Doctor Penguin

Public Health

Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020.

In IEEE access : practical innovations, open solutions
COVID-19 has dramatically affected various aspects of human society with worldwide repercussions. Firstly, a serious public health issue has been generated, resulting in millions of deaths. Also, the global economy, social coexistence, psychological status, mental health, and the human-environment relationship/dynamics have been seriously affected. Indeed, abrupt changes in our daily lives have been enforced, starting with a mandatory quarantine and the application of biosafety measures. Due to the magnitude of these effects, research efforts from different fields were rapidly concentrated around the current pandemic to mitigate its impact. Among these fields, Artificial Intelligence (AI) and Deep Learning (DL) have supported many research papers to help combat COVID-19. The present work addresses a bibliometric analysis of this scholarly production during 2020. Specifically, we analyse quantitative and qualitative indicators that give us insights into the factors that have allowed papers to reach a significant impact on traditional metrics and alternative ones registered in social networks, digital mainstream media, and public policy documents. In this regard, we study the correlations between these different metrics and attributes. Finally, we analyze how the last DL advances have been exploited in the context of the COVID-19 situation.
Chicaiza Janneth, Villota Stephany D, Vinueza-Naranjo Paola G, Rumipamba-Zambrano Ruben

2022

Bibliometric analysis, COVID-19, deep learning, scholarly production

General

Crowd dynamics research in the era of Covid-19 pandemic: Challenges and opportunities.

In Safety science
With the issues of crowd control and physical distancing becoming central to disease prevention measures, one would expect that crowd research should become a focus of attention during the Covid-19 pandemic era. However, I will show, based on a variety of metrics, that not only has this not been the case, but also, the first two years of the pandemic have posed an undisputable setback to the development and growth of crowd science. Without intervention, this could potentially aggravate further and cause a long-lasting recession in this field. This article, in addition to documenting and highlighting this issue, aims to outline potential avenues through which crowd research can reshape itself in the era of Covid-19 pandemic, maintain its pre-pandemic momentum and even further expand the diversity of its topics. Despite significant changes that the pandemic has brought to human life, issues related to congregation and mobility of pedestrians, building fires, crowd incidents, rallying crowds and the like have not disappeared from societies and remain relevant. Moreover, the diversity of pandemic-related problems itself creates a rich ground for making novel scientific discoveries. This could provide grounds for establishing fresh dimensions in crowd dynamics research. These potential new dimensions extend to all areas of this field including numerical and experimental investigations, crowd psychology and applications of computer vision and artificial intelligence methods in crowd management. The Covid-19 pandemic may have posed challenges to crowd researchers but has also created ample potential opportunities. This is further evidenced by reviewing efforts taken thus far in pandemic-related crowd research.
Haghani Milad

2022-Sep

Covid-19, Crowd dynamics, Evacuation dynamics, Pandemic, Pedestrian dynamics

General

FluNet: An AI-Enabled Influenza-Like Warning System.

In IEEE sensors journal
Influenza is an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the COVID-19 pandemic exceeding 153 million cases worldwide, there is a need for a low-cost and contactless surveillance system to detect symptomatic individuals. The objective of this study was to develop FluNet, a novel, proof-of-concept, low-cost and contactless device for the detection of high-risk individuals. The system conducts face detection in the LWIR with a precision rating of 0.98, a recall of 0.91, an F-score of 0.96, and a mean intersection over union of 0.74 while sequentially taking the temperature trend of faces with a thermal accuracy of ± 1 K. In parallel, determining if someone is coughing by using a custom lightweight deep convolutional neural network with a precision rating of 0.95, a recall of 0.92, an F-score of 0.94 and an AUC of 0.98. We concluded this study by testing the accuracy of the direction of arrival estimation for the cough detection revealing an error of ± 4.78°. If a subject is symptomatic, a photo is taken with a specified region of interest using a visible light camera. Two datasets have been constructed, one for face detection in the LWIR consisting of 250 images of 20 participants' faces at various rotations and coverings, including face masks. The other for the real-time detection of coughs comprised of 40,482 cough / not cough sounds. These findings could be helpful for future low-cost edge computing applications for influenza-like monitoring.
Ward Ryan J, Mark Jjunju Fred Paul, Kabenge Isa, Wanyenze Rhoda, Griffith Elias J, Banadda Noble, Taylor Stephen, Marshall Alan

2021-Nov-01

COVID, COVID-19, Cough detection, SARS, face detection, machine learning

Pathology

A graph-transformer for whole slide image classification

ArXiv Preprint
Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected $4,818$ WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy$= 91.2$ $\pm$ $2.5\%$) based on five-fold cross-validation, and mean accuracy $= 82.3$ $\pm$ $1.0\%$ on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.
Yi Zheng, Rushin H. Gindra, Emily J. Green, Eric J. Burks, Margrit Betke, Jennifer E. Beane, Vijaya B. Kolachalama

2022-05-19

General

Adjuvant Therapy System of COVID-19 Patient: Integrating Warning, Therapy, Post-Therapy Psychological Intervention.

In IEEE transactions on network science and engineering
The 2019 novel coronavirus(COVID-19) spreads rapidly, and the large-scale infection leads to the lack of medical resources. For the purpose of providing more reasonable medical service to COVID-19 patients, we designed an novel adjuvant therapy system integrating warning, therapy, and post-therapy psychological intervention. The system combines data analysis, communication networks and artificial intelligence(AI) to design a guidance framework for the treatment of COVID-19 patients. Specifically, in this system, we first can use blood characteristic data to help make a definite diagnosis and classify the patients. Then, the classification results, together with the blood characteristics and underlying diseases disease characteristics of the patient, can be used to assist the doctor in treat treating the patient according to AI algorithms. Moreover, after the patient is discharged from the hospital, the system can monitor the psychological and physiological state at the data collection layer. And in the data feedback layer, this system can analyze the data and report the abnormalities of the patient to the doctor through communication network. Experiments show the effectiveness of our proposed system.
Li Miao, Hao Yixue, Ma Yaxiong, Chen Jincai, Hu Long, Chen Min, Hwang Kai, Liu Zhongchun

2022-Jan

5G communication, COVID-19, adjuvant therapy system, blood characteristic

General

The Impact of COVID-19 Pandemic on LGBTQ Online Communitie

ArXiv Preprint
The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general population's lives, few studies are focused on the LGBTQ population. In this paper, we identify a group of Twitter users who self-disclose to belong to the LGBTQ community. We develop and evaluate two sets of machine learning classifiers using a pre-pandemic and a during pandemic dataset to identify Twitter posts exhibiting minority stress, which is a unique pressure faced by the members of the LGBTQ population due to their sexual and gender identities. For this task, we collect a set of 20,593,823 posts by 7,241 self-disclosed LGBTQ users and annotate a randomly selected subset of 2800 posts. We demonstrate that our best pre-pandemic and during pandemic models show strong and stable performance for detecting posts that contain minority stress. We investigate the linguistic differences in minority stress posts across pre- and during-pandemic periods. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic. We explore the impact of the pandemic on the emotional states of the LGBTQ population by conducting controlled comparisons with the general population. We adopt propensity score-based matching to perform a causal analysis. The results show that the LBGTQ population have a greater increase in the usage of cognitive words and worsened observable attribute in the usage of positive emotion words than the group of the general population with similar pre-pandemic behavioral attributes.
Yunhao Yuan, Gaurav Verma, Barbara Keller, Talayeh Aledavood

2022-05-19

General

Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning.

In IT professional
The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.
Rehman Amjad, Sadad Tariq, Saba Tanzila, Hussain Ayyaz, Tariq Usman

2021-Jul-01

General

Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression

ArXiv Preprint
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges, we propose a psychiatric scale guided risky post screening method that can capture risky posts related to the dimensions defined in clinical depression scales, and providing interpretable diagnostic basis. A Hierarchical Attentional Network equipped with BERT (HAN-BERT) is proposed to further advance explainable predictions. For ERD, we propose an online algorithm based on an evolving queue of risky posts that can significantly reduce the number of model inferences to boost efficiency. Experiments show that our method outperforms the competitive feature-based and neural models under conventional depression detection settings, and achieves simultaneous improvement in both efficacy and efficiency for ERD.
Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Q. Zhu

2022-05-19

General

Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison.

In IEEE transactions on artificial intelligence
Amidst the ongoing pandemic, the assessment of computed tomography (CT) images for COVID-19 presence can exceed the workload capacity of radiologists. Several studies addressed this issue by automating COVID-19 classification and grading from CT images with convolutional neural networks (CNNs). Many of these studies reported initial results of algorithms that were assembled from commonly used components. However, the choice of the components of these algorithms was often pragmatic rather than systematic and systems were not compared to each other across papers in a fair manner. We systematically investigated the effectiveness of using 3-D CNNs instead of 2-D CNNs for seven commonly used architectures, including DenseNet, Inception, and ResNet variants. For the architecture that performed best, we furthermore investigated the effect of initializing the network with pretrained weights, providing automatically computed lesion maps as additional network input, and predicting a continuous instead of a categorical output. A 3-D DenseNet-201 with these components achieved an area under the receiver operating characteristic curve of 0.930 on our test set of 105 CT scans and an AUC of 0.919 on a publicly available set of 742 CT scans, a substantial improvement in comparison with a previously published 2-D CNN. This article provides insights into the performance benefits of various components for COVID-19 classification and grading systems. We have created a challenge on grand-challenge.org to allow for a fair comparison between the results of this and future research.
de Vente Coen, Boulogne Luuk H, Venkadesh Kiran Vaidhya, Sital Cheryl, Lessmann Nikolas, Jacobs Colin, Sanchez Clara I, van Ginneken Bram

2022-Apr

CO-RADS, 3-D convolutional neural network (CNN), COVID-19, deep learning, medical imaging

General

A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topics

ArXiv Preprint
The COVID-19 pandemic has been accompanied by an `infodemic' -- of accurate and inaccurate health information across social media. Detecting misinformation amidst dynamically changing information landscape is challenging; identifying relevant keywords and posts is arduous due to the large amount of human effort required to inspect the content and sources of posts. We aim to reduce the resource cost of this process by introducing a weakly-supervised iterative graph-based approach to detect keywords, topics, and themes related to misinformation, with a focus on COVID-19. Our approach can successfully detect specific topics from general misinformation-related seed words in a few seed texts. Our approach utilizes the BERT-based Word Graph Search (BWGS) algorithm that builds on context-based neural network embeddings for retrieving misinformation-related posts. We utilize Latent Dirichlet Allocation (LDA) topic modeling for obtaining misinformation-related themes from the texts returned by BWGS. Furthermore, we propose the BERT-based Multi-directional Word Graph Search (BMDWGS) algorithm that utilizes greater starting context information for misinformation extraction. In addition to a qualitative analysis of our approach, our quantitative analyses show that BWGS and BMDWGS are effective in extracting misinformation-related content compared to common baselines in low data resource settings. Extracting such content is useful for uncovering prevalent misconceptions and concerns and for facilitating precision public health messaging campaigns to improve health behaviors.
Harry Wang, Sharath Chandra Guntuku

2022-05-19

General

Disinformation detection on social media: An integrated approach.

In Multimedia tools and applications
The emergence of social media platforms has amplified the dissemination of false information in various forms. Social media gives rise to virtual societies by providing freedom of expression to users in a democracy. Due to the presence of echo chambers on social media, social science studies play a vital role in the spread of false news. To this aim, we provide a comprehensive framework that is adapted from several scholarly studies. The framework is capable of detecting information into various types, namely real, disinformation and satire based on authenticity as well as intention. The process highlights the use of interdisciplinary approaches derived from fundamental theories of social science and integrating them with modern computational tools and techniques. Few of these theories claim that malicious users suggest writing fabricated content in a different style to attract the audience. Style-based methods evaluate the intention i.e., the content is written with an intent to mislead the audience or not. However, the writing style can be deceptive. Thus, it is important to involve user-oriented social information to improve model strength. Therefore, the paper used an integrated approach by combining style based and propagation-based features with a total of thirty-one features. The extracted features are divided into ten categories: relative frequency, quantity, complexity, uncertainty, sentiment, subjectivity, diversity, informality, additional, and popularity. The features have been iteratively utilized by supervised classifiers and then selected the best-correlated ones using the ANOVA test. Our experimental results have shown that the selected features are able to distinguish real from disinformation and satirical news. It has been observed that the Ensemble machine learning model outperformed other models over the developed multi-labelled corpus.
Rastogi Shubhangi, Bansal Divya

2022-May-12

Covid-19, Disinformation, Ensemble, Fake, Machine learning, Neural network, Satire

Oncology

TransTab: Learning Transferable Tabular Transformers Across Tables

ArXiv Preprint
Tabular data (or tables) are the most widely used data format in machine learning (ML). However, ML models often assume the table structure keeps fixed in training and testing. Before ML modeling, heavy data cleaning is required to merge disparate tables with different columns. This preprocessing often incurs significant data waste (e.g., removing unmatched columns and samples). How to learn ML models from multiple tables with partially overlapping columns? How to incrementally update ML models as more columns become available over time? Can we leverage model pretraining on multiple distinct tables? How to train an ML model which can predict on an unseen table? To answer all those questions, we propose to relax fixed table structures by introducing a Transferable Tabular Transformer (TransTab) for tables. The goal of TransTab is to convert each sample (a row in the table) to a generalizable embedding vector, and then apply stacked transformers for feature encoding. One methodology insight is combining column description and table cells as the raw input to a gated transformer model. The other insight is to introduce supervised and self-supervised pretraining to improve model performance. We compare TransTab with multiple baseline methods on diverse benchmark datasets and five oncology clinical trial datasets. Overall, TransTab ranks 1.00, 1.00, 1.78 out of 12 methods in supervised learning, feature incremental learning, and transfer learning scenarios, respectively; and the proposed pretraining leads to 2.3\% AUC lift on average over the supervised learning.}
Zifeng Wang, Jimeng Sun

2022-05-19

General

Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons.

In IT professional
Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.
Rehman Amjad, Saba Tanzila, Tariq Usman, Ayesha Noor

2021-May-01

Radiology

Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging

ArXiv Preprint
Details of the designs and mechanisms in support of human-AI collaboration must be considered in the real-world fielding of AI technologies. A critical aspect of interaction design for AI-assisted human decision making are policies about the display and sequencing of AI inferences within larger decision-making workflows. We have a poor understanding of the influences of making AI inferences available before versus after human review of a diagnostic task at hand. We explore the effects of providing AI assistance at the start of a diagnostic session in radiology versus after the radiologist has made a provisional decision. We conducted a user study where 19 veterinary radiologists identified radiographic findings present in patients' X-ray images, with the aid of an AI tool. We employed two workflow configurations to analyze (i) anchoring effects, (ii) human-AI team diagnostic performance and agreement, (iii) time spent and confidence in decision making, and (iv) perceived usefulness of the AI. We found that participants who are asked to register provisional responses in advance of reviewing AI inferences are less likely to agree with the AI regardless of whether the advice is accurate and, in instances of disagreement with the AI, are less likely to seek the second opinion of a colleague. These participants also reported the AI advice to be less useful. Surprisingly, requiring provisional decisions on cases in advance of the display of AI inferences did not lengthen the time participants spent on the task. The study provides generalizable and actionable insights for the deployment of clinical AI tools in human-in-the-loop systems and introduces a methodology for studying alternative designs for human-AI collaboration. We make our experimental platform available as open source to facilitate future research on the influence of alternate designs on human-AI workflows.
Riccardo Fogliato, Shreya Chappidi, Matthew Lungren, Michael Fitzke, Mark Parkinson, Diane Wilson, Paul Fisher, Eric Horvitz, Kori Inkpen, Besmira Nushi

2022-05-19

General

A Deep Learning Framework Integrating the Spectral and Spatial Features for Image-Assisted Medical Diagnostics.

In IEEE access : practical innovations, open solutions
The development of a computer-aided disease detection system to ease the long and arduous manual diagnostic process is an emerging research interest. Living through the recent outbreak of the COVID-19 virus, we propose a machine learning and computer vision algorithms-based automatic diagnostic solution for detecting the COVID-19 infection. Our proposed method applies to chest radiograph that uses readily available infrastructure. No studies in this direction have considered the spatial aspect of the medical images. This motivates us to investigate the role of spectral-domain information of medical images along with the spatial content towards improved disease detection ability. Successful integration of spatial and spectral features is demonstrated on the COVID-19 infection detection task. Our proposed method comprises three stages - Feature extraction, Dimensionality reduction via projection, and prediction. At first, images are transformed into spectral and spatio-spectral domains by using Discrete cosine transform (DCT) and Discrete Wavelet transform (DWT), two powerful image processing algorithms. Next, features from spatial, spectral, and spatio-spectral domains are projected into a lower dimension through the Convolutional Neural Network (CNN), and those three types of projected features are then fed to Multilayer Perceptron (MLP) for final prediction. The combination of the three types of features yielded superior performance than any of the features when used individually. This indicates the presence of complementary information in the spectral domain of the chest radiograph to characterize the considered medical condition. Moreover, saliency maps corresponding to classes representing different medical conditions demonstrate the reliability of the proposed method. The study is further extended to identify different medical conditions using diverse medical image datasets and shows the efficiency of leveraging the combined features. Altogether, the proposed method exhibits potential as a generalized and robust medical image-assisted diagnostic solution.
Ghosh Susmita, Das Swagatam, Mallipeddi Rammohan

2021

COVID-19 detection, Medical imaging, class imbalance, deep learning, diagnostic solution, discrete cosine transform, discrete wavelet transform, saliency map

General

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method

bioRxiv Preprint
The World Health Organization (WHO) introduced "Coronavirus disease 19" or "COVID19" as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These "representative genes" are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks.
Taheri, G.; Habibi, M.

2022-05-19

Radiology

A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis

ArXiv Preprint
Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects peripheral synovial joints, like fingers, wrist and feet. Radiology plays a critical role in the diagnosis and monitoring of RA. Limited by the current spatial resolution of radiographic imaging, joint space narrowing (JSN) progression of RA with the same reason above can be less than one pixel per year with universal spatial resolution. Insensitive monitoring of JSN can hinder the radiologist/rheumatologist from making a proper and timely clinical judgment. In this paper, we propose a novel and sensitive method that we call partial image phase-only correlation which aims to automatically quantify JSN progression in the early stages of RA. The majority of the current literature utilizes the mean error, root-mean-square deviation and standard deviation to report the accuracy at pixel level. Our work measures JSN progression between a baseline and its follow-up finger joint images by using the phase spectrum in the frequency domain. Using this study, the mean error can be reduced to 0.0130mm when applied to phantom radiographs with ground truth, and 0.0519mm standard deviation for clinical radiography. With its sub-pixel accuracy far beyond manual measurement, we are optimistic that our work is promising for automatically quantifying JSN progression.
Yafei Ou, Prasoon Ambalathankandy, Ryunosuke Furuya, Seiya Kawada, Tianyu Zeng, Yujie An, Tamotsu Kamishima, Kenichi Tamura, Masayuki Ikebe

2022-05-19

Radiology

Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data.

In BMJ open

OBJECTIVE : To examine sex and gender roles in COVID-19 test positivity and hospitalisation in sex-stratified predictive models using machine learning.

DESIGN : Cross-sectional study.

SETTING : UK Biobank prospective cohort.

PARTICIPANTS : Participants tested between 16 March 2020 and 18 May 2020 were analysed.

MAIN OUTCOME MEASURES : The endpoints of the study were COVID-19 test positivity and hospitalisation. Forty-two individuals' demographics, psychosocial factors and comorbidities were used as likely determinants of outcomes. Gradient boosting machine was used for building prediction models.

