-Knock Johnathan, Weimer Annika K, Shi Minyi, Kozhaya Lina, Unutmaz Derya, Harvey Calum, Julian Thomas H, Furini Simone, Frullanti Elisa, Fava Francesca, Renieri Alessandra, Gao Peng, Shen Xiaotao, Timpanaro Ilia Sarah, Kenna Kevin P, Baillie J Kenneth, Davis Mark M, Tsao Philip S, Snyder Michael P

2022-Jun-03

COVID-19, GWAS, Mendelian randomization, NK cell, gene discovery, genome-wide association study, machine learning, network analysis, rare variant analysis, single-cell multiomic profiling

Radiology Radiology

Risk stratification models for stroke in patients hospitalized with COVID-19 infection.

In Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

OBJECTIVES : To derive models that identify patients with COVID-19 at high risk for stroke.

MATERIALS AND METHODS : We used data from the AHA's Get With The Guidelines® COVID-19 Cardiovascular Disease Registry to generate models for predicting stroke risk among adults hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our models, we used data on demographics, comorbidities, medications, and vital sign and laboratory values at admission. The outcome was a cerebrovascular event (stroke, TIA, or cerebral vein thrombosis). First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable and multivariable analyses. Then, we assigned points for each variable based on corresponding coefficients to create a prediction score. Second, we used machine learning techniques to create risk estimators using all available covariates.

RESULTS : Among 21,420 patients hospitalized with COVID-19, 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created/validated a COVID-19 stroke risk score with a C-statistic of 0.66 (95% CI, 0.60-0.72). The CANDLE score assigns 1 point each for prior cerebrovascular disease, afebrile temperature, no prior pulmonary disease, history of hypertension, leukocytosis, and elevated systolic blood pressure. CANDLE stratified risk of an acute cerebrovascular event according to low- (0-1: 0.2% risk), medium- (2-3: 1.1% risk), and high-risk (4-6: 2.1-3.0% risk) groups. Machine learning estimators had similar discriminatory performance as CANDLE: C-statistics, 0.63-0.69.

CONCLUSIONS : We developed a practical clinical score, with similar performance to machine learning estimators, to help stratify stroke risk among patients hospitalized with COVID-19.

Merkler Alexander E, Zhang Cenai, Diaz Ivan, Stewart Carolyn, LeMoss Natalie M, Mir Saad, Parikh Neal, Murthy Santosh, Lin Ning, Gupta Ajay, Iadecola Costantino, Elkind Mitchell S V, Kamel Hooman, Navi Babak B

2022-Jun-02

COVID-19, Cerebrovascular disease, Intracerebral hemorrhage, Risk-stratification, Stroke

Radiology Radiology

Artificial intelligence-based CT metrics used in predicting clinical outcome of COVID-19 in young and middle-aged adults.

In Medical physics ; h5-index 59.0

BACKGROUND : Currently, most researchers mainly analyzed COVID-19 pneumonia visually or qualitatively, probably somewhat time-consuming and not precise enough.

PURPOSE : This study aimed to excavate more information, such as differences in distribution, density, and severity of pneumonia lesions between males and females in a specific age group using artificial intelligence (AI)-based CT metrics. Besides, these metrics were incorporated into a clinical regression model to predict the short-term outcome.

MATERIALS AND METHODS : The clinical, laboratory information and a series of HRCT images from 49 patients, aged from 20 to 50 years and confirmed with COVID-19, were collected. The volumes and percentages of infection (POI) among bilateral lungs and each bronchopulmonary segment were extracted using uAI-Discover-NCP software (version R001). The POI in three HU ranges, (i.e. ←300, -300∼49 and ≥50 HU representing ground-glass opacity (GGO), mixed opacity and consolidation), were also extracted. Hospital stay was predicted with several POI after adjusting days from illness onset to admission, leucocytes, lymphocytes, c-reactive protein, age and gender using a multiple linear regression model. A total of 91 patients aged 20-50 from public database were selected.

RESULTS : Right lower lobes had the highest POI, followed by left lower lobes, right upper lobes, middle lobes and left upper lobes. The distributions in lung lobes and segments were different between the sexes. Men had a higher total POI and GGO of the lungs, but less consolidation than women in initial CT (all p < 0.05). The total POI, percentage of consolidation on initial CT and changed POI were positively correlated with hospital stay in the model. A total of 91 patients aged 20-50 years in the public database were selected, and AI segmentation was performed. The POI of the lower lobes was obviously higher than that in the upper lobes; the POI of each segment of the right upper lobe in the males was higher than that in the females, which was consistent with the result of the 49 patients previously CONCLUSION: Both men and women had characteristic distributions in lung lobes and bronchopulmonary segments. AI-based CT quantitative metrics can provide more precise information regarding lesion distribution and severity to predict clinical outcome. This article is protected by copyright. All rights reserved.

Xudong Yu, Weihong Liu, Feng Xia, Yanli Li, Weishun Lan, Fengjun Zhang, Jiao Gao, Jiawei Li, Xiaolu Huang, Huailiang Huang, Jianye Liang, Sihui Zeng, Chuanmiao Xie, Hanhui Li, Liang Mao

2022-Jun-11

COVID-19, artificial intelligence, distribution, outcome, percentages of infection, quantitative metrics

General General

Using Machine Learning to Efficiently Vaccinate Homebound Patients Against COVID-19: A Real Time Immunization Campaign.

In Journal of medical Internet research ; h5-index 88.0

We use a machine learning-based tool to geographically cluster patients and optimize route planning for vaccination of homebound patients against Covid-19.

Kumar Anish, Ren Jen, Ornstein Katherine, Gliatto Peter

2022-Jun-07

General General

Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies.

In Frontiers in public health

In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of ("COVID-19" OR "covid19" OR "covid" OR "coronavirus" OR "Sars-CoV-2") AND ("readmission" OR "re-admission" OR "rehospitalization" OR "rehospitalization") were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.

Loo Wei Kit, Hasikin Khairunnisa, Suhaimi Anwar, Yee Por Lip, Teo Kareen, Xia Kaijian, Qian Pengjiang, Jiang Yizhang, Zhang Yuanpeng, Dhanalakshmi Samiappan, Azizan Muhammad Mokhzaini, Lai Khin Wee

2022

COVID-19, machine learning, mortality, readmission, risk factors

General General

Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.

In Frontiers in public health

Motivation : Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission.

Methods : In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness.

Results : The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84).

Conclusion : A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.

Fu Yacheng, Zhong Weijun, Liu Tao, Li Jianmin, Xiao Kui, Ma Xinhua, Xie Lihua, Jiang Junyi, Zhou Honghao, Liu Rong, Zhang Wei

2022

COVID-19, LASSO regression, critical illness, machine learning, risk factors

General General

Prediction of COVID-19 using long short-term memory by integrating principal component analysis and clustering techniques.

In Informatics in medicine unlocked

Severe acute respiratory syndrome coronavirus (SARS-COV) is a major family of viruses that cause infections in both animals and humans, including common cold, coronavirus disease (COVID-19), severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome. This study primarily aims to predict the number of COVID-19 positive cases in 36 states of Nigeria using a long short-term memory (LSTM) algorithm of deep learning. The proposed approach employs K-means clustering to detect outliers and principal component analysis (PCA) to select important features from the dataset. The LSTM was chosen because of its non-linear characteristics to handle the dataset. As COVID-19 cases follow non-linear characteristics, LSTM is the most suitable algorithm for predicting their numbers. For comparison, several types of machine learning algorithms, such as naive Bayes, XG-boost, and SVM, were employed. After the comparison, LSTM was observed to be superior among all algorithms.

Ilu Saratu Yusuf, Rajesh Prasad, Mohammed Hassan

2022

COVID-19, Classification and clustering, LSTM, Machine learning

General General

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers.

In JAMIA open

Objective : To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

Materials and Methods : Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app.

Results : We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age.

Discussion : We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection.

Conclusion : Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

Hirten Robert P, Tomalin Lewis, Danieletto Matteo, Golden Eddye, Zweig Micol, Kaur Sparshdeep, Helmus Drew, Biello Anthony, Pyzik Renata, Bottinger Erwin P, Keefer Laurie, Charney Dennis, Nadkarni Girish N, Suarez-Farinas Mayte, Fayad Zahi A

2022-Jul

COVID-19, apple watch, coronavirus, machine learning, wearable device

Public Health Public Health

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation.

In Biomedical and environmental sciences : BES

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019 (COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web interface with adjustable parameters, including choice of intervention measures, intervention weights, vaccination, and viral variants, was designed for users to run the simulation. The total case number was set as the outcome. The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set. Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model. The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200, which were 25 more days and 36 fewer cases than the real situation, respectively. Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people's livelihood.

Wu Jie Wen, Jiao Xiao Kang, Du Xin Hui, Jiao Zeng Tao, Liang Zuo Ru, Pang Ming Fan, Ji Han Ran, Cheng Zhi Da, Cai Kang Ning, Qi Xiao Peng

2022-May-20

COVID-19, Compartment model, Intervention policy simulation, Machine learning

General General

Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities.