RESULTS : Of 4510 individuals tested (51.2% female, mean age=68.5±8.9 years), 29.4% tested positive. Males were more likely to be positive than females (31.6% vs 27.3%, p=0.001). In females, living in more deprived areas, lower income, increased low-density lipoprotein (LDL) to high-density lipoprotein (HDL) ratio, working night shifts and living with a greater number of family members were associated with a higher likelihood of COVID-19 positive test. While in males, greater body mass index and LDL to HDL ratio were the factors associated with a positive test. Older age and adverse cardiometabolic characteristics were the most prominent variables associated with hospitalisation of test-positive patients in both overall and sex-stratified models.

CONCLUSION : High-risk jobs, crowded living arrangements and living in deprived areas were associated with increased COVID-19 infection in females, while high-risk cardiometabolic characteristics were more influential in males. Gender-related factors have a greater impact on females; hence, they should be considered in identifying priority groups for COVID-19 infection vaccination campaigns.

Azizi Zahra, Shiba Yumika, Alipour Pouria, Maleki Farhad, Raparelli Valeria, Norris Colleen, Forghani Reza, Pilote Louise, El Emam Khaled

2022-May-18

COVID-19, health policy, risk management

General

Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough.

In IEEE journal of selected topics in signal processing
The COVID-19 pandemic created significant interest and demand for infection detection and monitoring solutions. In this paper, we propose a machine learning method to quickly detect COVID-19 using audio recordings made on consumer devices. The approach combines signal processing and noise removal methods with an ensemble of fine-tuned deep learning networks and enables COVID detection on coughs. We have also developed and deployed a mobile application that uses a symptoms checker together with voice, breath, and cough signals to detect COVID-19 infection. The application showed robust performance on both openly sourced datasets and the noisy data collected during beta testing by the end users.
Ponomarchuk Alexander, Burenko Ilya, Malkin Elian, Nazarov Ivan, Kokh Vladimir, Avetisian Manvel, Zhukov Leonid

2022-Feb

Acoustic signal processing, Big Data applications, biomedical informatics, machine learning, public heathcare, signal detection

Public Health

A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19.

In Acta astronautica
The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.
Atek Sofiane, Pesaresi Cristiano, Eugeni Marco, De Vito Corrado, Cardinale Vincenzo, Mecella Massimo, Rescio Antonello, Petronzio Luca, Vincenzi Aldo, Pistillo Pasquale, Bianchini Filippo, Giusto Gianfranco, Pasquali Giorgio, Gaudenzi Paolo

2022-May-13

Business intelligence, COVID-19, Decision support system, Earth observation, Geospatial artificial intelligence

Radiology

Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification.

In Applied soft computing
The COVID-19 pandemic continues to wreak havoc on the world's population's health and well-being. Successful screening of infected patients is a critical step in the fight against it, with radiology examination using chest radiography being one of the most important screening methods. For the definitive diagnosis of COVID-19 disease, reverse-transcriptase polymerase chain reaction remains the gold standard. Currently available lab tests may not be able to detect all infected individuals; new screening methods are required. We propose a Multi-Input Transfer Learning COVID-Net fuzzy convolutional neural network to detect COVID-19 instances from torso X-ray, motivated by the latter and the open-source efforts in this research area. Furthermore, we use an explainability method to investigate several Convolutional Networks COVID-Net forecasts in an effort to not only gain deeper insights into critical factors associated with COVID-19 instances, but also to aid clinicians in improving screening. We show that using transfer learning and pre-trained models, we can detect it with a high degree of accuracy. Using X-ray images, we chose four neural networks to predict its probability. Finally, in order to achieve better results, we considered various methods to verify the techniques proposed here. As a result, we were able to create a model with an AUC of 1.0 and accuracy, precision, and recall of 0.97. The model was quantized for use in Internet of Things devices and maintained a 0.95 percent accuracy.
Hu Qinhua, Gois Francisco Nauber B, Costa Rafael, Zhang Lijuan, Yin Ling, Magai Naercio, de Albuquerque Victor Hugo C

2022-May-13

COVID-19, Intern of Things, Multi-input convolutional network, Soft computing, X-ray, XAI

General

Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests.

In IEEE transactions on instrumentation and measurement
A sample blood test has recently become an important tool to help identify false-positive/false-negative real-time reverse transcription polymerase chain reaction (rRT-PCR) tests. Importantly, this is mainly because it is an inexpensive and handy option to detect the potential COVID-19 patients. However, this test should be conducted by certified laboratories, expensive equipment, and trained personnel, and 3-4 h are needed to deliver results. Furthermore, it has relatively large false-negative rates around 15%-20%. Consequently, an alternative and more accessible solution, quicker and less costly, is needed. This article introduces flexible and unsupervised data-driven approaches to detect the COVID-19 infection based on blood test samples. In other words, we address the problem of COVID-19 infection detection using a blood test as an anomaly detection problem through an unsupervised deep hybrid model. Essentially, we amalgamate the features extraction capability of the variational autoencoder (VAE) and the detection sensitivity of the one-class support vector machine (1SVM) algorithm. Two sets of routine blood tests samples from the Albert Einstein Hospital, S ao Paulo, Brazil, and the San Raffaele Hospital, Milan, Italy, are used to assess the performance of the investigated deep learning models. Here, missing values have been imputed based on a random forest regressor. Compared to generative adversarial networks (GANs), deep belief network (DBN), and restricted Boltzmann machine (RBM)-based 1SVM, the traditional VAE, GAN, DBN, and RBM with softmax layer as discriminator layer, and the standalone 1SVM, the proposed VAE-based 1SVM detector offers superior discrimination performance of potential COVID-19 infections. Results also revealed that the deep learning-driven 1SVM detection approaches provide promising detection performance compared to the conventional deep learning models.
Dairi Abdelkader, Harrou Fouzi, Sun Ying

2022

COVID-19, deep learning, generative models, routine blood tests, unsupervised anomaly detection

General

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

In IEEE access : practical innovations, open solutions
In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.
Shah Pir Masoom, Ullah Faizan, Shah Dilawar, Gani Abdullah, Maple Carsten, Wang Yulin, Shahid Abrar, Mohammad Islam

2022

CNN, COVID-19, GRU, Medical data, chest X-rays, deep learning

General

HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

In Briefings in bioinformatics
Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional HLA-based immunotherapy, particularly against cancer and COVID-19 infection. In the last two decades, several methods have been developed to predict the binders of classical HLA alleles. In contrast, limited attempts have been made to develop methods for predicting non-classical HLA binding peptides, due to the scarcity of sufficient experimental data. Of note, in order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of class-Ib HLA alleles. All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning models achieved more than 0.98 AUC for HLA-G alleles on validation dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.94 on the validation dataset for HLA-E01:01 and HLA-E01:03, respectively. We have summarized the models developed in the past for non-classical HLA and validated the performance with the models developed in this study. Moreover, to facilitate the community, we have utilized our tool for predicting the potential non-classical HLA binding peptides in the spike protein of different variants of virus causing COVID-19, including Omicron (B.1.1.529). One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. To predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred) and standalone package.
Dhall Anjali, Patiyal Sumeet, Raghava Gajendra P S

2022-May-18

COVID-19, binders, non-classical HLA, prediction, standalone, web server

Radiology

The state of the art for artificial intelligence in lung digital pathology.

In The Journal of pathology
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI) based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung digital pathology, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung digital pathology such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. This article is protected by copyright. All rights reserved.
Viswanathan Vidya Sankar, Toro Paula, Corredor Germán, Mukhopadhyay Sanjay, Madabhushi Anant

2022-May-17

Artificial Intelligence, Computational Pathology, Digital Pathology, Lung Diseases, Machine Learning

General

Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data.

In Drug safety

INTRODUCTION : Adverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients' adverse drug reaction reports.

OBJECTIVES : We aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal.

METHODS : Reports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period.

RESULTS : Internal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results.

CONCLUSIONS : Our artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.

Martin Guillaume L, Jouganous Julien, Savidan Romain, Bellec Axel, Goehrs Clément, Benkebil Mehdi, Miremont Ghada, Micallef Joëlle, Salvo Francesco, Pariente Antoine, Létinier Louis

2022-May

Internal Medicine

Development and evaluation of a machine learning-based in-hospital COvid-19 disease outcome predictor (CODOP): a multicontinental retrospective study.

In eLife
New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to nine days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.
Klén Riku, Purohit Disha, Gómez-Huelgas Ricardo, Casas-Rojo José Manuel, Antón-Santos Juan Miguel, Nunez-Cortes Jesus Millan, Lumbreras Carlos, Ramos-Rincon Jose Manuel, Garcia Barrio Noelia, Pedrera Jimenez Miguel, Lalueza Blanco Antonio, Martin-Escalante María Dolores, Rivas-Ruiz Francisco, Onieva-Garcia Mari Ángeles, Young Pablo, Ramirez Juan Ignacio, Titto Omonte Estela Edith, Gross Artega Rosmery, Canales Beltrán Magdy Teresa, Valdez Pascual Ruben, Pugliese Florencia, Castagna Rosa, Huesped Ivan, Boietti Bruno, Pollan Javier A, Funke Nico, Leiding Benjamin, Gómez-Varela David

2022-May-17

computational biology, human, medicine, systems biology

Pathology

Human phospho-signaling networks of SARS-CoV-2 infection are rewired by population genetic variants.

In Molecular systems biology
SARS-CoV-2 infection hijacks signaling pathways and induces protein-protein interactions between human and viral proteins. Human genetic variation may impact SARS-CoV-2 infection and COVID-19 pathology; however, the genetic variation in these signaling networks remains uncharacterized. Here, we studied human missense single nucleotide variants (SNVs) altering phosphorylation sites modulated by SARS-CoV-2 infection, using machine learning to identify amino acid substitutions altering kinase-bound sequence motifs. We found 2,033 infrequent phosphorylation-associated SNVs (pSNVs) that are enriched in sequence motif alterations, potentially reflecting the evolution of signaling networks regulating host defenses. Proteins with pSNVs are involved in viral life cycle and host responses, including RNA splicing, interferon response (TRIM28), and glucose homeostasis (TBC1D4) with potential associations with COVID-19 comorbidities. pSNVs disrupt CDK and MAPK substrate motifs and replace these with motifs of Tank Binding Kinase 1 (TBK1) involved in innate immune responses, indicating consistent rewiring of signaling networks. Several pSNVs associate with severe COVID-19 and hospitalization (STARD13, ARFGEF2). Our analysis highlights potential genetic factors contributing to inter-individual variation of SARS-CoV-2 infection and COVID-19 and suggests leads for mechanistic and translational studies.
Pellegrina Diogo, Bahcheli Alexander T, Krassowski Michal, Reimand Jüri

2022-May

General

Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.

In SN computer science
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
Meraihi Yassine, Gabis Asma Benmessaoud, Mirjalili Seyedali, Ramdane-Cherif Amar, Alsaadi Fawaz E

2022

Artificial intelligence, CNN, COVID-19 detection, COVID-19 diagnosis, COVID-19 prediction, Deep learning, Machine learning

General

Effective Multiscale Deep Learning Model for COVID19 Segmentation Tasks: A Further Step Towards helping Radiologist.

In Neurocomputing
Infection by the SARS-CoV-2 leading to COVID-19 disease is still rising and techniques to either diagnose or evaluate the disease are still thoroughly investigated. The use of CT as a complementary tool to other biological tests is still under scrutiny as the CT scans are prone to many false positives as other lung diseases display similar characteristics on CT scans. However, fully investigating CT images is of tremendous interest to better understand the disease progression and therefore thousands of scans need to be segmented by radiologists to study infected areas. Over the last year, many deep learning models for segmenting CT-lungs were developed. Unfortunately, the lack of large and shared annotated multicentric datasets led to models that were either under-tested (small dataset) or not properly compared (own metrics, none shared dataset), often leading to poor generalization performance. To address, these issues, we developed a model that uses a multiscale and multilevel feature extraction strategy for COVID19 segmentation and extensively validated it on several datasets to assess its generalization capability for other segmentation tasks on similar organs. The proposed model uses a novel encoder and decoder with a proposed kernel-based atrous spatial pyramid pooling module that is used at the bottom of the model to extract small features with a multistage skip connection concatenation approach. The results proved that our proposed model could be applied on a small-scale dataset and still produce generalizable performances on other segmentation tasks. The proposed model produced an efficient Dice score of 90 % on a 100 cases dataset, 95 % on the NSCLC dataset, 88.49 % on the COVID19 dataset, and 97.33 on the StructSeg 2019 dataset as compared to existing state-of-the-art models. The proposed solution could be used for COVID19 segmentation in clinic applications. The source code is publicly available at https://github.com/RespectKnowledge/Mutiscale-based-Covid-_segmentation-usingDeep-Learning-models.
Qayyum Abdul, Lalande Alain, Meriaudeau Fabrice

2022-May-12

COVID-19 CT segmentation, Multiscale, Deep Learning Models, Lung segmentation

General

Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19

bioRxiv Preprint
Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. Results: We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19.
Taheri, G.; Habibi, M.

2022-05-18

General

Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction

bioRxiv Preprint
Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the ACE2 receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly-compiled database of 96 species we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8 - 87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5 - 91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.
Mollentze, N.; Keen, D.; Munkhbayar, U.; Biek, R.; Streicker, D. G.

2022-05-17

General

Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil.

In Plants (Basel, Switzerland)
Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture.
Ben Ayed Rayda, Hanana Mohsen, Ercisli Sezai, Karunakaran Rohini, Rebai Ahmed, Moreau Fabienne

2022-May-02

DNA technologies, artificial intelligence, big data, blockchain, internet of things, olive fruit, smart agriculture

General

The 3D-Printing-Accelerated Design for a Biodegradable Respirator from Tree Leaves (TRespirator).

In Polymers
The unpredictable coronavirus pandemic (COVID-19) has led to a sudden and massive demand for face masks, leading to severe plastic pollution. Here, we propose a method for manufacturing biodegradable masks using high-precision 3D printing technology, called "TRespirator", mainly made of banana leaves and dental floss silk fibers. By adding plastic recycling waste appropriately, TRespirator can achieve similar protection and mechanical properties as N95 masks. In addition, microorganisms attracted during the degradation of plant fibers will accelerate the degradation of microplastics. This respirator provides a new idea for solving the global problem of plastic pollution of masks.
Wang Ziao, Xu Yao, Liu Rulin, Zhu Xi

2022-Apr-21

biodegradable masks, high precision 3D printing, mechanical properties, microplastic, plastic pollution problem

General

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning.

In Molecules (Basel, Switzerland)
Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.
Tayebi Aida, Yousefi Niloofar, Yazdani-Jahromi Mehdi, Kolanthai Elayaraja, Neal Craig J, Seal Sudipta, Garibay Ozlem Ozmen

2022-May-06

ACE2 receptor, SARS-CoV-2, deep learning, drug-target interaction, ensemble learning, machine learning, spike protein

Public Health

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques.

In International journal of environmental research and public health ; h5-index 73.0
The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were "family", "anxiety", "house", and "life". Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.
Jojoa Mario, Garcia-Zapirain Begonya, Gonzalez Marino J, Perez-Villa Bernardo, Urizar Elena, Ponce Sara, Tobar-Blandon Maria Fernanda

2022-May-07

COVID-19, Swivel embedding, continents, habits, institutions, mental health, natural language processing, online learning, perception, satisfaction, socio-demographic factors, university student, word cloud

General

Changes in Air-Pollution-Related Information-Seeking Behaviour during the COVID-19 Pandemic in Poland.

In International journal of environmental research and public health ; h5-index 73.0
Low air quality in Poland is a problem of particularly high urgency. Therefore, Poles must be aware of air quality levels, also during the COVID-19 pandemic. The study aimed to compare air-pollution-related information-seeking behaviour between the pre- and intra-pandemic periods as well as between the actual and theoretical machine-learning-forecasted intra-pandemic models. Google Trends search volumes (GTSVs) in Poland for air-pollution-related keywords were collected between January 2016 and January 2022. To investigate the changes that would have occurred without the outbreak of the pandemic, Seasonal Autoregressive Integrated Moving Average (SARIMA) machine-learning models were trained. Approximately 4,500,000 search queries were analysed. Between pre- and intra-pandemic periods, weighted mean GTSVs changed by -39.0%. When the actual intra-pandemic weighted mean GTSVs were compared to the intra-pandemic forecasts, the actual values were lower by -16.5% (SARIMA's error = 6.2%). Compared to the pre-pandemic period, in the intra-pandemic period, the number of search queries containing keywords connected with air pollution decreased. Moreover, the COVID-19 pandemic might have facilitated the decrease. Possible causes include an attention shift towards everyday problems connected to the pandemic, worse mental health status and lower outdoor exposure that might have resulted in a lower intensity of non-pandemic-related active information-seeking behaviour.
Nazar Wojciech, Niedoszytko Marek

2022-May-05

COVID-19, Poland, air pollution, information-seeking behaviour, machine learning

Public Health

Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic.

In International journal of environmental research and public health ; h5-index 73.0
Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.
Martin-Moreno Jose M, Alegre-Martinez Antoni, Martin-Gorgojo Victor, Alfonso-Sanchez Jose Luis, Torres Ferran, Pallares-Carratala Vicente

2022-May-03

COVID-19, explanatory models, forecasting, health policy, predictive models, public health

Public Health

Telemonitoring in Long-COVID Patients-Preliminary Findings.

In International journal of environmental research and public health ; h5-index 73.0
The COVID-19 pandemic has revealed the high usefulness of telemedicine. To date, no uniform recommendations or diagnostic protocols for long-COVID patients have been developed. This article presents the preliminary results of the examination of patients after SARS-CoV-2 infection who were provided with medical telemonitoring devices in order to oversee their pulmonological and cardiological health. Three cases have been analyzed. Each patient underwent a 10-day registration of basic vital signs, in three 15-min sessions daily: RR (respiratory rate), ECG (electrocardiogram), HR (pulse), SPO₂ (saturation), body temperature and cough. Rule methods and machine learning were employed to automatically detect events. As a result, serious disorders of all the three patients were detected: cardiological and respiratory disorders that required extended diagnostics. Furthermore, average values of the selected parameters (RR, HR, SPO₂) were calculated for every patient, including an indication of how often they exceeded the alarm thresholds. In conclusion, monitoring parameters in patients using telemedicine, especially in a time of limited access to the healthcare system, is a valuable clinical instrument. It enables medical professionals to recognize conditions which may endanger a patient's health or life. Telemedicine provides a reliable assessment of a patient's health status made over a distance, which can alleviate a patient's stress caused by long-COVID syndrome. Telemedicine allows identification of disorders and performing further diagnosis, which is possible owing to the implementation of advanced analysis. Telemedicine, however, requires flexibility and the engagement of a multidisciplinary team, who will respond to patients' problems on an ongoing basis.
Romaszko-Wojtowicz Anna, Maksymowicz Stanisław, Jarynowski Andrzej, Jaśkiewicz Łukasz, Czekaj Łukasz, Doboszyńska Anna

2022-Apr-26

cardiological and respiratory disorders, long-COVID, telemedicine

Public Health

Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study.

In International journal of environmental research and public health ; h5-index 73.0
Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring-in a very short time-of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration.
Improta Giovanni, Borrelli Anna, Triassi Maria

2022-Apr-26

COVID-19, DMAIC, Six Sigma, clinical pathway, health care, statistical analysis

General

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.

In International journal of environmental research and public health ; h5-index 73.0
COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.
Saleem Farrukh, Al-Ghamdi Abdullah Saad Al-Malaise, Alassafi Madini O, AlGhamdi Saad Abdulla

2022-Apr-22

basic reproduction rate, deep learning, epidemiology of COVID-19, machine learning

General

Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

In Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

BACKGROUND : Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns.

METHODS : A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States.

RESULTS : The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates.

CONCLUSIONS : Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

Bari Anasse, Heymann Matthias, Cohen Ryan J, Zhao Robin, Szabo Levente, Apas Vasandani Shailesh, Khubchandani Aashish, DiLorenzo Madeline, Coffee Megan

2022-May-15

COVID-19, Twitter, artificial intelligence, vaccination, vaccine hesitancy

General

Distinct immune cell dynamics correlate with the immunogenicity and reactogenicity of SARS-CoV-2 mRNA vaccine.

In Cell reports. Medicine
Two doses of Pfizer/BioNTech BNT162b2 mRNA vaccine elicit robust severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies with frequent adverse events. Here, by applying a high-dimensional immune profiling on 92 vaccinees, we identify six vaccine-induced immune dynamics that correlate with the amounts of neutralizing antibodies, the severity of adverse events, or both. The early dynamics of natural killer (NK)/monocyte subsets (CD16⁺ NK cells, CD56^high NK cells, and non-classical monocytes), dendritic cell (DC) subsets (DC3s and CD11c^- Axl⁺ Siglec-6⁺ [AS]-DCs), and NKT-like cells are revealed as the distinct cell correlates for neutralizing-antibody titers, severity of adverse events, and both, respectively. The cell correlates for neutralizing antibodies or adverse events are consistently associated with elevation of interferon gamma (IFN-γ)-inducible chemokines, but the chemokine receptors CCR2 and CXCR3 are expressed in distinct manners between the two correlates: vaccine-induced expression on the neutralizing-antibody correlate and constitutive expression on the adverse-event correlate. The finding may guide vaccine strategies that balance immunogenicity and reactogenicity.
Takano Tomohiro, Morikawa Miwa, Adachi Yu, Kabasawa Kiyomi, Sax Nicolas, Moriyama Saya, Sun Lin, Isogawa Masanori, Nishiyama Ayae, Onodera Taishi, Terahara Kazutaka, Tonouchi Keisuke, Nishimura Masashi, Tomii Kentaro, Yamashita Kazuo, Matsumura Takayuki, Shinkai Masaharu, Takahashi Yoshimasa

2022-Apr-22

SARS-CoV-2, adverse events, cell dynamics, immune correlates, innate immunity, mRNA vaccine, neutralizing antibodies

General

SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.

In Medical image analysis
Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment. Unfortunately, due to the urgent nature of the COVID-19 pandemic, a systematic collection of CT data sets for deep neural network training is quite difficult, especially high-quality annotations of multi-category infections are limited. In addition, it is still a challenge to segment the infected areas from CT slices because of the irregular shapes and fuzzy boundaries. To solve these issues, we propose a novel COVID-19 pneumonia lesion segmentation network, called Spatial Self-Attention network (SSA-Net), to identify infected regions from chest CT images automatically. In our SSA-Net, a self-attention mechanism is utilized to expand the receptive field and enhance the representation learning by distilling useful contextual information from deeper layers without extra training time, and spatial convolution is introduced to strengthen the network and accelerate the training convergence. Furthermore, to alleviate the insufficiency of labeled multi-class data and the long-tailed distribution of training data, we present a semi-supervised few-shot iterative segmentation framework based on re-weighting the loss and selecting prediction values with high confidence, which can accurately classify different kinds of infections with a small number of labeled image data. Experimental results show that SSA-Net outperforms state-of-the-art medical image segmentation networks and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Meanwhile, our semi-supervised iterative segmentation model can improve the learning ability in small and unbalanced training set and can achieve higher performance.
Wang Xiaoyan, Yuan Yiwen, Guo Dongyan, Huang Xiaojie, Cui Ying, Xia Ming, Wang Zhenhua, Bai Cong, Chen Shengyong

2022-Apr-22

COVID-19, Few-shot learning, Lesion segmentation, Semi-supervised

Surgery

Using Artificial Intelligence to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY): Protocol for the Development of a Clinical Prediction Model.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Hip and knee osteoarthritis is substantially prevalent worldwide, with large numbers of older adults undergoing joint replacement (arthroplasty) every year. A backlog of elective surgery due to the COVID-19 pandemic, and an aging population, has led to substantial issues with access to timely arthroplasty surgery. A potential method to improve the efficiency of arthroplasty services is by increasing the percentage of patients who are listed for surgery from primary care referrals. The use of artificial intelligence (AI) techniques, specifically machine learning, provides a potential unexplored solution to correctly and rapidly select suitable patients for arthroplasty surgery.

OBJECTIVE : This study has 2 objectives: (1) develop a cohort of patients with referrals by general practitioners regarding assessment of suitability for hip or knee replacement from National Health Service (NHS) Grampian data via the Grampian Data Safe Haven and (2) determine the demographic, clinical, and imaging characteristics that influence the selection of patients to undergo hip or knee arthroplasty, and develop a tested and validated patient-specific predictive model to guide arthroplasty referral pathways.

METHODS : The AI to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY) project will be delivered through 2 linked work packages conducted within the Grampian Data Safe Haven and Safe Haven Artificial Intelligence Platform. The data set will include a cohort of individuals aged ≥16 years with referrals for the consideration of elective primary hip or knee replacement from January 2015 to January 2022. Linked pseudo-anonymized NHS Grampian health care data will be acquired including patient demographics, medication records, laboratory data, theatre records, text from clinical letters, and radiological images and reports. Following the creation of the data set, machine learning techniques will be used to develop pattern classification and probabilistic prediction models based on radiological images. Supplemental demographic and clinical data will be used to improve the predictive capabilities of the models. The sample size is predicted to be approximately 2000 patients-a sufficient size for satisfactory assessment of the primary outcome. Cross-validation will be used for development, testing, and internal validation. Evaluation will be performed through standard techniques, such as the C statistic (area under curve) metric, calibration characteristics (Brier score), and a confusion matrix.

RESULTS : The study was funded by the Chief Scientist Office Scotland as part of a Clinical Research Fellowship that runs from August 2021 to August 2024. Approval from the North Node Privacy Advisory Committee was confirmed on October 13, 2021. Data collection started in May 2022, with the results expected to be published in the first quarter of 2024. ISRCTN registration has been completed.

CONCLUSIONS : This project provides a first step toward delivering an automated solution for arthroplasty selection using routinely collected health care data. Following appropriate external validation and clinical testing, this project could substantially improve the proportion of referred patients that are selected to undergo surgery, with a subsequent reduction in waiting time for arthroplasty appointments.

TRIAL REGISTRATION : ISRCTN Registry ISRCTN18398037; https://www.isrctn.com/ISRCTN18398037.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : PRR1-10.2196/37092.

Farrow Luke, Ashcroft George Patrick, Zhong Mingjun, Anderson Lesley

2022-May-11

arthritis, arthroplasty, artificial intelligence, health care, hip, imaging, knee, machine learning, orthopedics, patient care, prediction modelling

General

Rapid Method to Quantify the Antiviral Potential of Porous and Nonporous Material Using the Enveloped Bacteriophage Phi6.

In Environmental science & technology ; h5-index 132.0
The pandemic revealed significant gaps in our understanding of the antiviral potential of porous textiles used for personal protective equipment and nonporous touch surfaces. What is the fate of a microbe when it encounters an abiotic surface? How can we change the microenvironment of materials to improve antimicrobial properties? Filling these gaps requires increasing data generation throughput. A method to accomplish this leverages the use of the enveloped bacteriophage ϕ6, an adjustable spacing multichannel pipette, and the statistical design opportunities inherent in the ordered array of the 24-well culture plate format, resulting in a semi-automated small drop assay. For 100 mm² nonporous coupons of Cu and Zn, the reduction in ϕ6 infectivity fits first-order kinetics, resulting in half-lives (T₅₀) of 4.2 ± 0.1 and 29.4 ± 1.6 min, respectively. In contrast, exposure to stainless steel has no significant effect on infectivity. For porous textiles, differences associated with composition, color, and surface treatment of samples are detected within 5 min of exposure. Half-lives for differently dyed Zn-containing fabrics from commercially available masks ranged from 2.1 ± 0.05 to 9.4 ± 0.2 min. A path toward full automation and the application of machine learning techniques to guide combinatorial material engineering is presented.
Reiss Rebecca A, Makhnin Oleg, Lowe Terry C

2022-May-11

SARS CoV-2, antimicrobial, antiviral, copper, materials testing, virucidal, zinc

General

Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions.

In Annals of operations research
In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence.
Goodarzian Fariba, Navaei Ali, Ehsani Behdad, Ghasemi Peiman, Muñuzuri Jesús

2022-May-05

COVID-19 epidemic, Green-cold vaccine supply chain network, Internet-of-Things, OR in medicine, Waste management

General

Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.

In Computer methods and programs in biomedicine

BACKGROUND : over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. In that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens. Consequently, efforts are being poured toward distinguishing the infection source to help clinicians to diagnose the correct disease origin. Following this tendency, this study further explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm.

METHODOLOGY : to present a comprehensive comparison, 12 well-known ImageNet pre-trained models were fine-tuned and used to discriminate among chest-x-rays of healthy people, and those showing pneumonia symptoms derived from either a viral (i.e., generic or SARS-CoV-2) or bacterial source. Furthermore, since a common public collection distinguishing between such categories is currently not available, two distinct datasets of chest-x-rays images, describing the aforementioned sources, were combined and employed to evaluate the various architectures.

RESULTS : the experiments were performed using a total of 6330 images split between train, validation, and test sets. For all models, standard classification metrics were computed (e.g., precision, f1-score), and most architectures obtained significant performances, reaching, among the others, up to 84.46% average f1-score when discriminating the four identified classes. Moreover, execution times, areas under the receiver operating characteristic (AUROC), confusion matrices, activation maps computed via the Grad-CAM algorithm, and additional experiments to assess the robustness of each model using only 50%, 20%, and 10% of the training set were also reported to present an informed discussion on the networks classifications.

CONCLUSION : this paper examines the effectiveness of well-known architectures on a joint collection of chest-x-rays presenting pneumonia cases derived from either viral or bacterial sources, with particular attention to SARS-CoV-2 contagions for viral pathogens; demonstrating that existing architectures can effectively diagnose pneumonia sources and suggesting that the transfer learning paradigm could be a crucial asset in diagnosing future unknown illnesses.

Avola Danilo, Bacciu Andrea, Cinque Luigi, Fagioli Alessio, Marini Marco Raoul, Taiello Riccardo

2022-Apr-22

Deep learning, Explainable AI, Pneumonia classification, Transfer learning

General

Riding the wave: Predicting the use of the bike-sharing system in Barcelona before and during COVID-19.

In Sustainable cities and society
To simultaneously promote health, economic, and environmental sustainability, a number of cities worldwide have established bike-sharing systems (BSS) that complement the conventional public transport systems. As the rapid spread of COVID-19 becoming a global pandemic disrupted urban mobility due to government-imposed lockdowns and the heightened fear of infection in crowded spaces, populations were increasingly less likely to use public transportation and instead shifted toward alternative transport systems, including BSS. In this study, we use probabilistic machine learning in a quasi-experimental research design to identify how the relevance of a comprehensive set of factors to predict the use of Bicing (the BSS in Barcelona) may have changed as COVID-19 unfolded. We unpack the key factors in predicting the use of Bicing, uncovering evidence of increasing bike-related built infrastructure (e.g., tactical urbanism), trip distance, and the income levels of neighborhoods as the most relevant predictors. Moreover, we find that the relevance of the factors in predicting Bicing usage has generally decreased during the global pandemic, suggesting altered societal behavior. Our study enhances the understanding of BSS and societal behavior, which can have important implications for developing resilient programs for cities to adopt sustainable practices through transport policy, infrastructure planning, and urban development.
Bustamante Xavier, Federo Ryan, Fernández-I-Marin Xavier

2022-Aug

Bicing Barcelona, Bike-sharing system, COVID-19, Probabilistic machine learning, Quasi-experimental research, Sustainability, Tactical urbanism

General

Non-invasive health prediction from visually observable features.

In F1000Research
Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person's health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.
Khong Fan Yi, Connie Tee, Goh Michael Kah Ong, Wong Li Pei, Teh Pin Shen, Choo Ai Ling

2021

Health prediction, Machine learning, Remote screening and diagnosis

General

The Conflict Between Explainable and Accountable Decision-Making Algorithms

ArXiv Preprint
Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility.
Gabriel Lima, Nina Grgić-Hlača, Jin Keun Jeong, Meeyoung Cha

2022-05-11

Surgery

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling.

In Frontiers in immunology ; h5-index 100.0
Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.
Elemam Noha M, Hammoudeh Sarah, Salameh Laila, Mahboub Bassam, Alsafar Habiba, Talaat Iman M, Habib Peter, Siddiqui Mehmood, Hassan Khalid Omar, Al-Assaf Omar Yousef, Taneera Jalal, Sulaiman Nabil, Hamoudi Rifat, Maghazachi Azzam A, Hamid Qutayba, Saber-Ayad Maha

2022

Aritficial Intelligence, COVID-19, Machine Learning, RNA seq, ROC analysis, multiplex, transcriptomics

General

DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans.

In Expert systems with applications
The recent disaster of COVID-19 has brought the whole world to the verge of devastation because of its highly transmissible nature. In this pandemic, radiographic imaging modalities, particularly, computed tomography (CT), have shown remarkable performance for the effective diagnosis of this virus. However, the diagnostic assessment of CT data is a human-dependent process that requires sufficient time by expert radiologists. Recent developments in artificial intelligence have substituted several personal diagnostic procedures with computer-aided diagnosis (CAD) methods that can make an effective diagnosis, even in real time. In response to COVID-19, various CAD methods have been developed in the literature, which can detect and localize infectious regions in chest CT images. However, most existing methods do not provide cross-data analysis, which is an essential measure for assessing the generality of a CAD method. A few studies have performed cross-data analysis in their methods. Nevertheless, these methods show limited results in real-world scenarios without addressing generality issues. Therefore, in this study, we attempt to address generality issues and propose a deep learning-based CAD solution for the diagnosis of COVID-19 lesions from chest CT images. We propose a dual multiscale dilated fusion network (DMDF-Net) for the robust segmentation of small lesions in a given CT image. The proposed network mainly utilizes the strength of multiscale deep features fusion inside the encoder and decoder modules in a mutually beneficial manner to achieve superior segmentation performance. Additional pre- and post-processing steps are introduced in the proposed method to address the generality issues and further improve the diagnostic performance. Mainly, the concept of post-region of interest (ROI) fusion is introduced in the post-processing step, which reduces the number of false-positives and provides a way to accurately quantify the infected area of lung. Consequently, the proposed framework outperforms various state-of-the-art methods by accomplishing superior infection segmentation results with an average Dice similarity coefficient of 75.7%, Intersection over Union of 67.22%, Average Precision of 69.92%, Sensitivity of 72.78%, Specificity of 99.79%, Enhance-Alignment Measure of 91.11%, and Mean Absolute Error of 0.026.
Owais Muhammad, Baek Na Rae, Park Kang Ryoung

2022-Sep-15

COVID-19 lesions segmentation, Computer-aided diagnosis, DMDF-Net, Infection quantification, Lung segmentation

General

Arabic fake news detection based on deep contextualized embedding models.

In Neural computing & applications
Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Arabic language is scarce. Our contribution is twofold: first, we have constructed a large and diverse Arabic fake news dataset. Second, we have developed and evaluated transformer-based classifiers to identify fake news while utilizing eight state-of-the-art Arabic contextualized embedding models. The majority of these models had not been previously used for Arabic fake news detection. We conduct a thorough analysis of the state-of-the-art Arabic contextualized embedding models as well as comparison with similar fake news detection systems. Experimental results confirm that these state-of-the-art models are robust, with accuracy exceeding 98%.
Nassif Ali Bou, Elnagar Ashraf, Elgendy Omar, Afadar Yaman

2022-May-03

Arabic fake news, Contextualized models, Deep learning, Natural language processing

General

Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemic.

In International journal of applied earth observation and geoinformation : ITC journal
The COVID-19 virus has caused and continues to cause unprecedented impacts on the life trajectories of millions of people globally. Recently, to combat the transmission of the virus, vaccination campaigns around the world have become prevalent. However, while many see such campaigns as positive (e.g., protecting lives), others see them as negative (e.g., the side effects that are not fully understood scientifically), resulting in diverse sentiments towards vaccination campaigns. In addition, the diverse sentiments have seldom been systematically quantified let alone their dynamic changes over space and time. To shed light on this issue, we propose an approach to analyze vaccine sentiments in space and time by using supervised machine learning combined with word embedding techniques. Taking the United States as a test case, we utilize a Twitter dataset (approximately 11.7 million tweets) from January 2015 to July 2021 and measure and map vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the nation. In doing so, we can capture the heterogeneous public opinions within social media discussions regarding vaccination among states. Results show how positive sentiment in social media has a strong correlation with the actual vaccinated population. Furthermore, we introduce a simple ratio between Anti and Pro-vaccine as a proxy to quantify vaccine hesitancy and show how our results align with other traditional survey approaches. The proposed approach illustrates the potential to monitor the dynamics of vaccine opinion distribution online, which we hope, can be helpful to explain vaccination rates for the ongoing COVID-19 pandemic.
Chen Qingqing, Crooks Andrew

2022-Jun

COVID-19 Pandemic, Sentiment Analysis, Social Media, Time & Space, United States, Vaccination

General

Non-invasive health prediction from visually observable features.

In F1000Research
Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person's health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.
Khong Fan Yi, Connie Tee, Goh Michael Kah Ong, Wong Li Pei, Teh Pin Shen, Choo Ai Ling

2021

Health prediction, Machine learning, Remote screening and diagnosis

General

C-COVIDNet: A CNN Model for COVID-19 Detection Using Image Processing.

In Arabian journal for science and engineering
COVID-19 has become a global disaster that has disturbed the socioeconomic fabric of the world. Efficient and cost-effective diagnosis methods are very much required for better treatment and eliminating false cases for COVID-19. COVID-19 disease is a type of respiratory syndrome, thus lung X-ray analysis has got the attention for an effective diagnosis. Hence, the proposed study introduces an Image processing based COVID-19 detection model C-COVIDNet, which is trained on a dataset of chest X-ray images belonging to three categories: COVID-19, Pneumonia, and Normal person. Image preprocessing pipeline is used for extracting the region of interest (ROI), so that the required features may be present in the input. This lightweight convolution neural network (CNN) based approach has achieved an accuracy of 97.5% and an F1-score of 97.91%. Model input images are generated in batches using a custom data generator. The performance of C-COVIDNet has outperformed the state-of-the-art. The promising results will surely help in accelerating the development of deep learning-based COVID-19 diagnosis tools using radiography.
Rajawat Neha, Hada Bharat Singh, Meghawat Mayank, Lalwani Soniya, Kumar Rajesh

2022-Apr-30

COVID-19 detection, Convolution neural network, Deep learning, Image processing

General

Cyber Risk Recommendation System for Digital Education Management Platforms.

In Computational intelligence and neuroscience
Covid-19 pandemic has ushered in a new school and academic year for students in a distance learning regime. This new daily routine was unprecedented and undoubtedly unusual, especially for the younger ones. At this point and at these ages, the risk of cyber fraud is even greater. The transition from the physical environment to the Internet took place quickly without the appropriate time to control potential risks and the proper information and training of teachers and students. Some common threats that need to be addressed to protect learners and their data when using e-learning methods are malicious remote access, malware, phishing, cyber fraud, etc. Considering the above situation, this work presents an innovative cyber risk recommendation system for digital education management platforms. The system in question is a distributed two-stage algorithm based on game theory and machine learning, which is trained by the constant change in the choice of recommendations by users to maximize security. We examine the algorithm's ability to simulate a user system in which everyone independently selects a user recommendation, assesses the environment and the implications of this choice, and then concludes whether it will continue to have that recommendation fixed. The methodology with which we have represented the digital e-learning system has been done with an approach that directly corresponds with their general view as a cyber-physical-social system. We consider the digital school as an environment that brings limitations, leading us to a pretty demanding personalization problem. Users coexist in this environment, in which everyone acts voluntarily but influences and is influenced by the surrounding environment. Our results lead us to conclude that this algorithm responds in a fully effective, flexible, and efficient way to the needs of protection and risk assessment of e-learning education systems.
Yin Xiufang, Chen Yanfang

2022

General

Face mask detection in COVID-19: a strategic review.