In Lab on a chip

Microfluidic diagnostic (μDX) technologies miniaturize sensors and actuators to the length-scales that are relevant to biology: the micrometer scale to interact with cells and the nanometer scale to interrogate biology's molecular machinery. This miniaturization allows measurements of biomarkers of disease (cells, nanoscale vesicles, molecules) in clinical samples that are not detectable using conventional technologies. There has been steady progress in the field over the last three decades, and a recent burst of activity catalyzed by the COVID-19 pandemic. In this time, an impressive and ever-growing set of technologies have been successfully validated in their ability to measure biomarkers in clinical samples, such as blood and urine, with sensitivity and specificity not possible using conventional tests. Despite our field's many accomplishments to date, very few of these technologies have been successfully commercialized and brought to clinical use where they can fulfill their promise to improve medical care. In this paper, we identify three major technological trends in our field that we believe will allow the next generation of μDx to have a major impact on the practice of medicine, and which present major opportunities for those entering the field from outside disciplines: 1. the combination of next generation, highly multiplexed μDx technologies with machine learning to allow complex patterns of multiple biomarkers to be decoded to inform clinical decision points, for which conventional biomarkers do not necessarily exist. 2. The use of micro/nano devices to overcome the limits of binding affinity in complex backgrounds in both the detection of sparse soluble proteins and nucleic acids in blood and rare circulating extracellular vesicles. 3. A suite of recent technologies that obviate the manual pre-processing and post-processing of samples before they are measured on a μDX chip. Additionally, we discuss economic and regulatory challenges that have stymied μDx translation to the clinic, and highlight strategies for successfully navigating this challenging space.

Iyer Vasant, Yang Zijian, Ko Jina, Weissleder Ralph, Issadore David

2022-Jun-08

General General

MRI and laboratory monitoring of disease-modifying therapy efficacy and risks.

In Current opinion in neurology

PURPOSE OF REVIEW : Increasingly, therapeutic strategy in multiple sclerosis (MS) is informed by imaging and laboratory biomarkers, in addition to traditional clinical factors. Here, we review aspects of monitoring the efficacy and risks of disease-modifying therapy (DMT) with both conventional and emerging MRI and laboratory measures.

RECENT FINDINGS : The adoption of consensus-driven, stable MRI acquisition protocols and artificial intelligence-based, quantitative image analysis is heralding an era of precision monitoring of DMT efficacy. New MRI measures of compartmentalized inflammation, neuro-degeneration and repair complement traditional metrics but require validation before use in individual patients. Laboratory markers of brain cellular injury, such as neurofilament light, are robust outcomes in DMT efficacy trials; their use in clinical practice is being refined. DMT-specific laboratory monitoring for safety is critical and may include lymphocytes, immunoglobulins, autoimmunity surveillance, John Cunningham virus serology and COVID-19 vaccination seroresponse.

SUMMARY : A biomarker-enhanced monitoring strategy has immediate clinical application, with growing evidence of long-term reductions in disability accrual when both clinically symptomatic and asymptomatic inflammatory activity is fully suppressed; and amelioration of the risks associated with therapy. Emerging MRI and blood-based measures will also become important tools for monitoring agents that target the innate immune system and promote neuro-repair.

Barnett Michael, Barnett Yael, Reddel Stephen

2022-Jun-01

General General

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models.

In Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research

Objectives : When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.

Material and methods : The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.

Results : In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.

Conclusion : The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.

Huyut M T

2022-Jun-01

Biochemical and hematological biomarkers, COVID-19, Classification, Feature selection methods, Routine blood values, Supervised machine learning models

General General

A Natural Language Processing Tool Offering Data Extraction for COVID-19 Related Information (DECOVRI).

In Studies in health technology and informatics ; h5-index 23.0

A new natural language processing (NLP) application for COVID-19 related information extraction from clinical text notes is being developed as part of our pandemic response efforts. This NLP application called DECOVRI (Data Extraction for COVID-19 Related Information) will be released as a free and open source tool to convert unstructured notes into structured data within an OMOP CDM-based ecosystem. The DECOVRI prototype is being continuously improved and will be released early (beta) and in a full version.

Heider Paul M, Pipaliya Ronak M, Meystre Stéphane M

2022-Jun-06

COVID-19, Machine Learning, Natural Language Processing

General General

Identifying New COVID-19 Variants from Spike Proteins Using Novelty Detection.

In Studies in health technology and informatics ; h5-index 23.0

The COVID-19 pandemic has caused millions of infections and deaths worldwide in an ongoing pandemic. With the passage of time, several variants of this virus have surfaced. Machine learning methods and algorithms have been very useful in understanding the virus and its implications so far. In this paper, we have studied a set of novelty detection algorithms and applied it to the problem of detecting COVID-19 variants. Our results show accuracies of 79.64% and 82.43% on the B.1.1.7 and B.1.351 variants respectively on ProtVec unaligned COVID-19 spike protein sequences using One Class SVM with fine-tuned parameters. We believe that a system for automated and timely detection of variants will help countries formulate mitigation measures and study remedies in terms of medicines and vaccines that can protect against the new variants.