In Multimedia tools and applications
With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a cumbersome task. The exigency of monitoring people wearing face masks necessitated building an automatic system. Currently, distinct methods using machine learning and deep learning can be used effectively. In this paper, all the essential requirements for such a model have been reviewed. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources to multiple datasets are mentioned. The several software needed for the implementation are also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research.
Vibhuti Jindal, Neeru Singh, Harpreet Rana

2022-May-05

COVID-19, Classification, Face mask detection, Object detection

General

Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.

In BioMed research international ; h5-index 102.0
COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4⁺ cell, CD8⁺ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression.
Chen Lei, Mei Zi, Guo Wei, Ding ShiJian, Huang Tao, Cai Yu-Dong

2022

Public Health

Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.

In Artificial intelligence in medicine ; h5-index 34.0
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
Comito Carmela, Pizzuti Clara

2022-Jun

Artificial intelligence, COVID-19, Deep learning, Diagnosing, Forecasting, Machine learning

General

Enrichment analysis on regulatory subspaces: A novel direction for the superior description of cellular responses to SARS-CoV-2.

In Computers in biology and medicine

STATEMENT : Enrichment analysis of cell transcriptional responses to SARS-CoV-2 infection from biclustering solutions yields broader coverage and superior enrichment of GO terms and KEGG pathways against alternative state-of-the-art machine learning solutions, thus aiding knowledge extraction.

MOTIVATION AND METHODS : The comprehensive understanding of the impacts of SARS-CoV-2 virus on infected cells is still incomplete. This work aims at comparing the role of state-of-the-art machine learning approaches in the study of cell regulatory processes affected and induced by the SARS-CoV-2 virus using transcriptomic data from both infectable cell lines available in public databases and in vivo samples. In particular, we assess the relevance of clustering, biclustering and predictive modeling methods for functional enrichment. Statistical principles to handle scarcity of observations, high data dimensionality, and complex gene interactions are further discussed. In particular, and without loos of generalization ability, the proposed methods are applied to study the differential regulatory response of lung cell lines to SARS-CoV-2 (α-variant) against RSV, IAV (H1N1), and HPIV3 viruses.

RESULTS : Gathered results show that, although clustering and predictive algorithms aid classic stances to functional enrichment analysis, more recent pattern-based biclustering algorithms significantly improve the number and quality of enriched GO terms and KEGG pathways with controlled false positive risks. Additionally, a comparative analysis of these results is performed to identify potential pathophysiological characteristics of COVID-19. These are further compared to those identified by other authors for the same virus as well as related ones such as SARS-CoV-1. The findings are particularly relevant given the lack of other works utilizing more complex machine learning algorithms within this context.

Rodrigues Pedro, Costa Rafael S, Henriques Rui

2022-Apr-25

Biclustering, COVID-19, Computational biology, Discriminative regulatory patterns, Machine learning, SARS-CoV-2, Transcriptomics

Cardiology

ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects.

In Computers in biology and medicine

OBJECTIVE : Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19.

METHOD : We propose a shallow 1-D convolutional neural network (CNN) deep learning architecture, namely ECG-iCOVIDNet, to distinguish ECG data of post-COVID subjects and healthy subjects. Further, we employed ShAP technique to interpret ECG segments that are highlighted by the CNN model for the classification of ECG recordings into healthy and post-COVID subjects.

RESULTS : ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F₁-score of 100%.

CONCLUSION : So far, we have not come across any other study with an in-depth ECG signal analysis of the COVID-recovered subjects. In this study, it is shown that the shallow ECG-iCOVIDNet CNN model performed good for distinguishing ECG signals of COVID-recovered subjects from those of healthy subjects. In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model. Successful deployment of such systems can help the doctors identify the changes in the ECG of the post-COVID subjects on time that can save many lives.

Agrawal Amulya, Chauhan Aniket, Shetty Manu Kumar, P Girish M, Gupta Mohit D, Gupta Anubha

2022-Apr-30

AI in ECG, CNN, COVID, Electrocardiogram (ECG), Interpretability, Post-COVID, Shapley additive exPlanations (ShAP)

Public Health

Deep neural networks for simultaneously capturing public topics and sentiments during a pandemic. Application to a COVID-19 tweet dataset.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Introduction: Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been more and more applied to the analysis of text from social networks. However, most of developed approaches can only capture topics or sentiments alone, but not both together.

OBJECTIVE : Objective: Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments, and applied it to tweets sent just after the announcement of the SARS-CoV-2 pandemic by the WHO.

METHODS : Methods: A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80/20 into training/validation sets. We combined lexicons and convolutional neural networks for improving sentiment prediction. The trained model achieved an overall accuracy of 81 % and a precision of 82 %, and was able to capture simultaneously the weighted words associated to a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word-cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores.

RESULTS : Results: In reaction to the announcement of the pandemic by the WHO, six negative and five positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, the economic consequences, and the medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people.

CONCLUSIONS : Conclusions: We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.

CLINICALTRIAL :

Boukobza Adrien, Burgun Anita, Roudier Bertrand, Tsopra Rosy

2022-Apr-21

General

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images.

In Biomedical signal processing and control
Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first identified in the city of Wuhan, China somewhere in the early December 2019. It affects the human respiratory system by causing respiratory infections with symptoms (mild to severe) like fever, cough, and weakness but can further lead to other serious diseases and has resulted in millions of deaths until now. Therefore, an accurate diagnosis for such types of diseases is highly needful for the current healthcare system. In this paper, a state of the art deep learning method is described. We propose COVDC-Net, a Deep Convolutional Network-based classification method which is capable of identifying SARS-CoV-2 infected amongst healthy and/or pneumonia patients from their chest X-ray images. The proposed method uses two modified pre-trained models (on ImageNet) namely MobileNetV2 and VGG16 without their classifier layers and fuses the two models using the Confidence fusion method to achieve better classification accuracy on the two currently publicly available datasets. It is observed through exhaustive experiments that the proposed method achieved an overall classification accuracy of 96.48% for 3-class (COVID-19, Normal and Pneumonia) classification tasks. For 4-class classification (COVID-19, Normal, Pneumonia Viral, and Pneumonia Bacterial) COVDC-Net method delivered 90.22% accuracy. The experimental results demonstrate that the proposed COVDC-Net method has shown better overall classification accuracy as compared to the existing deep learning methods proposed for the same task in the current COVID-19 pandemic.
Sharma Anubhav, Singh Karamjeet, Koundal Deepika

2022-Aug

COVID-19, Chest X-ray, Confidence fusion, Deep learning, Transfer learning

General

Learning COVID-19 Pneumonia Lesion Segmentation from Imperfect Annotations via Divergence-Aware Selective Training.

In IEEE journal of biomedical and health informatics
The COVID-19 pandemic has spread the world like no other crisis in recent history. Automatic segmentation of COVID-19 pneumonia lesions is critical for quantitative measurement for diagnosis and treatment management. For this task, deep learning is the state-of-the-art method while requires a large set of accurately annotated images for training, which is difficult to obtain due to limited access to experts and the time-consuming annotation process. To address this problem, we aim to train the segmentation network from imperfect annotations, where the training set consists of a small clean set of accurately annotated images by experts and a large noisy set of inaccurate annotations by non-experts. To avoid the labels with different qualities corrupting the segmentation model, we propose a new approach to train segmentation networks to deal with noisy labels. We introduce a dual-branch network to separately learn from the accurate and noisy annotations. To fully exploit the imperfect annotations as well as suppressing the noise, we design a Divergence-Aware Selective Training (DAST) strategy, where a divergence-aware noisiness score is used to identify severely noisy annotations and slightly noisy annotations. For severely noisy samples we use an unsupervised regularization through dual-branch consistency between predictions from the two branches. We also refine slightly noisy samples and use them as supplementary data for the clean branch to avoid overfitting. Experimental results show that our method achieves a higher performance than standard training process for COVID-19 pneumonia lesion segmentation when learning from imperfect labels, and our framework outperforms the state-of-the-art noise-tolerate methods significantly with various clean label percentages.
Yang Shuojue, Wang Guotai, Sun Hui, Luo Xiangde, Sun Peng, Li Kang, Wang Qijun, Zhang Shaoting

2022-May-06

General

An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models.

In Environmental science and pollution research international
Coronavirus (COVID-19) is a highly contagious virus (SARS-CoV-2) that has caused a global pandemic since January 2020. Scientists around the world are doing extensive research to control this disease. They are working tirelessly to find out the origin and causes of the disease. Several studies and experiments mentioned that there are some meteorological parameters which are highly correlated with COVID-19 transmission. In this work, we studied the effects of 11 meteorological parameters on the transmission of COVID-19 in Bangladesh. We first applied statistical analysis and observed that there is no significant effect of these parameters. Therefore, we proposed a novel technique to analyze the insight effects of these parameters by using a combination of Random Forest, CART, and Lasso feature selection techniques. We observed that 4 parameters are highly influential for COVID-19 where [Formula: see text] and Cloud have positive association whereas WS and AQ have negative impact. Among them, Cloud has the highest positive impact which is 0.063 and WS has the highest negative association which is [Formula: see text]. Moreover, we have validated our performance using DLNM technique. The result of this investigation can be used to develop an alert system that will assist the policymakers to know the characteristics of COVID-19 against meteorological parameters and can impose different policies based on the weather conditions.
Karmokar Jaionto, Islam Mohammad Aminul, Uddin Machbah, Hassan Md Rakib, Yousuf Md Sayeed Iftekhar

2022-May-06

Bangladesh, CART, COVID-19, DLNM, Lasso, Meteorological parameters, Random Forest

Public Health

Asymptomatic Transmissibility Calls for Implementing a Zero-COVID Strategy to End the Current Global Crisis.

In Frontiers in cellular and infection microbiology ; h5-index 53.0
The coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented global challenges. A zero-COVID strategy is needed to end the crisis, but there is a lack of biological evidence. In the present study, we collected available data on SARS, MERS, and COVID-19 to perform a comprehensive comparative analysis and visualization. The study results revealed that the fatality rate of COVID-19 is low, whereas its death toll is high compared to SARS and MERS. Moreover, COVID-19 had a higher asymptomatic rate. In particular, COVID-19 exhibited unique asymptomatic transmissibility. Further, we developed a foolproof operating software in Python language to simulate COVID-19 spread in Wuhan, showing that the cumulative cases of existing asymptomatic spread would be over 100 times higher than that of only symptomatic spread. This confirmed the essential role of asymptomatic transmissibility in the uncontrolled global spread of COVID-19, which enables the necessity of implementing the zero-COVID policy. In conclusion, we revealed the triggering role of the asymptomatic transmissibility of COVID-19 in this unprecedented global crisis, which offers support to the zero-COVID strategy against the recurring COVID-19 spread.
Zhang Chaobao, Wang Hongzhi, Wen Zilu, Gu Mingjun, Liu Lianyong, Li Xiangqi

2022

COVID-19, SARS-CoV-2, asymptomatic spread, containment measure, software, artificial intelligence, zero-COVID strategy

General

Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.

In Frontiers in cellular and infection microbiology ; h5-index 53.0
The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people's lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.
Zhao Yu, Zhang Rusen, Zhong Yi, Wang Jingjing, Weng Zuquan, Luo Heng, Chen Cunrong

2022

attention mechanism, clinical indicators, machine learning, pneumonia, statistical analysis, the coronavirus disease 2019

Public Health

Quantifying depression-related language on social media during the COVID-19 pandemic.

In International journal of population data science

Introduction : The COVID-19 pandemic had clear impacts on mental health. Social media presents an opportunity for assessing mental health at the population level.

Objectives : 1) Identify and describe language used on social media that is associated with discourse about depression. 2) Describe the associations between identified language and COVID-19 incidence over time across several geographies.

Methods : We create a word embedding based on the posts in Reddit's /r/Depression and use this word embedding to train representations of active authors. We contrast these authors against a control group and extract keywords that capture differences between the two groups. We filter these keywords for face validity and to match character limits of an information retrieval system, Elasticsearch. We retrieve all geo-tagged posts on Twitter from April 2019 to June 2021 from Seattle, Sydney, Mumbai, and Toronto. The tweets are scored with BM25 using the keywords. We call this score rDD. We compare changes in average score over time with case counts from the pandemic's beginning through June 2021.

Results : We observe a pattern in rDD across all cities analyzed: There is an increase in rDD near the start of the pandemic which levels off over time. However, in Mumbai we also see an increase aligned with a second wave of cases.

Conclusions : Our results are concordant with other studies which indicate that the impact of the pandemic on mental health was highest initially and was followed by recovery, largely unchanged by subsequent waves. However, in the Mumbai data we observed a substantial rise in rDD with a large second wave. Our results indicate possible un-captured heterogeneity across geographies, and point to a need for a better understanding of this differential impact on mental health.

Davis Brent D, McKnight Dawn Estes, Teodorescu Daniela, Quan-Haase Anabel, Chunara Rumi, Fyshe Alona, Lizotte Daniel J

2020

COVID-19, Twitter, depression, information retrieval, machine learning, public health surveillance, social media

Public Health

Plasmonic nanosensors for point-of-care biomarker detection.

In Materials today. Bio
Advancement of materials along with their fascinating properties play increasingly important role in facilitating the rapid progress in medicine. An excellent example is the recent development of biosensors based on nanomaterials that induce surface plasmon effect for screening biomarkers of various diseases ranging from cancer to Covid-19. The recent global pandemic re-confirmed the trend of real-time diagnosis in public health to be in point-of-care (POC) settings that can screen interested biomarkers at home, or literally anywhere else, at any time. Plasmonic biosensors, thanks to its versatile designs and extraordinary sensitivities, can be scaled into small and portable devices for POC diagnostic tools. In the meantime, efforts are being made to speed up, simplify and lower the cost of the signal readout process including converting the conventional heavy laboratory instruments into lightweight handheld devices. This article reviews the recent progress on the design of plasmonic nanomaterial-based biosensors for biomarker detection with a perspective of POC applications. After briefly introducing the plasmonic detection working mechanisms and devices, the selected highlights in the field focusing on the technology's design including nanomaterials development, structure assembly, and target applications are presented and analyzed. In parallel, discussions on the sensor's current or potential applicability in POC diagnosis are provided. Finally, challenges and opportunities in plasmonic biosensor for biomarker detection, such as the current Covid-19 pandemic and its testing using plasmonic biosensor and incorporation of machine learning algorithms are discussed.
Jin Congran, Wu Ziqian, Molinski John H, Zhou Junhu, Ren Yundong, Zhang John X J

2022-Mar

Biomarker, Biosensor, Covid-19, Plasmonic, Point-of-care

General

Mobile perceived trust mediation on the intention and adoption of FinTech innovations using mobile technology: A systematic literature review.

In F1000Research
The banking and financial sectors have witnessed a significant development recently due to financial technology (FinTech), and it has become an essential part of the financial system. Many factors helped the development of this sector, including the pandemics such as Covid-19, the considerable increasing market value of the FinTech sector worldwide, and new technologies such as blockchain, artificial intelligence, big data, cloud computing and mobile technology. Moreover, changes in consumer's preferences, especially the Z-generation (digital generation). FinTech shifted the traditional business models to mobile platforms characterized by ease of access and swift transactions. Mobile technology became the main backbone for FinTech innovations and acts as a channel to deliver FinTech services that overcome all geographical and timing barriers, thus enhancing financial inclusion. Mobile perceived Trust (MPT), or the trust in using financial business models via mobile technology, is a crucial factor in the FinTech context that has mediation effects on the intention and adoption of different FinTech business models. Unfortunately, few studies have explored MPT mediations on consumers' intention to adopt FinTech innovations using mobile technology. Typically, many studies examined trust/MPT as an independent and unidirectional variable and investigated its effects on behaviour intention without predicting its mediation effects. This study aimed to develop a systematic literature review on MPT mediation in FinTech, focusing on the period from 2016 and 2021, in journals ranked Q1 and Q2, and known-based theories such as the technology acceptance model, the unified theory of acceptance and use of technology, and the mobile technology acceptance model. This study found that only four articles were published in Q1 and Q2 journals. In these articles, the MPT was used as a mediator, and its effects were measured on the intention and adoption of the behaviour.
Dawood Hatim M, Liew Chee Yoong, Lau Teck Chai

2021

Benefit-Risk framework., Fintech, Mobile Perceived Trust, Mobile technology acceptance model, Net Valence framework, Perceived Benefit, Perceived Risk

Internal Medicine

Validation of Machine Learning Models to Predict Adverse Outcomes in Patients with COVID-19: A Prospective Pilot Study.

In Yonsei medical journal

PURPOSE : We previously developed learning models for predicting the need for intensive care and oxygen among patients with coronavirus disease (COVID-19). Here, we aimed to prospectively validate the accuracy of these models.

MATERIALS AND METHODS : Probabilities of the need for intensive care [intensive care unit (ICU) score] and oxygen (oxygen score) were calculated from information provided by hospitalized COVID-19 patients (n=44) via a web-based application. The performance of baseline scores to predict 30-day outcomes was assessed.

RESULTS : Among 44 patients, 5 and 15 patients needed intensive care and oxygen, respectively. The area under the curve of ICU score and oxygen score to predict 30-day outcomes were 0.774 [95% confidence interval (CI): 0.614-0.934] and 0.728 (95% CI: 0.559-0.898), respectively. The ICU scores of patients needing intensive care increased daily by 0.71 points (95% CI: 0.20-1.22) after hospitalization and by 0.85 points (95% CI: 0.36-1.35) after symptom onset, which were significantly different from those in individuals not needing intensive care (p=0.002 and <0.001, respectively). Trends in daily oxygen scores overall were not markedly different; however, when the scores were evaluated within <7 days after symptom onset, the patients needing oxygen showed a higher daily increase in oxygen scores [1.81 (95% CI: 0.48-3.14) vs. -0.28 (95% CI: 1.00-0.43), p=0.007].

CONCLUSION : Our machine learning models showed good performance for predicting the outcomes of COVID-19 patients and could thus be useful for patient triage and monitoring.

Kim Hyung-Jun, Heo JoonNyung, Han Deokjae, Oh Hong Sang

2022-May

COVID-19, machine learning, prognosis, prospective studies, validation study

General

Using supervised learning to analyze the French vaccine debate on Twitter.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : As the pandemic progressed, disinformation, fake news and conspiracy spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media is particularly important, but the large amount of data exchanged on social networks requires specific methods. This is why machine learning and natural language processing (NLP) models are increasingly applied to social media data.

OBJECTIVE : The aim of this study is to examine the capability of the CamemBERT French language model to faithfully predict elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic or irrelevant to the studied topic.

METHODS : A total of 901,908 unique French tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using the Twitter API v2. Approximately 2,000 randomly selected tweets were labeled with two types of categorization: (1) arguments for ("pros") or against ("cons") vaccination (sanitary measures included) and (2) the type of content of tweets ("scientific", "political", "social", or "vaccination status"). The CamemBERT model was fine-tuned and tested for the classification of French tweets. The model performance was assessed by computing the F1-score, and confusion matrices were obtained.

RESULTS : The accuracy of the applied machine learning reached up to 70.6% for the first classification ("pros" and "cons" tweets) and up to 90.0% for the second classification ("scientific" and "political" tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio = 1.86; 1.20 < 95% confidence interval < 2.86).

CONCLUSIONS : The accuracy is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the models drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy of the model by selecting tweets using a new method based on tweet size.

CLINICALTRIAL :

Sauvayre Romy, Vernier Jessica, Chauvière Cédric

2022-May-04

Public Health

Examining the Implementation of Digital Health to Strengthen COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries.

In JMIR formative research
The COVID-19 pandemic has profoundly impacted the globe taking the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence-based platforms to tackle the transmission of the SARS-COV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies i.e. contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping can overwhelm the healthcare delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
Olusanya Olufunto A, White Brianna, Melton Chad A, Shaban-Nejad Arash

2022-Apr-21

General

An Objective Framework for Evaluating Unrecognized Bias in Medical AI Models Predicting COVID-19 Outcomes.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models.

MATERIALS AND METHODS : Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. Models were evaluated both retrospectively and prospectively using model-level metrics of discrimination, accuracy, and reliability, and a novel individual-level metric for error.

RESULTS : We found inconsistent instances of model-level bias in the prediction models. From an individual-level aspect, however, we found most all models performing with slightly higher error rates for older patients.

DISCUSSION : While a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations.

CONCLUSION : Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

Estiri Hossein, Strasser Zachary H, Rashidian Sina, Klann Jeffrey G, Wagholikar Kavishwar B, McCoy Thomas H, Murphy Shawn N

2022-May-02

Bias, COVID-19, Medical AI, Predictive Model, electronic health records

General

deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions.

In Briefings in bioinformatics
Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.
Kaundal Rakesh, Loaiza Cristian D, Duhan Naveen, Flann Nicholas

2022-Apr-30

computational modeling, convolutional neural networks (CNNs), deep learning, host–pathogen interactions, neural networks, prediction

Pathology

Genome sequencing and analysis of genomic diversity in the locally transmitted SARS-CoV-2 in Pakistan.

In Transboundary and emerging diseases ; h5-index 40.0
Surveillance of genetic diversity of the SARS-CoV-2 is extremely important to detect the emergence of more infectious and deadly strains of the virus. In this study, we evaluated mutational events in the SARS-CoV-2 genomes through whole genome sequencing. The samples were collected from COVID-19 patients in different major cities of Pakistan during the four waves of the pandemic (May 2020 to July 2021) and subjected to whole genome sequencing. Using in silico and machine learning tools, the viral mutational events were analyzed, and variants of concern and of interest were identified during each of the four waves. The overall mutation frequency (mutations per genome) increased during the course of the pandemic from 12.19, to 23.63, 31.03, and 41.22 in the first, second, third, and fourth waves, respectively. We determined the viral strains rose to higher frequencies in local transmission. The first wave had three most common strains B.1.36, B.1.160, and B.1.255, the second wave comprised of B.1.36, and B.1.247 strains, the third wave had B.1.1.7 (Alpha variant) and B.1.36 strains, and the fourth waves comprised of B.1.617.2 (Delta). Intriguingly, the B.1.36 variants were found in all the waves of the infection indicating their survival fitness. Through phylogenetic analysis, the probable routes of transmission of various strains in the country were determined. Collectively, our study provided an insight into the evolution of SARS-CoV-2 lineages in the spatio-temporal local transmission during different waves of the pandemic, which aided the state institutions in implementing adequate preventive measures. This article is protected by copyright. All rights reserved.
Shakeel Muhammad, Irfan Muhammad, Un Nisa Zaib, Farooq Saba, Ul Ain Noor, Iqbal Waseem, Kakar Niamatullah, Jahan Shah, Shahzad Mohsin, Siddiqi Saima, Khan Ishtiaq Ahmad

2022-May-05

COVID-19 pandemic, SARS-CoV-2 lineages, genetic evolution, spatio-temporal surveillance, viral variants

General

25 (S)-Hydroxycholesterol acts as a possible dual enzymatic inhibitor of SARS-CoV-2 Mpro and RdRp-: an insight from molecular docking and dynamics simulation approaches.

In Journal of biomolecular structure & dynamics
The coronavirus disease (COVID-19) pandemic has rapidly extended globally and killed approximately 5.83 million people all over the world. But, to date, no effective therapeutic against the disease has been developed. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and enters the host cell through the spike glycoprotein (S protein) of the virus. Subsequently, RNA-dependent RNA polymerase (RdRp) and main protease (M^pro) of the virus mediate viral transcription and replication. Mechanistically inhibition of these proteins can hinder the transcription as well as replication of the virus. Recently oxysterols and its derivative, such as 25 (S)-hydroxycholesterol (25-HC) has shown antiviral activity against SARS-CoV-2. But the exact mechanisms and their impact on RdRp and M^pro have not been explored yet. Therefore, the study aimed to identify the inhibitory activity of 25-HC against the viral enzymes RdRp and M^pro simultaneously. Initially, a molecular docking simulation was carried out to evaluate the binding activity of the compound against the two proteins. The pharmacokinetics (PK) and toxicity parameters were analyzed to observe the 'drug-likeness' properties of the compound. Additionally, molecular dynamics (MD) simulation was performed to confirm the binding stability of the compound to the targeted protein. Furthermore, molecular mechanics generalized Born surface area (MM-GBSA) was used to predict the binding free energies of the compound to the targeted protein. Molecular docking simulation identified low glide energy -51.0 kcal/mol and -35.0 kcal/mol score against the RdRp and M^pro, respectively, where MD simulation found good binding stability of the compound to the targeted proteins. In addition, the MM/GBSA approach identified a good value of binding free energies (ΔG bind) of the compound to the targeted proteins. Therefore, the study concludes that the compound 25-HC could be developed as a treatment and/or prevention option for SARS-CoV-2 disease-related complications. Although, experimental validation is suggested for further evaluation of the work.Communicated by Ramaswamy H. Sarma.
Alzahrani Faisal A, Alkarim Saleh A, Hawsawi Yousef M, Abdulaal Wesam H, Albiheyri Raed, Kurdi Bassem, Alguridi Hassan, El-Magd Mohammed A

2022-May-05

25-hydroxycholesterol, MD simulation, RdRp, SARS-CoV-2, main protease, molecular docking, spike glycoprotein

Radiology

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

In ERJ open research

Purpose : In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities.

Methods : The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60).

Results : The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items).

Conclusion : This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

Vaidyanathan Akshayaa, Guiot Julien, Zerka Fadila, Belmans Flore, Van Peufflik Ingrid, Deprez Louis, Danthine Denis, Canivet Gregory, Lambin Philippe, Walsh Sean, Occhipinti Mariaelena, Meunier Paul, Vos Wim, Lovinfosse Pierre, Leijenaar Ralph T H

2022-Apr

General

Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis.

In Medical image analysis
Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical diagnosis. The training of new sonographers and deep learning based algorithms for US image analysis usually requires a large amount of data. However, obtaining and labeling large-scale US imaging data are not easy tasks, especially for diseases with low incidence. Realistic US image synthesis can alleviate this problem to a great extent. In this paper, we propose a generative adversarial network (GAN) based image synthesis framework. Our main contributions include: (1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; (2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN. We superpose the edge sketch onto the object mask and use the composite mask as the network input; (3) to generate high-resolution US images, we adopt a progressive training strategy to gradually generate high-resolution images from low-resolution images. In addition, a feature loss is proposed to minimize the difference of high-level features between the generated and real images, which further improves the quality of generated images; (4) the proposed US image synthesis method is quite universal and can also be generalized to the US images of other anatomical structures besides the three ones tested in our study (lung, hip joint, and ovary); (5) extensive experiments on three large US image datasets are conducted to validate our method. Ablation studies, customized texture editing, user studies, and segmentation tests demonstrate promising results of our method in synthesizing realistic US images.
Liang Jiamin, Yang Xin, Huang Yuhao, Li Haoming, He Shuangchi, Hu Xindi, Chen Zejian, Xue Wufeng, Cheng Jun, Ni Dong

2022-Apr-26

COVID-19, Generative adversarial networks, Hip joint, Ovary and follicle, Ultrasound image synthesis

Surgery

Automated Interpretable Discovery of Heterogeneous Treatment Effectiveness: A COVID-19 Case Study.

In Journal of biomedical informatics ; h5-index 55.0
Testing multiple treatments for heterogeneous (varying) effectiveness with respect to many underlying risk factors requires many pairwise tests; we would like to instead automatically discover and visualize patient archetypes and predictors of treatment effectiveness using multitask machine learning. In this paper, we present a method to estimate these heterogeneous treatment effects with an interpretable hierarchical framework that uses additive models to visualize expected treatment benefits as a function of patient factors (identifying personalized treatment benefits) and concurrent treatments (identifying combinatorial treatment benefits). This method achieves state-of-the-art predictive power for COVID-19 in-hospital mortality and interpretable identification of heterogeneous treatment benefits. We first validate this method on the large public MIMIC-IV dataset of ICU patients to test recovery of heterogeneous treatment effects. Next we apply this method to a proprietary dataset of over 3000 patients hospitalized for COVID-19, and find evidence of heterogeneous treatment effectiveness predicted largely by indicators of inflammation and thrombosis risk: patients with few indicators of thrombosis risk benefit most from treatments against inflammation, while patients with few indicators of inflammation risk benefit most from treatments against thrombosis. This approach provides an automated methodology to discover heterogeneous and individualized effectiveness of treatments.
Lengerich Benjamin J, Nunnally Mark E, Aphinyanaphongs Yin, Ellington Caleb, Caruana Rich

2022-Apr-30

COVID-19, Heterogeneous Treatment Effects, Interpretable Machine Learning, Personalized Medicine

General

Drug repurposing for SARS-CoV-2: a high-throughput molecular docking, molecular dynamics, machine learning, and DFT study.

In Journal of materials science

** : A micro-molecule of dimension 125 nm has caused around 479 million human infections (80 M for the USA) and 6.1 million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years period. The only other events in recent history that caused comparative human life loss through direct usage (either by human or nature, respectively) of structure-property relations of 'nano-structures' (either human-made or nature, respectively) were nuclear bomb attacks during World War II and 1918 Flu Pandemic. This molecule is called SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for this and emerging diseases. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. But so far, there has been no success in finding fully effective therapeutic candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. We have selected a group of ligands from the ZINC database based upon previously partially successful candidates, i.e., Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir. We have used the following robust framework to screen the ligands; Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis. In total, we have analyzed 242,000(ligands)9(proteins) = 2.178 million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies evaluating potential inhibitor binding sites. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands per protein were analyzed in Step-II with a molecular dynamic modeling-based RMSD filter of less than 1Å. It finally suggested three ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) attacking different binding sites of the same protein(7BV2), which were further analyzed in Step-III to find the rationale behind comparatively higher ligand efficacy.

Supplementary Information : The online version contains supplementary material available at 10.1007/s10853-022-07195-8.

Kashyap Jatin, Datta Dibakar*

2022-Apr-27

General

COVID - 19 Vaccine Sentiment Analysis using Public Opinions on Twitter.

In Materials today. Proceedings
Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.
Chinnasamy P, Suresh V, Ramprathap K, Jency A Jebamani B, Srinivas Rao K, Shiva Kranthi M

2022-Apr-28

Hashtags on Covid Vaccines, Machine Learning Algorithms, Public Sentiments, Sentiment Analysis, Tweets

General

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

In iScience
MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 COVID-19 patients using the high throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the COVID-19 patients, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection.
Zeng Qiqi, Qi Xin, Ma Junpeng, Hu Fang, Wang Xiaorui, Qin Hongyu, Li Mengyang, Huang Shaoxin, Yang Yong, Li Yixin, Bai Han, Jiang Meng, Ren Doudou, Kang Ye, Zhao Yang, Chen Xiaobei, Ding Xi, Ye Di, Wang Yankui, Jiang Jianguo, Li Dong, Chen Xi, Hu Ke, Zhang Binghong, Shi Bingyin, Zhang Chengsheng

2022-Apr-27

Asymptomatic infection, COVID-19, SARS-CoV-2, machine learning, miRNA

General

Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning.

In Computers & electrical engineering : an international journal
In the wake of the COVID-19 outbreak, automated disease detection has become a crucial part of medical science given the infectious nature of the coronavirus. This research aims to introduce a deep ensemble framework of transfer learning models for early prediction of COVID-19 from the respective chest X-ray images of the patients. The dataset used in this research was taken from the Kaggle repository having two classes- COVID-19 Positive and COVID-19 Negative. The proposed model achieved high accuracy on the test sample with minimum false positive prediction. It can assist doctors and technicians with early detection of COVID-19 infection. The patient's health can further be monitored remotely with the help of connected devices with the Internet, which may be termed as the Internet of Medical Things (IoMT). The proposed IoMT-based solution for the automatic detection of COVID-19 can be a significant step toward fighting the pandemic.
Roy Pradeep Kumar, Kumar Abhinav

2022-Apr-28

Classification, Convolutional Neural Network, Deep Learning, Ensemble learning, IoMT, Transfer learning

General

Artificial Intelligence approaches to predict COVID-19 infection in Senegal.

In Procedia computer science
The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the data thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we design clinical predictive models that estimate, using artificial intelligence and data, which patients are susceptible to receive a COVID-19 disease. To evaluate the predictive performance of our models, accuracy, AUROC, and scores calculated. From 12,727 individuals, models were tested with basic information (sex, age) and the patient's type of case, which is the combination of their symptoms, their travel during the last 14 days, their contact with an infected person or their participation in a festival requiring a gathering. We used 5 machine learning algorithms (LR, SVM, k-NN, RF, XGBoost) and 1 deep learning algorithm (ANN). Our models were validated with train-test split approach. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 73% and AUC of 69%. It is observed that predictive models trained on patients' basic information and type of case could be used to predict COVID-19 infection in Senegal and can be helpful for medical experts to optimize the resources efficiently.
Diallo Abdoulaye, Camara Gaoussou, Camara Fodé, Mboup Aminata

2022

Artificial Intelligence, Coronavirus, Deep Learning, Machine Learning, SARS-CoV2

General

Evaluation of E-learning Experience in the Light of the Covid-19 in Higher Education.

In Procedia computer science
Covid-19 has been stated as a worldwide outbreak of pandemic disease and crisis. The Covid-19 pandemic has dramatically affected the teaching and learning experience at universities and schools. In response, governments and higher education institutions around the world put significant efforts to ensure that students continue to obtain the best possible level of education and learning outcomes. As such effective evaluation of e-learning is essential in order to ensure that students get proper learning and education especially during the current circumstances of Covid-19. Our study was carried out to determine the main elements and factors related to students' satisfaction and quality of e-learning during the Covid-19 pandemic era based on various aspects and dimensions of e-learning. The main findings of the study indicated that students satisfaction and evaluation of the e-learning experience during the pandemic were not promising. Therefore, higher education institutions should reconsider their efforts and approaches to improve the quality of e-learning and the learning outcomes achieved. For example, IT infrastructure, Internet access, and particularly network connectivity could be improved to support fully online courses. Such elements need to be addressed because of the prevalence of the current Covid-19 pandemic which perhaps will lead to e-learning occurring for a long time. With the move to e-learning, the size of the class (the number of students in each class) has been increased leading to other significant challenges related to communication and participation in the class and reducing the possible interactivity for each student. Furthermore, it has been also observed that new students need relevant training on IT and e-learning applications to ensure sufficient use and utilization of these applications in their e-learning journey.
Al-Smadi Ahmad Mohmmad, Abugabah Ahed, Smadi Ahmad Al

2022

Covid-19, E-learning, Education, Pandemic, Students’ attitudes

General

Using Machine Learning to Analyze the Impact of Coronavirus Pandemic News on the Stock Markets in GCC Countries.

In Research in international business and finance
COVID-19 has resulted in high volatility in financial markets across the world. The goal of this study is to investigate the impact of COVID-19-related news on the stock markets in Gulf Cooperation Council (GCC) countries. The study utilizes machine learning approaches to assess the role of COVID-19 news in stock return predictability in these markets. The results reveal that the stock markets in the United Arab Emirates (UAE), Qatar, Saudi Arabia, and Oman were impacted by coronavirus-related news; however, this news had no impact on the stocks in Bahrain. Moreover, the results indicate that the impacted markets were influenced differently in terms of the quantities and types of news.
Al-Maadid Alanoud, Alhazbi Saleh, Al-Thelaya Khaled

2022-Apr-28

COVID-19, GCC, STOCK MARKETS, machine-learning

General

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

bioRxiv Preprint
Strong evidence suggests that human RNA-binding proteins (RBPs) are critical factors for viral infection, yet there is no feasible experimental approach to map exact binding sites of RBPs across the SARS-CoV-2 genome systematically at a large scale. We investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP / viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments. We evaluated conservation of RBP binding between 6 other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of variants from 11 viral strains on protein-RNA interaction, identifying a set of gain-and loss of binding events. Lastly, we linked RBPs to functional data and OMICs from other studies, and identified MBNL1, FTO and FXR2 as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and are available through a comprehensive online resource (https://sc2rbpmap.helmholtz-muenchen.de).
Horlacher, M.; Oleshko, S.; Hu, Y.; Cantini, G.; Schinke, P.; Ghanbari, M.; Vergara, E. E.; Bittner, F.; Mueller, N.; Ohler, U.; Moyon, L.; Marsico, A.

2022-05-04

General

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

bioRxiv Preprint
Viruses hijack the host cell's machinery for the purpose of viral replication and interfere with the activity of master regulatory proteins - including RNA binding proteins (RBPs). These RBPs are major actors in several steps of RNA processing, able to recognize and bind to their target RNAs by means of sequence or structure motifs. While host RBPs are known to represent critical factors for RNA viral replication, stability, and escape of host immune responses, their role in the context of SARS-CoV-2 infection remains poorly understood. Few experimental studies have mapped the SARS-CoV-2 RNA-protein interactome in infected human cells, but they are limited in the resolution and exhaustivity of their output. In contrast, computational approaches enable rapid screening of a large number of human RBPs for putative interactions with the viral RNA and are thus crucial to prioritize candidates for further experimental investigation. Here, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing a first single-nucleotide in silico map of human RBP / viral RNA interactions. To this end, we trained pysster and DeepRiPe, two deep learning methods based on convolutional neural networks, to learn the sequence preferences of >100 RBPs from eCLIP and PAR-CLIP data generated on human cell lines. We then applied our models cross-species to predict the propensity of each host RBP to bind to the SARS-CoV-2 RNA genome at single-base resolution. We further evaluated conservation of RBP binding between 6 other human pathogenic coronaviruses and identified sites of conserved and differential binding in the untranslated regions of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of sequence variants from 11 viral strains on protein-RNA interaction, including alpha, delta and omicron strains, and identified a set of gain-and loss of binding events. Further, we performed a systematic in silico mutagenesis to screen the SARS-CoV-2 genome for hypothetical high impact variants, which provides a resource to anticipate the regulatory impact of variants on novel strains. Lastly, we explore the clinical impact of the identified RBPs by linking them to other functional data and OMICs on COVID-19 patients from other studies. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways by providing insights into new players of host-virus interactions and provide a rich resource that enables the discovery of new antiviral targets and therapeutics. To facilitate the use of our results in future studies, we integrated the protein-RNA interaction map and variant impact predictions into an online resource (https://sc2rbpmap.helmholtz-muenchen.de). By providing the community with pre-trained RBP models we enable host-viral RNA interaction prediction for any (RNA) virus beyond SARS-CoV-2 and provide a tool to efficiently monitor new viral strains.
Horlacher, M.; Oleshko, S.; Hu, Y.; Cantini, G.; Schinke, P.; Ghanbari, M.; Vergara, E. E.; Bittner, F.; Mueller, N.; Ohler, U.; Moyon, L.; Marsico, A.

2022-05-02

Internal Medicine

A Multi-task Gaussian Process Self-attention Neural Network for Real-time Prediction of the Need for Mechanical Ventilators in COVID-19 Patients.