Basu Sayantani, Campbell Roy H

2022-Jun-06

Coronavirus [B04.820.578.500.540.150], Machine Learning [L01.224.050.375.530], Proteins [D12.776]

General General

The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays.

In Studies in health technology and informatics ; h5-index 23.0

Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used open-source data and tested on an external test set and a hospital dataset, for the task of classifying chest X-rays into one of three classes: COVID-19, non-COVID pneumonia and no-pneumonia. Classification performance of the models investigated is evaluated through ROC curves, confusion matrices and standard classification metrics. Explainability modules are implemented to explore the image features most important to classification. Data analysis and model evalutions show that the popular open-source dataset COVIDx is not representative of the real clinical problem and that results from testing on this are inflated. Dependence on open-source data can leave models vulnerable to bias and confounding variables, requiring careful analysis to develop clinically useful/viable AI tools for COVID-19 detection in chest X-rays.

Harkness Rachael, Hall Geoff, Frangi Alejandro F, Ravikumar Nishant, Zucker Kieran

2022-Jun-06

Computing Methodologies, Data Science, Respiratory Tract Infections

General General

A Sample Size Extractor for RCT Reports.

In Studies in health technology and informatics ; h5-index 23.0

Sample size is an important indicator of the power of randomized controlled trials (RCTs). In this paper, we designed a total sample size extractor using a combination of syntactic and machine learning methods, and evaluated it on 300 Covid-19 abstracts (Covid-Set) and 100 generic RCT abstracts (General-Set). To improve the performance, we applied transfer learning from a large public corpus of annotated abstracts. We achieved an average F1 score of 0.73 on the Covid-Set testing set, and 0.60 on the General-Set using exact matches. The F1 scores for loose matches on both datasets were over 0.74. Compared with the state-of-the-art tool, our extractor reports total sample sizes directly and improved F1 scores by at least 4% without transfer learning. We demonstrated that transfer learning improved the sample size extraction accuracy and minimized human labor on annotations.

Lin Fengyang, Liu Hao, Moon Paul, Weng Chunhua

2022-Jun-06

Natural Language Processing, Randomized Controlled Trial, Sample Size

Public Health Public Health

Latent Linguistic Motifs in Social Media Postings Resisting COVID-19 Misinformation.

In Studies in health technology and informatics ; h5-index 23.0

Social media has become a predominant source of information for many health care consumers. However, false and misleading information is a pervasive problem in this context. Specifically, health-related misinformation has become a significant public health challenge, impeding the effectiveness of public health awareness campaigns and resulting in suboptimal responsiveness to the communication of legitimate risk-related information. Little is known about the mechanisms driving the seeding and spreading of such information. In this paper, we specifically examine COVID-19 tweets which attempt to correct misinformation. We employ a mixed-methods approach comprising qualitative coding, deep learning classification, and computerized text analysis to understand the manifestation of speech acts and other linguistic variables. Results indicate significant differences in linguistic variables (e.g., positive emotion, tone, authenticity) of corrective tweets and their dissemination level. Our deep learning classifier has a macro average performance of 0.82. Implications for effective and persuasive misinformation correction efforts are discussed.

Singh Tavleen, Olivares Sofia, Myneni Sahiti

2022-Jun-06

COVID-19, Deep learning, Misinformation

General General

MedSentinel - A Smart Sentinel for Biomedical Online Search Demonstrated by a COVID-19 Search.

In Studies in health technology and informatics ; h5-index 23.0

We present a work-in-progress software project which aims to assist cross-database medical research and knowledge acquisition from heterogeneous sources. Using a Natural Language Processing (NLP) model based on deep learning algorithms, topical similarities are detected, going beyond measures of connectivity via citation or database suggestion algorithms. A network is generated based on the NLP-similarities between them, and then presented within an explorable 3D environment. Our software will then generate a list of publications and datasets which pertain to a certain topic of interest, based on their level of similarity in terms of knowledge representation.

Schölly Reto, Yazijy Suhail, Kellmeyer Philipp

2022-Jun-06

Data Mining (D057225), Information Services (D007255), Knowledge Bases (D051188)

General General

Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray.

In Radiologia

OBJECTIVES : To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters.

METHODS : All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model.

RESULTS : A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics.

CONCLUSION : The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.

Calvillo-Batllés P, Cerdá-Alberich L, Fonfría-Esparcia C, Carreres-Ortega A, Muñoz-Núñez C F, Trilles-Olaso L, Martí-Bonmatí L

Artificial intelligence, COVID-19, Chest X-Ray, Inteligencia artificial, Modelos predictivos, Mortalidad, Mortality, Predictive models, Prognosis, Pronóstico, Radiografía torácica

oncology Oncology

5G in Healthcare: from COVID-19 to Future Challenges.