In Journal of biomedical informatics ; h5-index 55.0

OBJECTIVE : The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality.

METHODS : We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task Gaussian Process to handle the irregular sampling rate in observational data together with a self-attention neural network for the prediction task.

RESULTS : We evaluate our model on a large database with 9,532 nationwide in-hospital patients with COVID-19. The model demonstrates significant robustness and consistency improvements compared to conventional machine learning models. The proposed prediction model also shows performance improvements in terms of area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC) compared to various deep learning models, especially at early times after a patient's hospital admission.

CONCLUSION : The availability of large and real-time clinical data calls for new methods to make the best use of them for real-time patient risk prediction. It is not ideal for simplifying the data for traditional methods or for making unrealistic assumptions that deviate from observation's true dynamics. We demonstrate a pilot effort to harmonize cross-sectional and longitudinal information for mechanical ventilation needing prediction.

Zhang Kai, Karanth Siddharth, Patel Bela, Murphy Robert, Jiang Xiaoqian

2022-Apr-27

Deep neural network, Gaussian process, Mechanical ventilation prediction

General

Diagnosis of COVID-19 via Acoustic Analysis and Artificial Intelligence by Monitoring Breath Sounds on Smartphones.

In Journal of biomedical informatics ; h5-index 55.0
Scientific evidence shows that acoustic analysis could be an indicator for diagnosing COVID-19. From analyzing recorded breath sounds on smartphones, it is discovered that patients with COVID-19 have different patterns in both the time domain and frequency domain. These patterns are used in this paper to diagnose the infection of COVID-19. Statistics of the sound signals, analysis in the frequency domain, and Mel-Frequency Cepstral Coefficients (MFCCs) are then calculated and applied in two classifiers, k-Nearest Neighbors (kNN) and Convolutional Neural Network (CNN), to diagnose whether a user is contracted with COVID-19 or not. Test results show that, amazingly, an accuracy of over 97% could be achieved with a CNN classifier and more than 85% on kNN with optimized features. Optimization methods for selecting the best features and using various metrics to evaluate the performance are also demonstrated in this paper. Owing to the high accuracy of the CNN model, the CNN model was implemented in an Android app to diagnose COVID-19 with a probability to indicate the confidence level. The initial medical test shows a similar test result between the method proposed in this paper and the lateral flow method, which indicates that the proposed method is feasible and effective. Because of the use of breath sound and tested on the smartphone, this method could be used by everybody regardless of the availability of other medical resources, which could be a powerful tool for society to diagnose COVID-19.
Chen Zhiang, Li Muyun, Wang Ruoyu, Sun Wenzhuo, Liu Jiayi, Li Haiyang, Wang Tianxin, Lian Yuan, Zhang Jiaqian, Wang Xinheng

2022-Apr-27

Acoustic analysis, Breath sound, COVID-19, Convolutional Neural Network (CNN), k-Nearest Neighbors (kNN)

Public Health

First-onset major depression during the COVID-19 pandemic: A predictive machine learning model.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples.

METHODS : An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution.

RESULTS : There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent.

LIMITATIONS : Small sample size; self-report assessment; data covering 2020 only.

CONCLUSIONS : Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

Caldirola Daniela, Daccò Silvia, Cuniberti Francesco, Grassi Massimiliano, Alciati Alessandra, Torti Tatiana, Perna Giampaolo

2022-Apr-27

COVID-19, Depression, First-onset, General population, Machine learning, Predictive model

General

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

In Computers in biology and medicine

BACKGROUND : As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic. Therefore, we intended to develop a computer-aided diagnosis system for the diagnosis of suspected cases.

METHODS : To address the shortcomings of commonly used pre-training methods and exploit the information in unlabeled images, we proposed a new pre-training method based on transfer learning with self-supervised learning (TS). After that, a new convolutional neural network based on attention mechanism and deep residual network (RANet) was proposed to extract features. Based on this, a hybrid ensemble model (TSRNet) was proposed for classifying lung CT images of suspected patients as COVID-19 and normal.

RESULTS : Compared with the existing five models in terms of accuracy (DarkCOVIDNet: 98.08%; Deep-COVID: 97.58%; NAGNN: 97.86%; COVID-ResNet: 97.78%; Patch-based CNN: 88.90%), TSRNet has the highest accuracy of 99.80%. In addition, the recall, f1-score, and AUC of the model reached 99.59%, 99.78%, and 1, respectively.

CONCLUSION : TSRNet can effectively diagnose suspected COVID-19 cases with the help of the information in unlabeled and labeled images, thus helping physicians to adopt early treatment plans for confirmed cases.

Sun Junding, Pi Pengpeng, Tang Chaosheng, Wang Shui-Hua, Zhang Yu-Dong

2022-Apr-16

Attention, COVID-19, CT, Deep learning, Ensemble, Machine learning, Self-supervised learning, Transfer learning

Public Health

Clinical and Laboratory Profiles of SARS-CoV-2 Delta Variant Compared to Pre-Delta Variants.

In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

BACKGROUND : The emergence of SARS-CoV-2 variants of concern has led to significant phenotypical changes in transmissibility, virulence, and public health measures. Our study used clinical data to compare characteristics between a Delta variant wave and a pre-Delta variant wave of hospitalized patients.

METHODS : This single-center retrospective study defined a wave as an increasing number of COVID-19 hospitalizations, which peaked and later decreased. Data from the United States Department of Health and Human Services was used to identify the waves' primary variant. Wave 1 (08/08/20-04/01/21) was characterized by heterogeneous variants, while Wave 2 (06/26/21-10/18/21) was predominantly Delta variant. Descriptive statistics, regression techniques, and machine learning approaches supported the comparisons between waves.

RESULTS : From the cohort(n=1318), Wave 2 patients(n=665) were more likely to be younger, have fewer comorbidities, require more ICU care, and show an inflammatory profile with higher C-reactive protein, lactate dehydrogenase, ferritin, fibrinogen, prothrombin time, activated thromboplastin time, and INR compared to Wave 1. The gradient boosting model showed an area under the ROC curve of 0.854(sensitivity 86.4%;specificity 61.5%;positive predictive value 73.8%; negative predictive value 78.3%).

CONCLUSIONS : Clinical and laboratory characteristics can be used to estimate the COVID-19 variant regardless of genomic testing availability. This finding has implications for variant-driven treatment protocols and further research.

Bhakta Shivang, Sanghavi Devang K, Johnson Patrick W, Kunze Katie L, Neville Matthew R, Wadei Hani M, Bosch Wendelyn, Carter Rickey E, Shah Sadia Z, Pollock Benjamin D, Oman Sven P, Speicher Leigh, Siegel Jason, Libertin Claudia R, Matson Mark W, Franco Pablo Moreno, Cowart Jennifer B

2022-Apr-26

COVID-19, delta variant, genomics, machine learning, variants of concern

Internal Medicine

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.

In Computational biology and chemistry
Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.
Ulgen Ayse, Cetin Sirin, Cetin Meryem, Sivgin Hakan, Li Wentian

2022-Apr-09

COVID-19, Competing risks, Composite ranking, Survival analysis

General

EXPLORING SENTIMENT AND CARE MANAGEMENT OF HOSPITALIZED PATIENTS DURING FIRST WAVE OF COVID-19 PANDEMIC USING ELECTRONIC NURSING HEALTH RECORDS: DESCRIPTIVE STUDY.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The COVID-19 pandemic has changed the usual work in many hospitalization units (or wards). Few studies use electronic nursing clinical notes (ENCN) and their unstructured text to identify alterations in patients' feelings and therapeutic procedures of interest.

OBJECTIVE : Analysis of positive/negative sentiments through inspection of the free text of the ENCN; comparison of sentiments of ENCN with/without hospitalized COVID-19 patients; temporal analysis of the sentiments of the patients during the start of the first wave of the COVID-19 pandemic; and identification of the topics in ENCN.

METHODS : This is a descriptive study with analysis of the text content of ENCN. All ENCNs between January and June 2020 at Guadarrama Hospital (Madrid, Spain) extracted from the CGM Selene Electronic Health Records System were included. Two groups of ENCNs were analyzed: one from hospitalized patients in post intensive care units COVID-19, and a second group from hospitalized patients with non COVID-19. A sentiment analysis was performed on the lemmatized text, using the dictionaries NRC, Affin and Bing. A polarity analysis of the sentences was performed using the Bing dictionary, the SO Dictionaries V1.11Spa dictionary as amplifiers and decrementators. Machine learning techniques were applied in order to evaluate the presence of significant differences in the ENCN in groups of COVID-19 or non COVID-19 patients. Finally, a structural analysis of thematic models was performed to study the abstract topics that occur in the ENCN, using Latent Dirichlet Allocation topic modeling.

RESULTS : A total of 37,564 electronic health records were analyzed. Sentiment analysis in ENCN showed that patients with subacute COVID-19 have a higher proportion of positive sentiments compared to non COVID-19. Also, there are significant differences in polarity between both groups (Z=5.532, P<.001) with a polarity in COVID-19 patients of 0.108±0.299 versus a polarity in non COVID-19 patients of 0.09±0.301. Machine learning modeling reported that despite all models presenting high values, it is the neural network that presents the best indicators, over 0.8, and with significant P values between both groups. From Structural Topic Modeling analysis, the final model containing 10 topics was selected. It is noted a high correlation between topics 2, 5 and 8 (pressure ulcer and pharmacotherapy treatment), topics 1, 4, 7 and 9 (incidences related to fever and well-being state, and baseline oxygen saturation) and topics 3, 10 (blood glucose level and pain).

CONCLUSIONS : The ENCN may help in the development and implementation of more effective programs which allows to the COVID-19 pandemic patients a faster come back to a pre-pandemic way of life. Topic modeling could help identify specific clinical problems in patients and better target the care they receive.

CLINICALTRIAL :

Cuenca-Zaldívar Juan Nicolás, Torrente-Regidor Maria, Martín-Losada Laura, Fernández-DE-Las-Peñas César, Florencio Lidiane Lima, Sousa Pedro Alexandre, Palacios-Ceña Domingo

2022-Apr-21

Public Health

COVID-19 Vaccination and Public Health Countermeasures on Variants of Concern in Canada: Evidence from a Spatial Hierarchical Cluster Analysis.

In JMIR public health and surveillance

BACKGROUND : There is mounting evidence that the third wave of coronavirus disease 2019 (COVID-19) incidence is declining, yet variants of concern (VOCs) continue to present public health challenges in Canada. The emergence of VOCs has sparked debate on how to effectively control their impacts on the Canadian population.

OBJECTIVE : Provincial and territorial governments have implemented a wide range of policy measures to protect residents against community transmission of COVID-19, but research examining the specific impact of policy countermeasures on the VOCs in Canada is needed. Our study objective was to identify provinces with disproportionate prevalence of VOCs relative to COVID-19 mitigation efforts in provinces and territories in Canada.

METHODS : We analyzed publicly available provincial and territorial level data on the prevalence of VOCs in relation to mitigating factors (summarized in three measures: 1. strength of public health countermeasures: stringency index, 2. the extent to which people moved about outside their homes: mobility index, and 3. the proportion of the provincial/ territorial population that was fully vaccinated: vaccine uptake. Using spatial agglomerative hierarchical cluster analysis (unsupervised machine learning), provinces and territories were grouped into clusters by stringency index, mobility index and full vaccine uptake. The Kruskal-Wallis test was used to compare the prevalence of VOC (Alpha, or B.1.1.7, Beta, or B.1.351, Gamma, or P.1, and Delta, or B.1.617.2 variants) across the clusters.

RESULTS : Three clusters of vaccine uptake and countermeasures were identified. Cluster 1 consisted of the three Canadian territories and was characterized by a higher degree of vaccine deployment and fewer countermeasures. Cluster 2 (located in Central Canada and the Atlantic region) was typified by lower levels of vaccine deployment and moderate countermeasures. The third cluster which consisted of provinces in the Pacific region, Central Canada, and the Prairies, exhibited moderate vaccine deployment but stronger countermeasures. The overall and variant-specific prevalences were significantly different across the clusters.

CONCLUSIONS : This 'up to the point' analysis found that implementation of COVID-19 public health measures, including the mass vaccination of populations, is key to controlling VOC prevalence rates in Canada. As of June 15, 2021, the third wave of COVID-19 in Canada is declining and those provinces and territories that had implemented more comprehensive public health stringency measures showed lower VOC prevalence. Public health authorities and governments need to continue to communicate the importance of socio-behavioural preventive measures, even as populations in Canada continue to receive their primary and booster doses of vaccines.

CLINICALTRIAL : Non applicable.

Adeyinka Daniel, Neudorf Cory, Camillo Cheryl A, Marks Wendie, Muhajarine Nazeem

2022-Apr-26

General

Digital Technologies to Support Better Outcome and Experience of Care in Patients with Heart Failure.

In Current heart failure reports

PURPOSE OF REVIEW : In this article, we review a range of digital technologies for possible application in heart failure patients, with a focus on lessons learned. We also discuss a future model of heart failure management, as digital technologies continue to become part of standard care.

RECENT FINDINGS : Digital technologies are increasingly used by healthcare professionals and those living with heart failure to support more personalised and timely shared decision-making, earlier identification of problems, and an improved experience of care. The COVID-19 pandemic has accelerated the acceptability and implementation of a range of digital technologies, including remote monitoring and health tracking, mobile health (wearable technology and smartphone-based applications), and the use of machine learning to augment data interpretation and decision-making. Much has been learned over recent decades on the challenges and opportunities of technology development, including how best to evaluate the impact of digital health interventions on health and healthcare, the human factors involved in implementation and how best to integrate dataflows into the clinical pathway. Supporting patients with heart failure as well as healthcare professionals (both with a broad range of health and digital literacy skills) is crucial to success. Access to digital technologies and the internet remains a challenge for some patients. The aim should be to identify the right technology for the right patient at the right time, in a process of co-design and co-implementation with patients.

McBeath K C C, Angermann C E, Cowie M R

2022-Apr-29

Digital health, Digital technology, Heart failure, Person-centred care, Shared decision-making

General

Ultrahigh-resolution PM2.5 estimation from top-of-atmosphere reflectance with machine learning: Theories, methods, and applications.

In Environmental pollution (Barking, Essex : 1987)
Intra-urban pollution monitoring requires fine particulate (PM_2.5) concentration mapping at ultrahigh-resolution (dozens to hundreds of meters). However, current PM_2.5 concentration estimation, which is mainly based on aerosol optical depth (AOD) and meteorological data, usually had a low spatial resolution (kilometers) and severe spatial missing problem, cannot be applied to intra-urban pollution monitoring. To solve these problems, top-of-atmosphere reflectance (TOAR), which contains both the information about land and atmosphere and has high resolution and large spatial coverage, may be efficiently used for PM_2.5 estimation. This study aims to systematically evaluate the feasibility of retrieving ultrahigh-resolution PM_2.5 concentration at a large scale (national level) from TOAR. Firstly, we make a detailed discussion about several important but unsolved theoretic problems on TOAR-based PM_2.5 retrieval, including the band selection, scale effect, cloud impact, and mapping quality evaluation. Secondly, four types and eight retrieval models are compared in terms of quantitative accuracy, mapping quality, model generalization, and model efficiency, with the pros and cons of each type summarized. Deep neural network (DNN) model shows the highest retrieval accuracy, and linear models were the best in efficiency and generalization. As a compromise, ensemble learning shows the best overall performance. Thirdly, using the highly accurate DNN model (cross-validated R² equals 0.93) and through combining Landsat 8 and Sentinel 2 images, a 90 m and ∼4-day resolution PM_2.5 product was generated. The retrieved maps were used for analyzing the fine-scale interannual pollution change inner the city and the pollution variations during novel coronavirus disease 2019 (COVID-19). Results of this study proves that ultrahigh resolution can bring new findings of intra-urban pollution change, which cannot be observed at previous coarse resolution. Lastly, some suggestions for future ultrahigh-resolution PM_2.5 mapping research were given.
Yang Qianqian, Yuan Qiangqiang, Li Tongwen

2022-Apr-25

Deep learning, Intra-urban PM(2.5) pollution, Large scale, Top-of-atmosphere reflectance, Ultrahigh resolution

Radiology

External COVID-19 Deep Learning Model Validation on ACR AI-LAB: It's a Brave New World.

In Journal of the American College of Radiology : JACR

PURPOSE : Deploying external artificial intelligence (AI) models locally can be logistically challenging. We aimed to use the ACR AI-LAB software platform for local testing of a chest radiograph (CXR) algorithm for COVID-19 lung disease severity assessment.

METHODS : An externally developed deep learning model for COVID-19 radiographic lung disease severity assessment was loaded into the AI-LAB platform at an independent academic medical center, which was separate from the institution in which the model was trained. The data set consisted of CXR images from 141 patients with reverse transcription-polymerase chain reaction-confirmed COVID-19, which were routed to AI-LAB for model inference. The model calculated a Pulmonary X-ray Severity (PXS) score for each image. This score was correlated with the average of a radiologist-based assessment of severity, the modified Radiographic Assessment of Lung Edema score, independently interpreted by three radiologists. The associations between the PXS score and patient admission and intubation or death were assessed.

RESULTS : The PXS score deployed in AI-LAB correlated with the radiologist-determined modified Radiographic Assessment of Lung Edema score (r = 0.80). PXS score was significantly higher in patients who were admitted (4.0 versus 1.3, P < .001) or intubated or died within 3 days (5.5 versus 3.3, P = .001).

CONCLUSIONS : AI-LAB was successfully used to test an external COVID-19 CXR AI algorithm on local data with relative ease, showing generalizability of the PXS score model. For AI models to scale and be clinically useful, software tools that facilitate the local testing process, like the freely available AI-LAB, will be important to cross the AI implementation gap in health care systems.

Ardestani Ali, Li Matthew D, Chea Pauley, Wortman Jeremy R, Medina Adam, Kalpathy-Cramer Jayashree, Wald Christoph

2022-Apr-08

ACR AI-LAB, AI, COVID-19, chest radiograph, local testing

General

Explainability of radiomics through formal methods.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems.

METHODS : Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences.

RESULTS : In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach.

CONCLUSIONS : In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.