In IEEE journal of biomedical and health informatics

Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients' data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.

Moglia Andrea, Georgiou Konstantinos, Marinov Blagoi, Georgiou Evangelos, Berchiolli Raffaella Nice, Satava Richard M, Cuschieri Alfred

2022-Jun-08

Radiology Radiology

Vasculopathy and Increased Vascular Congestion in Fatal COVID-19 and ARDS.

In American journal of respiratory and critical care medicine ; h5-index 108.0

Rationale The leading cause of death in coronavirus disease 2019(COVID-19) is severe pneumonia, with many patients developing acute respiratory distress syndrome(ARDS) and diffuse alveolar damage(DAD). Whether DAD in fatal COVID-19 is distinct from other causes of DAD remains unknown. Objective To compare lung parenchymal and vascular alterations between patients with fatal COVID-19 pneumonia and other DAD-causing etiologies using a multidimensional approach. Methods This autopsy cohort consisted of consecutive patients with COVID-19 pneumonia(n=20) and with respiratory failure and histologic DAD(n=21; non-COVID-19 viral and non-viral etiologies). Premortem chest computed tomography(CT) scans were evaluated for vascular changes. Postmortem lung tissues were compared using histopathological and computational analyses. Machine-learning-derived morphometric analysis of the microvasculature was performed, with a random forest classifier quantifying vascular congestion(CVasc) in different microscopic compartments. Respiratory-mechanics and gas-exchange parameters were evaluated longitudinally in patients with ARDS. Measurements and Main Results On premortem CT, COVID-19 patients showed more dilated vasculature when evaluating all lung segments (p=0.001) compared to DAD-controls. Histopathology revealed vasculopathic changes including hemangiomatosis-like-changes(p=0.043), thromboemboli(p=0.0038), pulmonary infarcts(p=0.047), and perivascular inflammation(p<0.001). Generalized estimating equations revealed significant regional differences in the lung microarchitecture among all DAD-causing entities. COVID-19 showed a larger overall CVasc-range(p=0.002). Alveolar-septal-congestion was associated with a significantly shorter time-to-death from symptom onset(p=0.03), length-of-hospital-stay(p=0.02), and increased ventilatory ratio[an estimate for pulmonary dead space fraction(Vd); p=0.043] in all cases of ARDS. Conclusions Severe COVID-19 pneumonia is characterized by significant vasculopathy and aberrant alveolar-septal-congestion. Our findings also highlight the role that vascular alterations may play in Vd and clinical outcomes in ARDS in general.

Villalba Julian A, Hilburn Caroline F, Garlin Michelle A, Elliott Grant A, Li Yijia, Kunitoki Keiko, Poli Sergio, Alba George A, Madrigal Emilio, Taso Manuel, Price Melissa C, Aviles Alexis J, Araujo-Medina Milagros, Bonanno Liana, Boyraz Baris, Champion Samantha N, Harris Cynthia K, Helland Timothy L, Hutchison Bailey, Jobbagy Soma, Marshall Michael S, Shepherd Daniel J, Barth Jaimie L, Hung Yin P, Ly Amy, Hariri Lida P, Turbett Sarah E, Pierce Virginia M, Branda John A, Rosenberg Eric S, Mendez-Pena Javier, Chebib Ivan, Rosales Ivy A, Smith Rex N, Miller Miles A, Rosas Ivan O, Hardin Charles C, Baden Lindsey R, Medoff Benjamin D, Colvin Robert B, Little Brent P, Stone James R, Mino-Kenudson Mari, Shih Angela R

2022-Jun-07

ARDS, COVID-19, Vascular Congestion, Vasculopathy, Ventilatory ratio

General General

Deep Transfer Learning for Communicable Disease Detection and Recommendation in Edge Networks.

In IEEE/ACM transactions on computational biology and bioinformatics

Considering the increasing number of communicable disease cases such as COVID-19 worldwide, the early detection of the disease can prevent and limit the outbreak. Besides that, the PCR test kits are not available in most parts of the world, and there is genuine concern about their performance and reliability. To overcome this, in this paper, we develop a novel edge-centric healthcare framework integrating with wearable sensors and advanced machine learning (ML) model for timely decisions with minimum delay. Through wearable sensors, a set of features have been collected that are further preprocessed for preparing a useful dataset. However, due to limited resource capacity, analyzing the features in resource-constrained edge devices is challenging. Motivated by this, we introduce an advanced ML technique for data analysis at edge networks, namely Deep Transfer Learning (DTL). DTL transfers the knowledge from the well-trained model to a new lightweight ML model that can support the resource-constraint nature of distributed edge devices. We consider a benchmark COVID-19 dataset for validation purposes, consisting of 11 features and 2 Million sensor data. The extensive simulation results demonstrate the efficiency of the proposed DTL technique over the existing ones and achieve 99.8% accuracy while diseases prediction.