Varriano Giulia, Guerriero Pasquale, Santone Antonella, Mercaldo Francesco, Brunese Luca

2022-Apr-19

Artificial intelligence, COVID-19, Explainability, Formal methods, Model checking, Radiomics

General

A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

In Computers in biology and medicine
Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.
Singh Prateek, Ujjainiya Rajat, Prakash Satyartha, Naushin Salwa, Sardana Viren, Bhatheja Nitin, Singh Ajay Pratap, Barman Joydeb, Kumar Kartik, Gayali Saurabh, Khan Raju, Rawat Birendra Singh, Tallapaka Karthik Bharadwaj, Anumalla Mahesh, Lahiri Amit, Kar Susanta, Bhosale Vivek, Srivastava Mrigank, Mugale Madhav Nilakanth, Pandey C P, Khan Shaziya, Katiyar Shivani, Raj Desh, Ishteyaque Sharmeen, Khanka Sonu, Rani Ankita, Promila Sharma, Jyotsna Seth, Anuradha Dutta, Mukul Saurabh, Nishant Veerapandian, Murugan Venkatachalam, Ganesh Bansal, Deepak Gupta, Dinesh Halami, Prakash M Peddha, Muthukumar Serva Veeranna, Ravindra P Pal, Anirban Singh, Ranvijay Kumar Anandasadagopan, Suresh Kumar Karuppanan, Parimala Rahman, Syed Nasar Selvakumar, Gopika Venkatesan, Subramanian Karmakar, Malay Kumar Sardana, Harish Kumar Kothari, Anamika Parihar, Devendra Singh Thakur, Anupma Saiﬁ, Anas Gupta, Naman Singh, Yogita Reddu, Ritu Gautam, Rizul Mishra, Anuj Mishra, Avinash Gogeri, Iranna Rayasam, Geethavani Padwad, Yogendra Patial, Vikram Hallan, Vipin Singh, Damanpreet Tirpude, Narendra Chakrabarti, Partha Maity, Sujay Krishna Ganguly, Dipyaman Sistla, Ramakrishna Balthu, Narender Kumar A, Kiran Kumar Ranjith, Siva Kumar, B Vijay Jamwal, Piyush Singh Wali, Anshu Ahmed, Sajad Chouhan, Rekha Gandhi, Sumit G Sharma, Nancy Rai, Garima Irshad, Faisal Jamwal, Vijay Lakshmi Paddar, Masroor Ahmad Khan, Sameer Ullah Malik, Fayaz Ghosh, Debashish Thakkar, Ghanshyam Barik, S K Tripathi, Prabhanshu Satija, Yatendra Kumar Mohanty, Sneha Khan, Md Tauseef Subudhi, Umakanta Sen, Pradip Kumar, Rashmi Bhardwaj, Anshu Gupta, Pawan Sharma, Deepak Tuli, Amit Ray Chaudhuri, Saumya Krishnamurthi, Srinivasan Prakash, L Rao, Ch V Singh, B N Chaurasiya, Arvindkumar Chaurasiyar, Meera Bhadange, Mayuri Likhitkar, Bhagyashree Mohite, Sharada Patil, Yogita Kulkarni, Mahesh Joshi, Rakesh Pandya, Vaibhav Mahajan, Sachin Patil, Amita Samson, Rachel Vare, Tejas Dharne, Mahesh Giri, Ashok Mahajan, Sachin Paranjape, Shilpa Sastry, G Narahari Kalita, Jatin Phukan, Tridip Manna, Prasenjit Romi, Wahengbam Bharali, Pankaj Ozah, Dibyajyoti Sahu, Ravi Kumar Dutta, Prachurjya Singh, Moirangthem Goutam Gogoi, Gayatri Tapadar, Yasmin Begam Babu, Elapavalooru Vssk Sukumaran, Rajeev K Nair, Aishwarya R Puthiyamadam, Anoop Valappil, Prajeesh Kooloth Pillai Prasannakumari, Adrash Velayudhan Chodankar, Kalpana Damare, Samir Agrawal, Ved Varun Chaudhary, Kumardeep Agrawal, Anurag Sengupta, Shantanu Dash

2022-Apr-25

BBV152, COVID-19, Covaxin, Ensemble methods, Infection, Machine learning, SARS-CoV-2

General

An investigation of traffic density changes inside Wuhan during the COVID-19 epidemic with GF-2 time-series images.

In International journal of applied earth observation and geoinformation : ITC journal
In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow. Therefore, in this paper, we collected time-series high-resolution remote sensing images with the resolution of 1 m acquired before, during and after Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and counted for the statistics of traffic density to reflect the changes of human transmissions in the whole period of Wuhan lockdown. Open Street Map was used to obtain observation road surfaces, and a vehicle detection method combing morphology filter and deep learning was utilized to extract vehicles with the accuracy of 62.56%. According to the experimental results, the traffic density of Wuhan dropped with the percentage higher than 80%, and even higher than 90% on main roads during city lockdown; after lockdown lift, the traffic density recovered to the normal rate. Traffic density distributions also show the obvious reduction and increase throughout the whole study area. The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.
Wu Chen, Guo Yinong, Guo Haonan, Yuan Jingwen, Ru Lixiang, Chen Hongruixuan, Du Bo, Zhang Liangpei

2021-Dec-01

COVID-19, GF-2, Remote sensing, Time-series images, Traffic density changes, Wuhan lockdown

General

Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients.

In Journal of healthcare engineering
SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.
Mashraqi Aisha, Halawani Hanan, Alelyani Turki, Mashraqi Mutaib, Makkawi Mohammed, Alasmari Sultan, Shaikh Asadullah, Alshehri Ahmad

2022

General

Selecting a stable solid form of remdesivir using microcrystal electron diffraction and crystal structure prediction.

In RSC advances
Therapeutic options in response to the coronavirus disease 2019 (COVID-19) outbreak are urgently needed. In this communication, we demonstrate how to support selection of a stable solid form of an antiviral drug remdesivir in quick time using the microcrystal electron diffraction (MicroED) technique and a cloud-based and artificial intelligence implemented crystal structure prediction platform. We present the MicroED structures of remdesivir forms II and IV and conclude that form II is more stable than form IV at ambient temperature in agreement with experimental observations. The combined experimental and theoretical study can serve as a template for formulation scientists in the pharmaceutical industry.
Sekharan Sivakumar, Liu Xuetao, Yang Zhuocen, Liu Xiang, Deng Li, Ruan Shigang, Abramov Yuriy, Sun GuangXu, Li Sizhu, Zhou Tian, Shi Baime, Zeng Qun, Zeng Qiao, Chang Chao, Jin Yingdi, Shi Xuekun

2021-May-06

General

A machine learning and clustering-based approach for county-level COVID-19 analysis.

In PloS one ; h5-index 176.0
COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factors that impact disease propagation. This is especially true for regionally specific predictive models due to either limited case histories or other unique factors characterizing the region. This paper employs both supervised and unsupervised methods to identify the critical county-level demographic, mobility, weather, medical capacity, and health related county-level factors for studying COVID-19 propagation prior to the widespread availability of a vaccine. We use this feature subspace to aggregate counties into meaningful clusters to support more refined disease analysis efforts.
Nicholson Charles, Beattie Lex, Beattie Matthew, Razzaghi Talayeh, Chen Sixia

2022

Internal Medicine

Authors Reply: Using social media to recruit research participants should proceed with caution. Comment on Telemanagement of Home-Isolated COVID-19 Patients Using Oxygen Therapy With Noninvasive Positive Pressure Ventilation and Physical Therapy Techniques: Randomized Clinical Trial.

In Journal of medical Internet research ; h5-index 88.0
No abstract/not applicable.
Adly Aya Sedky, Adly Mahmoud Sedky, Adly Afnan Sedky

2022-Apr-26

General

COVID-19 vaccine hesitancy in eight European countries: Prevalence, determinants, and heterogeneity.

In Science advances
We examine heterogeneity in COVID-19 vaccine hesitancy across eight European countries. We reveal striking differences across countries, ranging from 6.4% of adults in Spain to 61.8% in Bulgaria reporting being hesitant. We experimentally assess the effectiveness of different messages designed to reduce COVID-19 vaccine hesitancy. Receiving messages emphasizing either the medical benefits or the hedonistic benefits of vaccination significantly increases COVID-19 vaccination willingness in Germany, whereas highlighting privileges contingent on holding a vaccination certificate increases vaccination willingness in both Germany and the United Kingdom. No message has significant positive effects in any other country. Machine learning-based heterogeneity analyses reveal that treatment effects are smaller or even negative in settings marked by high conspiracy beliefs and low health literacy. In contrast, trust in government increases treatment effects in some groups. The heterogeneity in vaccine hesitancy and responses to different messages suggests that health authorities should avoid one-size-fits-all vaccination campaigns.
Steinert Janina I, Sternberg Henrike, Prince Hannah, Fasolo Barbara, Galizzi Matteo M, Büthe Tim, Veltri Giuseppe A

2022-Apr-29

Public Health

6G and Artificial Intelligence Technologies for Dementia Care: Literature Review and Practical Analysis.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The dementia epidemic is progressing fast. As the world's older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients' health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients' health care needs and improve their quality of life.

OBJECTIVE : This study aims to investigate ways in which 6G and AI technologies could elevate dementia care to address this study gap.

METHODS : A literature review was conducted in databases such as PubMed, Scopus, and PsycINFO. The search focused on three themes: dementia, 6G, and AI technologies. The initial search was conducted on April 25, 2021, complemented by relevant articles identified via a follow-up search on November 11, 2021, and Google Scholar alerts.

RESULTS : The findings of the study were analyzed in terms of the interplay between people with dementia's unique health challenges and the promising capabilities of health technologies, with in-depth and comprehensive analyses of advanced technology-based solutions that could address key dementia care needs, ranging from impairments in memory (eg, Egocentric Live 4D Perception), speech (eg, Project Relate), motor (eg, Avatar Robot Café), cognitive (eg, Affectiva), to social interactions (eg, social robots).

CONCLUSIONS : To live is to grow old. Yet dementia is neither a proper way to live nor a natural aging process. By identifying advanced health solutions powered by 6G and AI opportunities, our study sheds light on the imperative of leveraging the potential of advanced technologies to elevate dementia patients' will to live, enrich their daily activities, and help them engage in societies across shapes and forms.

Su Zhaohui, Bentley Barry L, McDonnell Dean, Ahmad Junaid, He Jiguang, Shi Feng, Takeuchi Kazuaki, Cheshmehzangi Ali, da Veiga Claudimar Pereira

2022-Apr-27

6G, COVID-19, artificial intelligence, dementia, digital health, first-perspective health solutions

General

Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies.

In International journal of disaster risk reduction : IJDRR
The COVID-19 pandemic has made a significant impact on various supply chains (SCs). All around the world, the COVID-19 pandemic affects different dimensions of SCs, including but not limited to finance, lead time, demand changes, and production performance. There is an urgent need to respond to this grand challenge. The catastrophic impact of the COVID-19 pandemic prompted scholars to develop innovative SC disruption management strategies and disseminate them via numerous scientific articles. However, there is still a lack of systematic literature survey studies that aim to identify promising SC disruption management strategies through the bibliometric, network, and thematic analyses. In order to address this drawback, this study presents a set of up-to-date bibliometric, network, and thematic analyses to identify the influential contributors, main research streams, and disruption management strategies related to the SC performance under the COVID-19 settings. The conducted analyses reveal that resilience and sustainability are the primary SC topics. Furthermore, the major research themes are found to be food, health-related SCs, and technology-aided tools (e.g., artificial intelligence (AI), internet of things (IoT), and blockchains). Various disruption management strategies focusing on resilience and sustainability themes are extracted from the most influential studies that were identified as a part of this work. In addition, we draw some managerial insights to ensure a resilient and sustainable supply of critical products in the event of a pandemic, such as personal protective equipment (PPE) and vaccines.
Moosavi Javid, Fathollahi-Fard Amir M, Dulebenets Maxim A

2022-Jun-01

Bibliometric and network analysis, COVID-19, Literature review, Resilience, Supply chain disruption, Sustainability

Public Health

A deep fuzzy model for diagnosis of COVID-19 from CT images.

In Applied soft computing
From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Therefore, this paper constructs a fuzzy classifier, which aims to detect infected subjects by observing and analyzing the CT images of suspected patients. Firstly, a deep learning algorithm is used to extract the low-level features of CT images in the COVID-CT dataset. Subsequently, we analyze the extracted feature information with attribute reduction algorithm to obtain features with high recognition Then, some key features are selected as the input for the fuzzy diagnosis model to the training model. Finally, several images in the dataset are used as the test set to test the trained fuzzy classifier. The obtained accuracy rate is 94.2%, and the F1-score is 93.8%. Experimental results show that, compared with the deep learning diagnosis methods widely used in medical image analysis, the proposed fuzzy model improves the accuracy and efficiency of diagnosis, which consequently helps to curb the spread of COVID-19.
Song Liping, Liu Xinyu, Chen Shuqi, Liu Shuai, Liu Xiangbin, Muhammad Khan, Bhattacharyya Siddhartha

2022-Apr-22

COVID-19, CT images, Deep learning, Disease prediction, Feature extraction, Fuzzy model

General

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network.

In ACS omega
There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.
Ghouchan Nezhad Noor Nia Raheleh, Jalali Mehrdad, Mail Matthias, Ivanisenko Yulia, Kübel Christian

2022-Apr-19

General

Covid-19 fake news sentiment analysis.

In Computers & electrical engineering : an international journal
'Fake news' refers to the misinformation presented about issues or events, such as COVID-19. Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously, however, they have been ineffectual. This research uses Information Fusion to obtain real news data from News Broadcasting, Health, and Government websites, while fake news data are collected from social media sites. 39 features were created from multimedia texts and used to detect fake news regarding COVID-19 using state-of-the-art deep learning models. Our model's fake news feature extraction improved accuracy from 59.20% to 86.12%. Overall high precision is 85% using the Recurrent Neural Network (RNN) model; our best recall and F1-Measure for fake news were 83% using the Gated Recurrent Units (GRU) model. Similarly, precision, recall, and F1-Measure for real news are 88%, 90%, and 88% using the GRU, RNN, and Long short-term memory (LSTM) model, respectively. Our model outperformed standard machine learning algorithms.
Iwendi Celestine, Mohan Senthilkumar, Khan Suleman, Ibeke Ebuka, Ahmadian Ali, Ciano Tiziana

2022-Jul

Deep learning, Emotions, Fake news, Mining, NLP, Social media

General

Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan.

In Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan

OBJECTIVE : To summarize the potential characteristics of convalescent patients with coronavirus disease 2019 (COVID-19) in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.

METHODS : In this study, we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients. In addition, this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values, including thick and thin, fur, peeling, fat and lean, tooth marks and cracked, and greasy and putrid fur.

RESULTS : We included 2164 tongue images in total (34% from day 0, 35.4% from day 14 and 30.6% from day 28) from convalescent patients. The significance results are shown as follows. Firstly, as the recovery time prolongs, the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively. Secondly, the decrease of abnormality rate of tongue coat, included greasy tongue fur, putrid fur, teeth-mark, thick-thin fur, are of significant statistical difference ( < 0.05). Thirdly, the average value of gray-level co-occurrence matrices increases from 0.173 to 0.194, the average value of entropy increases from 0.606 to 0.665, the average value of inverse difference normalized decrease from 0.981 to 0.979, and the average value of dissimilarity decrease from 0.1576 to 0.1828. The details of other radiomics features are describe in results section.

CONCLUSIONS : Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images, including L color space of the tongue and coat radiomics features analysis. This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.

Shixing Yan, Ziqing Liu, Meng Ren, Haiyang H E, Li Xiao, Feng Guo, Miao Peng, Xiaoxia L I, Yong Wang, Xi X U, Tao Yang, Zuoyu Shao, Jingjing Huang, Mingzhong Xiao

2022-Apr

COVID-19, dynamic change mechanisms, quantitative values, radiomics features analysis, tongue inspection

Internal Medicine

Trends in inflammatory bowel disease infections and vaccinations in the past four decades: A high-level text mining analysis of PubMed publications.

In Human vaccines & immunotherapeutics ; h5-index 43.0

AIM : We aimed at assessing the published literature on different prophylactic screening and vaccination options in inflammatory bowel disease (IBD) patients between 1980 and 2020. Special attention was attributed to latest data assessing covid-19 vaccinations.

METHODS : We have queried PubMed for all available IBD-related entries published during 1980-2020. The following data were extracted for each entry: PubMed unique article ID (PMID), title, publishing journal, abstract text, keywords (if any), and authors' affiliations. Two gastrointestinal specialists decided by consensus on a list of terms to classify entries. The terms belonged to four treatment groups: opportunistic infections, prophylactic screening, prophylactic vaccinations/treatment, and routine vaccines. Annual trends of publications for the years 1980-2020 were plotted for different screening, vaccinations and infection types. Slopes of publication trends were calculated by fitting regression lines to the annual number of publications.

RESULTS : Overall, 98,339 IBD entries were published between 1980 and 2020. Of those, 7773 entries belonged to the investigated groups. Entries concerning opportunistic infections showed the sharpest rise, with 19 entries and 1980 to 423 entries in 2020 (slope 11.3, p < .001). Entries concerning prophylactic screening rose from 10 entries in 1980 to 204 entries in 2020 (slope 5.4, p < .001). Both entries concerning prophylactic vaccinations/treatments and routine vaccines did not show a significant rise (slope 0.33 and slope 0.92, respectively). During the COVID 19 pandemic, a total of 44 publications were identified. Of them, 37 were relevant to vaccines and immune reaction. Nineteen publications (51%) were guidelines/recommendations, and 14 (38%) assessed immune reaction to vaccination, most of them (11, 61%) to mRNA vaccines.

CONCLUSIONS : During the past two decades, along with a rapid increase in biologic therapy, publications regarding opportunistic infections and prophylactic screening increased in a steep slope compared to the two decades in the pre-biologic area. During the COVID-19 pandemic, most publications included vaccination recommendations and guidelines and only 38% included real-world data assessing reaction to vaccinations. More research is needed.

Klang Eyal, Soffer Shelly, Shachar Eyal, Lahat Adi

2022-Apr-26

Machine learning, artificial intelligence, biologic treatment, complications, infections, inflammatory bowel disease, vaccines

General

Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants.

In Infection
An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.
Sharma Shagun, Shrivastava Surabhi, Kausley Shankar B, Rai Beena, Pandit Aniruddha B

2022-Apr-26

COVID-19, Diagnostic devices, Infectious disease, Point-of-care, SARS-CoV-2, Variants

Public Health

Deep learning representations to support COVID-19 diagnosis on CT slices.

In Biomedica : revista del Instituto Nacional de Salud

INTRODUCTION : The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations.

OBJECTIVE : To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples.

MATERIALS AND METHODS : Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers.

RESULTS : The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively.

CONCLUSION : Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Ruano Josué, Arcila John, Romo-Bucheli David, Vargas Carlos, Rodríguez Jefferson, Mendoza Óscar, Plazas Miguel, Bautista Lola, Villamizar Jorge, Pedraza Gabriel, Moreno Alejandra, Valenzuela Diana, Vázquez Lina, Valenzuela-Santos Carolina, Camacho Paul, Mantilla Daniel, Martínez Carrillo Fabio

2022-Mar-01

General

Artificial intelligence at the time of COVID-19: who does the lion's share?

In Clinical chemistry and laboratory medicine ; h5-index 46.0

OBJECTIVES : The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19.

METHODS : We conducted a PubMed search using as query MeSH major terms "Artificial Intelligence" AND "COVID-19", searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved.

RESULTS : Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models.

CONCLUSIONS : The lion's share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.

Negrini Davide, Danese Elisa, Henry Brandon M, Lippi Giuseppe, Montagnana Martina

2022-Apr-25

COVID-19, artificial intelligence, deep learning, digital health, machine learning

Ophthalmology

Building a predictive model to identify clinical indicators for COVID-19 using machine learning method.

In Medical & biological engineering & computing ; h5-index 32.0
Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case-control study with 35 routine-collected clinical indicators and demographic factors to identify predictors for COVID-19 with CAP as controls. We randomly split the dataset into a training set (70%) and testing set (30%). We built Explainable Boosting Machine to select the important factors and built a decision tree on selected variables to interpret their relationships. The top five individual predictors of COVID-19 are albumin, total bilirubin, monocyte count, alanine aminotransferase, and percentage of monocyte with the importance scores ranging from 0.078 to 0.567. The top systematic predictors for COVID-19 are liver function, monocyte increasing, plasma protein, granulocyte, and renal function (importance scores ranging 0.009-0.096). We identified five combinations of important indicators to screen COVID-19 patients from CAP patients with differentiating abilities ranging 83.3-100%. An online predictive tool for our model was published. Certain clinical indicators collected routinely from most hospitals could help screen and distinguish COVID-19 from CAP. While further verification is needed, our findings and predictive tool could help screen suspected COVID-19 cases.
Deng Xinlei, Li Han, Liao Xin, Qin Zhiqiang, Xu Fan, Friedman Samantha, Ma Gang, Ye Kun, Lin Shao

2022-Apr-25

COVID-19, Community-acquired pneumonia, Machine learning, Predictor

Public Health

Prediction of SARS-CoV-2 Infection With a Symptoms-Based Model to Aid Public Health Decision Making in Latin America and other Low and Middle Income Settings.