Adhikari Mainak, Hazra Abhishek, Nandy Sudarshan

2022-Jun-07

General General

Cash stock strategies during regular and COVID-19 periods for bank branches by deep learning.

In PloS one ; h5-index 176.0

Determining the optimal amount of cash stock reserved in each bank branch is a strategic decision. A certain level of cash stock must be kept and ready for cash withdrawal needs at a branch. However, holding too much cash not only forfeits opportunities to make profit from the exceeding amount of cash in the stock but also increases insurance cost. This paper presents cash stock strategies for bank branches by using deep learning. Deep learning models were applied to historical data collected by a retail bank to predict the cash withdrawals and deposits. Data preparation and feature selection to identify important attributes from the bank branch data were performed. In the prediction process, two Recurrent Neural Network techniques-Long Short-Term Memory and Gated Recurrent Units methods-were compared. Then prediction errors were measured and statistically tested for their probability distributions. These distributions together with the predicted values were used in determining the lower and upper bounds for holding the cash stock. These bounds were employed to recommend the cash stock level strategies by having two options for different situations. The impacts of COVID-19 were also tested and discussed. According to the bank under this study, the proposed strategies can reduce the amount of cash stock by more than 10% for which was their initial target. Hence, the costs of cash management such as insurance cost and cash transportation cost were reduced. Moreover, the excess cash could be used for other purposes of the bank.

Jariyavajee Chattriya, Lamjiak Taninnuch, Ratanasanya San, Fairee Suthida, Puphaiboon Kreecha, Khompatraporn Charoenchai, Polvichai Jumpol, Sirinaovakul Booncharoen

2022

General General

HPiP: an R/Bioconductor package for predicting host-pathogen protein-protein interactions from protein sequences using ensemble machine learning approach.

In Bioinformatics advances

Motivation : Despite arduous and time-consuming experimental efforts, protein-protein interactions (PPIs) for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, we developed an R/Bioconductor package, HPiP (Host-Pathogen Interaction Prediction) software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins.

Results : Using severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) or the novel SARS-CoV-2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves a good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally in human monocyte THP-1 cells, and with several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs.

Availability and implementation : HPiP is released as an open-source code under the MIT license that is freely available on GitHub (https://github.com/BabuLab-UofR/HPiP) as well as on Bioconductor (http://bioconductor.org/packages/devel/bioc/html/HPiP.html).

Supplementary information : Supplementary data are available at Bioinformatics Advances online.

Rahmatbakhsh Matineh, Moutaoufik Mohamed Taha, Gagarinova Alla, Babu Mohan

2022

General General

A computational framework to support the treatment of bedsores during COVID-19 diffusion.

In Journal of ambient intelligence and humanized computing

The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process.

Di Martino Ferdinando, Orciuoli Francesco

2022-May-27

Augmented reality, COVID-19, Deep learning, Edge detection, Image segmentation, Pattern matching, Pressure ulcers

General General

Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown.

In Health and technology

The Severe Acute Respiratory Syndrome (SARS)-CoV-2 virus caused COVID-19 pandemic has led to various kinds of anxiety and stress in different strata and sections of the society. The aim of this study is to analyse the sleeping and anxiety disorder for a wide distribution of people of different ages and from different strata of life. The study also seeks to investigate the different symptoms and grievances that people suffer from in connection with their sleep patterns and predict the possible relationships and factors in association with outcomes related to COVID-19 pandemic induced stress and issues. A total of 740 participants (51.3% male and 48.7% female) structured with 2 sections, first with general demographic information and second with more targeted questions for each demographic were surveyed. Pittsburgh Sleep Quality Index (PSQI) and General Anxiety Disorder assessment (GAD-7) standard scales were utilized to measure the stress, sleep disorders and anxiety. Experimental results showed positive correlation between PSQI and GAD-7 scores for the participants. After adjusting for age and gender, occupation does not have an effect on sleep quality (PSQI), but it does have an effect on anxiety (GAD-7). Student community in spite of less susceptible to COVID-19 infection found to be highly prone to psychopathy mental health disturbances during the COVID-19 pandemic. The study also highlights the connectivity between lower social status and mental health issues. Random Forest model for college students indicates clearly the stress induced factors as anxiety score, worry about inability to understand concepts taught online, involvement of parents, college hours, worrying about other work load and deadlines for the young students studying in Universities.

Anbarasi L Jani, Jawahar Malathy, Ravi Vinayakumar, Cherian Sherin Miriam, Shreenidhi S, Sharen H

2022-May-30

ANOVA, Anxiety, COVID-19, Cross-sectional study, GAD-7, Insomnia, PSQI

General General

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2.

In iScience

Deep Mutational Scanning (DMS) experiments have been performed on SARS-CoV-2's spike receptor binding domain and human ACE2 zinc-binding peptidase domain - both central players in viral infection and evolution and antibody evasion - quantifying how mutations impact biochemical phenotypes. We modeled biochemical phenotypes from massively parallel assays, using neural networks trained on protein sequence mutations in the virus and human host. Neural networks were significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the physicochemical properties of amino acids, such as hydrophobicity and long-range non-bonded energy per atom, significantly improved prediction (empirical P<0.01p<0.01). We observed concordance of the neural network predictions with molecular dynamics (multiple 500 ns or 1 μs all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms.

Wang Bo, Gamazon Eric R

2022-Jun-02

General General

AI-based production and application of English multimode online reading using multi-criteria decision support system.

In Soft computing

Reading and writing English have greater significance in learning oral English and comprehensive skills. Artificial Intelligence (AI) is important in many aspects of our lives, including education, healthcare, business, and so on. AI has allowed for significant advancements in the educational system. It has quickly risen to the top of the list of the most rapidly expanding educational technology disciplines. Through its creation, AI has contributed to the creation of new educational and knowledge techniques that are currently being researched across a wide range of fields. Chatbots, Robots' Assistant, Vidreader, Seeing AI, Classcraft, 3D holograms, and other AI-based programmes were developed to assist both teaching staff and students in using and improving the educational system. In the sphere of education, AI is focusing on sentimentalized artificial learning aids and smart instruction systems. The primary goal and objective of the education business is to construct an intelligent education system, which is now possible thanks to the development of teaching assistant robots, smart classrooms based on AI, and English teaching assistance, among other things. Artificial Intelligence techniques may now be employed at all stages of learning to improve the educational system. During the COVID-19 illness, students and teachers took their education and instruction online in a variety of ways. Learning can be done digitally so that folks do not fall behind in their education. The proposed study has considered multi-criteria decision support systems (MCDM) for AI-enabled production and application of English multimode online reading. This study has offered the application of the super decision tool to facilitate the experimental work. As a result of this, researchers will be able to find and design new solutions to the subject.

Dong Yifan, Yu Xinyu, Alharbi Abdullah, Ahmad Sultan

2022-May-30

AI, English language, Learning, Multimode, Online reading

General General

Outcome prediction during an ICU surge using a purely data-driven approach: A supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak.

In International journal of medical informatics ; h5-index 49.0

PURPOSE : COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making. We assessed performances of three machine learning approaches to predict mortality in COVID-19 patients admitted to ICU using early operative data from the Lombardy ICU Network.

METHODS : This is a secondary analysis of prospectively collected data from Lombardy ICU network. A logistic regression, balanced logistic regression and random forest were built to predict survival on two datasets: dataset A included patient demographics, medications before admission and comorbidities, and dataset B included respiratory data the first day in ICU.

RESULTS : Models were trained on 1484 patients on four outcomes (7/14/21/28 days) and reached the greatest predictive performance at 28 days (F1-score: 0.75 and AUC: 0.80). Age, number of comorbidities and male gender were strongly associated with mortality. On dataset B, mode of ventilatory assistance at ICU admission and fraction of inspired oxygen were associated with an increase in prediction performances.

CONCLUSIONS : Machine learning techniques might be useful in emergency phases to reach good predictive performances maintaining interpretability to gain knowledge on complex situations and enhance patient management and resources.

Greco Massimiliano, Angelotti Giovanni, Caruso Pier Francesco, Zanella Alberto, Stomeo Niccolò, Costantini Elena, Protti Alessandro, Pesenti Antonio, Grasselli Giacomo, Cecconi Maurizio

2022-Jun-02

COVID-19, Emergency organization, Epidemiology, ICU management, Machine learning, Outcomes

General General

ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences

bioRxiv Preprint

We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clade's information, which means it could be used for predictive tasks using virus sequence analysis.

Pesaranghader, A.; Pelletier, J.; Grenier, J.-C.; Poujol, R.; Hussin, J.

2022-06-06

oncology Oncology

Prior-aware autoencoders for lung pathology segmentation.

In Medical image analysis

Segmentation of lung pathology in Computed Tomography (CT) images is of great importance for lung disease screening. However, the presence of different types of lung pathologies with a wide range of heterogeneities in size, shape, location, and texture, on one side, and their visual similarity with respect to surrounding tissues, on the other side, make it challenging to perform reliable automatic lesion segmentation. To leverage segmentation performance, we propose a deep learning framework comprising a Normal Appearance Autoencoder (NAA) model to learn the distribution of healthy lung regions and reconstruct pathology-free images from the corresponding pathological inputs by replacing the pathological regions with the characteristics of healthy tissues. Detected regions that represent prior information regarding the shape and location of pathologies are then integrated into a segmentation network to guide the attention of the model into more meaningful delineations. The proposed pipeline was tested on three types of lung pathologies, including pulmonary nodules, Non-Small Cell Lung Cancer (NSCLC), and Covid-19 lesion on five comprehensive datasets. The results show the superiority of the proposed prior model, which outperformed the baseline segmentation models in all the cases with significant margins. On average, adding the prior model improved the Dice coefficient for the segmentation of lung nodules by 0.038, NSCLCs by 0.101, and Covid-19 lesions by 0.041. We conclude that the proposed NAA model produces reliable prior knowledge regarding the lung pathologies, and integrating such knowledge into a prior segmentation network leads to more accurate delineations.

Astaraki Mehdi, Smedby Örjan, Wang Chunliang

2022-May-25

Healthy image generation, Lung pathology segmentation, Prior-aware deep learning

General General

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Disease Progression Prediction via Sequential Deep Learning: Model Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Recent work has shown the potential of using audio data (e.g., cough, breathing and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection that detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 disease progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in healthcare systems.

OBJECTIVE : The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 disease progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 disease progression, thus modelling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction.

METHODS : Crowdsourced respiratory audio data including breathing, cough, and voice from 212 individuals over 5 days to 385 days were analysed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using Gated Recurrent Units (GRUs) to detect COVID-19 disease progression, by exploring the audio dynamics of individuals' historical audio biomarkers. The investigation is composed of two parts: i) COVID-19 detection in terms of positive and negative (healthy) using sequential audio signals, which was primarily assessed in terms of area under the receiver operating characteristic curve (AUC-ROC), sensitivity, and specificity, with 95% Confidence Intervals (CIs); ii) the longitudinal disease progression prediction over time in terms of probability of positive, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels.

RESULTS : We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUC-ROC of 0.79, sensitivity of 0.75 and specificity of 0.71, supports the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displays high consistency with the longitudinal test results with a correlation of 0.75 in the test cohort, and 0.86 in a subset of the test cohort with 12 participants who report disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery.

CONCLUSIONS : An audio-based COVID-19 disease progression monitoring system was developed using deep learning techniques, with strong performance showing the high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has a good potential in the post-peak and post-pandemic era that can help guide medical treatment and optimise hospital resource allocations. This framework provides a flexible, affordable and timely tool for COVID-19 disease tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

CLINICALTRIAL :

Dang Ting, Han Jing, Xia Tong, Spathis Dimitris, Bondareva Erika, Brown Chloë, Chauhan Jagmohan, Grammenos Andreas, Hasthanasombat Apinan, Floto Andres, Cicuta Pietro, Mascolo Cecilia

2022-Apr-18

General General

Monitoring of sedation in mechanically ventilated patients using remote technology.

In Current opinion in critical care ; h5-index 39.0

PURPOSE OF REVIEW : Two years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected.

RECENT FINDINGS : High rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity.

SUMMARY : Preventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.

Hanidziar Dusan, Westover Michael Brandon

2022-Jun-01

General General

Artificial intelligence and clinical deterioration.

In Current opinion in critical care ; h5-index 39.0

PURPOSE OF REVIEW : To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI).

RECENT FINDINGS : There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms.

SUMMARY : Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging.

Malycha James, Bacchi Stephen, Redfern Oliver

2022-Jun-01

General General

New Strategies and Practices of Design Education Under the Background of Artificial Intelligence Technology: Online Animation Design Studio.

In Frontiers in psychology ; h5-index 92.0

This study is based on the background of how artificial intelligence (AI) technology is applied to the field of creativity and design education to improve the design vision, teaching methods, and actual design productivity of practitioners. The purpose of the research is to compare traditional design education and new design education methods combined with AI technology. Taking the Technological Pedagogical Content Knowledge (TPACK) technology integration model as the starting point, a comprehensive evaluation is selected for different types of research to explore the animation design professional courses in design education, the content of students' perception preferences, and the evaluation of ease of learning so as to conduct research and analyze AI technology. Design new education strategies and practice methods under the background. In the research, a comparative experimental study was conducted on 40 first-year students majoring in animation design. The results show that through online design studio project practice, with personalized project learning guidance, the learning needs of students to show a better trend, and customized learning and project practice content can enhance the learning experience and performance of students. In the future, we can further expand the scope of analysis, include more case studies, and conduct more comprehensive research, including how to deal with the expansion of the platform for students' learning of design in situations similar to coronavirus disease 2019 (COVID-19) that profoundly affects our lives, and how the project is applied in practice.

Tang Tianran, Li Pengfei, Tang Qiheng

2022

artificial intelligence, deep learning, design education, design studio, education strategy, online