In Preventive medicine reports
Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of .73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America.
Ramírez Varela Andrea, Moreno López Sergio, Contreras-Arrieta Sandra, Tamayo-Cabeza Guillermo, Restrepo-Restrepo Silvia, Sarmiento-Barbieri Ignacio, Caballero-Díaz Yuldor, Jorge Hernandez-Florez Luis, Mario González John, Salas-Zapata Leonardo, Laajaj Rachid, Buitrago-Gutierrez Giancarlo, de la Hoz-Restrepo Fernando, Vives Florez Martha, Osorio Elkin, Sofía Ríos-Oliveros Diana, Behrentz Eduardo

2022-Apr-20

COVID-19, SARS-CoV-2, anosmia, logistic model, machine learning, symptoms

General

Dealing with the COVID-19 crisis: Theoretical application of social media analytics in government crisis management.

In Public relations review
Little theory-grounded research addresses how to use social media strategically in government public relations through machine learning. To fill this gap, we propose a way to optimize social media analytics to manage issues and crises by using the framework of attribution theory to analyze 360,861 tweets. In particular, we examined the attribution of crisis responsibility related to the spread of COVID-19 and its relations to the negative emotions of U.S. citizens on Twitter for six months (from January 20 to June 30, 2020). The results of this study showed that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis for the Trump administration. In addition, the federal government's lack of response and inability to handle the outbreak led to citizens' engagement and amplification of negative tweets that blamed the Trump White House. Theoretical and practical implications of the results are discussed.
Chon Myoung-Gi, Kim Seonwoo

2022-Sep

Attribution theory, COVID-19, Government crisis management, Machine learning, Social media analytics

Public Health

COVID-19 GPH: tracking the contribution of genomics and precision health to the COVID-19 pandemic response.

In BMC infectious diseases ; h5-index 58.0
The scientific response to the COVID-19 pandemic has produced an abundance of publications, including peer-reviewed articles and preprints, across a wide array of disciplines, from microbiology to medicine and social sciences. Genomics and precision health (GPH) technologies have had a particularly prominent role in medical and public health investigations and response; however, these domains are not simply defined and it is difficult to search for relevant information using traditional strategies. To quantify and track the ongoing contributions of GPH to the COVID-19 response, the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention created the COVID-19 Genomics and Precision Health database (COVID-19 GPH), an open access knowledge management system and publications database that is continuously updated through machine learning and manual curation. As of February 11, 2022, COVID-GPH contained 31,597 articles, mostly on pathogen and human genomics (72%). The database also includes articles describing applications of machine learning and artificial intelligence to the investigation and control of COVID-19 (28%). COVID-GPH represents about 10% (22983/221241) of the literature on COVID-19 on PubMed. This unique knowledge management database makes it easier to explore, describe, and track how the pandemic response is accelerating the applications of genomics and precision health technologies. COVID-19 GPH can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action .
Yu Wei, Drzymalla Emily, Gwinn Marta, Khoury Muin J

2022-Apr-25

COVID-19, Database, Genomics, Precision Public Health, SARS-CoV-2

General

AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.

In BMC bioinformatics

BACKGROUND : The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world.

RESULTS : In this work, we proposed AutoCoV, a deep learning method with multiple loss object, that can track the early spread of COVID-19 in terms of spatial and temporal patterns until the disease is fully spread over the world in July 2020. Performances in learning spatial or temporal patterns were measured with two clustering measures and one classification measure. For annotated SARS-CoV-2 sequences from the National Center for Biotechnology Information (NCBI), AutoCoV outperformed seven baseline methods in our experiments for learning either spatial or temporal patterns. For spatial patterns, AutoCoV had at least 1.7-fold higher clustering performances and an F1 score of 88.1%. For temporal patterns, AutoCoV had at least 1.6-fold higher clustering performances and an F1 score of 76.1%. Furthermore, AutoCoV demonstrated the robustness of the embedding space with an independent dataset, Global Initiative for Sharing All Influenza Data (GISAID).

CONCLUSIONS : In summary, AutoCoV learns geographic and temporal spreading patterns successfully in experiments on NCBI and GISAID datasets and is the first of its kind that learns virus spreading patterns from the genome sequences, to the best of our knowledge. We expect that this type of embedding method will be helpful in characterizing fast-evolving pandemics.

Sung Inyoung, Lee Sangseon, Pak Minwoo, Shin Yunyol, Kim Sun

2022-Apr-25

COVID-19, Deep learning, Early spreading pattern, SARS-CoV-2, Sequence embedding

General

Discovery of new senolytics using machine learning

bioRxiv Preprint
Cellular senescence is a stress response characterised by a permanent cell cycle arrest and a proinflammatory secretome. In addition to its tumour suppressor role, senescence is involved in ageing and promotes many disease processes such as cancer, type 2 diabetes, osteoarthritis, and SARS-CoV-2 infection. There is a growing interest in therapies based on targeted elimination of senescent cells, yet so far only a few such senolytics are known, partly due to the poor grasp of the molecular mechanisms that control the senescence survival programme. Here we report a highly effective machine learning pipeline for the discovery of senolytic compounds. Using solely published data, we trained machine learning algorithms to classify compounds according to their senolytic action. Models were trained on as few as 58 known senolytics against a background of FDA-approved compounds or in late-stage clinical development (2,523 in total). We computationally screened various chemical libraries and singled out top candidates for validation in human lung fibroblasts (IMR90) and lung adenocarcinoma (A549) cell lines. This led to the discovery of three novel senolytics: ginkgetin, oleandrin and periplocin, with potency comparable to current senolytics and a several hundred-fold reduction in experimental screening costs. Our work demonstrates that machine learning can take maximum advantage of existing drug screening data, paving the way for new open science approaches to drug discovery for senescence-associated diseases.
Smer-Barreto, V.; Quintanilla, A.; Elliot, R. J.; Dawson, J. C.; Sun, J.; Carragher, N.; Acosta, J. C.; Oyarzun, D. A.

2022-04-27

General

Mobile perceived trust mediation on the intention and adoption of FinTech innovations using mobile technology: A systematic literature review.

In F1000Research
The banking and financial sectors have witnessed a significant development recently due to financial technology (FinTech), and it has become an essential part of the financial system. Many factors helped the development of this sector, including the pandemics such as Covid-19, the considerable increasing market value of the FinTech sector worldwide, and new technologies such as blockchain, artificial intelligence, big data, cloud computing and mobile technology. Moreover, changes in consumer's preferences, especially the Z-generation (digital generation). FinTech shifted the traditional business models to mobile platforms characterized by ease of access and swift transactions. Mobile technology became the main backbone for FinTech innovations and acts as a channel to deliver FinTech services that overcome all geographical and timing barriers, thus enhancing financial inclusion. Mobile perceived Trust (MPT), or the trust in using financial business models via mobile technology, is a crucial factor in the FinTech context that has mediation effects on the intention and adoption of different FinTech business models. Unfortunately, few studies have explored MPT mediations on consumers' intention to adopt FinTech innovations using mobile technology. Typically, many studies examined trust/MPT as an independent and unidirectional variable and investigated its effects on behaviour intention without predicting its mediation effects. This study aimed to develop a systematic literature review on MPT mediation in FinTech, focusing on the period from 2016 and 2021, in journals ranked Q1 and Q2, and known-based theories such as the technology acceptance model, the unified theory of acceptance and use of technology, and the mobile technology acceptance model. This study found that only four articles were published in Q1 and Q2 journals. In these articles, the MPT was used as a mediator, and its effects were measured on the intention and adoption of the behaviour.
Dawood Hatim M, Liew Chee Yoong, Lau Teck Chai

2021

Benefit-Risk framework., Fintech, Mobile Perceived Trust, Mobile technology acceptance model, Net Valence framework, Perceived Benefit, Perceived Risk

General

The Role of Machine Learning and Artificial Intelligence for making a Digital Classroom and its sustainable Impact on Education during Covid-19.

In Materials today. Proceedings
During the Disease outbreak and in the future, there will be a lot of learning. Since the pandemic has interrupted global schooling, remote learning has emerged as a viable option, depending on machine learning to accomplish its goals. Using the example of ten international science journals that speak out about artificial intelligence in education today and the future of earning, we hope to gain a better understanding of the large extend of the power of artificial intelligence in education, both during the COVID-19 period and during the future learning time frame. Additionally, in addition to evaluating 10 articles, we used an internet search engine to look for relevant material. We conducted searches using terms such as artificial intelligence, learning during a pandemic, and Machine learning, among other things. After that, we used a phenomenological technique to confirm that our results answered the research questions, which was done in accordance with a qualitative approach. Our findings can be summarized by taking into account the evidence from research and literature. Among our findings are that the detailed assessment of artificial intelligence in education, the use of AI in education, typical learning in the pandemic era, and the role of artificial intelligence (AI) disease outbreak learning are all important for both current and future residents. While statistical methods and automated based on learning jobs that are smarter than normal continue to be important, learning is becoming more automated. It helps individuals to be more concentrated on their learning opportunities and to recognize when they do not grasp a subject completely. First and foremost, the instructors provide valuable assistance throughout the assessment process of student learning outcomes.
Ara Shaikh Asmat, Kumar Anuj, Jani Kruti, Mitra Saloni, García-Tadeo Diego A, Devarajan Agilandeswari

2022

Artificial Intelligence (AI), Covid-19, E-learning, Education, Machine Learning (ML), Pandemic

Public Health

An online advertising intervention to increase adherence to stay-at-home-orders during the COVID-19 pandemic: An efficacy trial monitoring individual-level mobility data.

In International journal of applied earth observation and geoinformation : ITC journal
The COVID-19 pandemic has led public health departments to issue several orders and recommendations to reduce COVID-19-related morbidity and mortality. However, for various reasons, including lack of ability to sufficiently monitor and influence behavior change, adherence to these health orders and recommendations has been suboptimal. Starting April 29, 2020, during the initial stay-at-home orders issued by various state governors, we conducted an intervention that sent online website and mobile application advertisements to people's mobile phones to encourage them to adhere to stay-at-home orders. Adherence to stay-at-home orders was monitored using individual-level cell phone mobility data, from April 29, 2020 through May 10, 2020. Mobile devices across 5 regions in the United States were randomly-assigned to either receive advertisements from our research team advising them to stay at home to stay safe (intervention group) or standard advertisements from other advertisers (control group). Compared to control group devices that received only standard corporate advertisements (i.e., did not receive public health advertisements to stay at home), the (intervention group) devices that received public health advertisements to stay at home demonstrated objectively-measured increased adherence to stay at home (i.e., smaller radius of gyration, average travel distance, and larger stay-at-home ratios). Results suggest that 1) it is feasible to use mobility data to assess efficacy of an online advertising intervention, and 2) online advertisements are a potentially effective method for increasing adherence to government/public health stay-at-home orders.
Garett Renee R, Yang Jiannan, Zhang Qingpeng, Young Sean D

2022-Apr

Artificial intelligence, COVID-19, Digital health, Intervention, Mobility

General

A face detection ensemble to monitor the adoption of face masks inside the public transportation during the COVID-19 pandemic.

In Multimedia tools and applications
The designing of ensembles is widely adopted when single machine learning methods fail to obtain satisfactory performances by analyzing complex data characterized by being imbalanced, high-dimensional, and noisy. Such a failure is a well-known statistical challenge when the learning algorithm searches for a model in a large space of hypotheses and the data do not significantly represent the problem, thus not inducing it from a space of admissible functions towards the best global model. We have addressed this issue in a real-world application, whose main objective was to identify whether users were wearing masks inside public transportation during the COVID-19 pandemic. Several studies have already pointed that face masks are an important and efficient non-pharmacological strategy to reduce the virus spread. In this sense, we designed an approach using Convolutional Neural Networks (CNN) to track the adoption of masks in different transportation lines, regions, days, and time. Aiming at reaching this goal, we propose an ensemble of face detectors and a CNN architecture, called MaskNet, to analyze all public-transport passengers and provide valuable information to policymakers, which are able to dedicate efforts to more effective advertisements and awareness work. In practice, our approach is running in a real scenario in Salvador (Brazil).
Canário João Paulo, Ferreira Marcos Vinícius, Freire Junot, Carvalho Matheus, Rios Ricardo

2022-Apr-20

Covid-19, Deep Learning, Ensemble models, Face detection, Mask detection

General

Implementation of smart social distancing for COVID-19 based on deep learning algorithm.

In Multimedia tools and applications
The first step to reducing the effect of viral disease is to prevent the spread which could be achieved by implementing social distancing (reducing the number of close physical interactions between peoples). Almost every viral disease whose means of communication is air, and enters through mouth or nose, definitely will affect our vocal organs which cause changes in features of our voice and could be traceable using feature analysis of voice using deep learning. The detection of an affected person using deep neural networks and tracking him would help us in the implementation of the social distancing rule in an area where it is needed. The aim of this paper is to study different solutions which help in enabling, encouraging, and even enforcing social distancing. In this paper, we implemented and analyzed scenarios on the basis of COVID-19 patient detection using cough and tracking him using smart cameras, or emerging wireless technologies with deep learning techniques for prediction and preventing the spread of disease. Thus these techniques are easy to be implemented in the initial stage of any pandemic as well and will help us in the implementation of smart social distancing (apply whenever needed).
Haq Izaz Ul, Du Xianjun, Jan Haseeb

2022-Apr-20

Audio signal, Covid-19, Deep learning, Pandemic, Social distancing

General

Impact of Different Styles of Online Course Videos on Students' Attention During the COVID-19 Pandemic.

In Frontiers in public health

Background : The COVID-19 pandemic interfered with normal campus life, resulting in the need for the course to be conducted in an ideal online format. The purpose of this study is to analyze the impact of different styles of online political course videos on students' attention during the COVID-19 pandemic.

Methods : Four college students participated in this small sample study. They were required to conduct two sessions of the experiment, in which they were required to watch three different styles of course videos in each session. While watching the videos, their EEG signals were acquired. For the acquired EEG signals, the sample entropy (SampEn) features were extracted. On the other hand, Mayer's theories of multimedia technology provide guidance for teachers' online courses to enhance students' attention levels. The results of EEG signals analysis and Mayer's theories of multimedia technology were combined to compare and analyze the effects of three styles of instructional videos.

Results : Based on comparisons of the SampEn and Mayer's theories of multimedia technology analysis, the results suggest that online instruction in a style where the instructor and content appear on the screen at the same time and the instructor points out the location of the content as it is explained is more likely to elicit higher levels of students' attention.

Conclusions : During the COVID-19 pandemic, online instructional methods have an impact on students' classroom attention. It is essential for teachers to design online instructional methods based on students' classroom attention levels and some multimedia instructional techniques to improve students' learning efficiency.

Gao Qi, Tan Ying

2022

COVID-19, EEG, “Mayers theories of multimedia technology”, classroom attention, sample entropy

General

Sentimental and spatial analysis of COVID-19 vaccines tweets.

In Journal of intelligent information systems
The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people's sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines.
Umair Areeba, Masciari Elio

2022-Apr-15

COVID, Sentimental analysis, Spatial analysis, Vaccine hesitancy, Vaccines

General

Leveraging deep learning for COVID-19 diagnosis through chest imaging.

In Neural computing & applications
COVID-19 has taken a toll on the entire world, rendering serious illness and high mortality rate. In the present day, when the globe is hit by a pandemic, those suspected to be infected by the virus need to confirm its presence to seek immediate medical attention to avoid adverse outcomes and also to prevent further transmission of the virus in their close contacts by ensuring timely isolation. The most reliable laboratory testing currently available is the reverse transcription-polymerase chain reaction (RT-PCR) test. Although the test is considered gold standard, 20-25% of results can still be false negatives, which has lately led physicians to recommend medical imaging in specific cases. Our research examines the aspect of chest imaging as a method to diagnose COVID-19. This work is not directed to establish an alternative to RT-PCR, but to aid physicians in determining the presence of virus in medical images. As the disease presents lung involvement, it provides a basis to explore computer vision for classification in radiographic images. In this paper, authors compare the performance of various models, namely ResNet-50, EfficientNetB0, VGG-16 and a custom convolutional neural network (CNN) for detecting the presence of virus in chest computed tomography (CT) scan and chest X-ray images. The most promising results have been derived by using ResNet-50 on CT scans with an accuracy of 98.9% and ResNet-50 on X-rays with an accuracy of 98.7%, which offer an opportunity to further explore these methods for prospective use.
Khurana Yashika, Soni Umang

2022-Apr-19

COVID-19, Chest imaging, Convolutional neural network, Deep learning

General

Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity.

In PloS one ; h5-index 176.0
COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determined key molecules and biological pathways in disease severity, and disease status. In addition to the individual omics analyses, we perform the integrative method Sparse Multiple Canonical Correlation Analysis to analyse relationships of the different view of data. Our findings suggest that COVID-19 status is associated with the cell cycle and death, as well as the inflammatory response. This relationship is reflected in all four sets of molecules analyzed. We further observe that the metabolic processes, particularly processes to do with vitamin absorption and cholesterol are implicated in COVID-19 status and severity.
Lipman Danika, Safo Sandra E, Chekouo Thierry

2022

Public Health

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.

In The Lancet. Digital health

BACKGROUND : Predicting outcomes of patients with COVID-19 at an early stage is crucial for optimised clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, because of their requirements for extensive data preprocessing and feature engineering, they have not been validated or implemented outside of their original study site. Therefore, we aimed to develop accurate and transferrable predictive models of outcomes on hospital admission for patients with COVID-19.

METHODS : In this study, we developed recurrent neural network-based models (CovRNN) to predict the outcomes of patients with COVID-19 by use of available electronic health record data on admission to hospital, without the need for specific feature selection or missing data imputation. CovRNN was designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and prolonged hospital stay (>7 days). For in-hospital mortality and mechanical ventilation, CovRNN produced time-to-event risk scores (survival prediction; evaluated by the concordance index) and all-time risk scores (binary prediction; area under the receiver operating characteristic curve [AUROC] was the main metric); we only trained a binary classification model for prolonged hospital stay. For binary classification tasks, we compared CovRNN against traditional machine learning algorithms: logistic regression and light gradient boost machine. Our models were trained and validated on the heterogeneous, deidentified data of 247 960 patients with COVID-19 from 87 US health-care systems derived from the Cerner Real-World COVID-19 Q3 Dataset up to September 2020. We held out the data of 4175 patients from two hospitals for external validation. The remaining 243 785 patients from the 85 health systems were grouped into training (n=170 626), validation (n=24 378), and multi-hospital test (n=48 781) sets. Model performance was evaluated in the multi-hospital test set. The transferability of CovRNN was externally validated by use of deidentified data from 36 140 patients derived from the US-based Optum deidentified COVID-19 electronic health record dataset (version 1015; from January, 2007, to Oct 15, 2020). Exact dates of data extraction were masked by the databases to ensure patient data safety.

FINDINGS : CovRNN binary models achieved AUROCs of 93·0% (95% CI 92·6-93·4) for the prediction of in-hospital mortality, 92·9% (92·6-93·2) for the prediction of mechanical ventilation, and 86·5% (86·2-86·9) for the prediction of a prolonged hospital stay, outperforming light gradient boost machine and logistic regression algorithms. External validation confirmed AUROCs in similar ranges (91·3-97·0% for in-hospital mortality prediction, 91·5-96·0% for the prediction of mechanical ventilation, and 81·0-88·3% for the prediction of prolonged hospital stay). For survival prediction, CovRNN achieved a concordance index of 86·0% (95% CI 85·1-86·9) for in-hospital mortality and 92·6% (92·2-93·0) for mechanical ventilation.

INTERPRETATION : Trained on a large, heterogeneous, real-world dataset, our CovRNN models showed high prediction accuracy and transferability through consistently good performances on multiple external datasets. Our results show the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering.

FUNDING : Cancer Prevention and Research Institute of Texas.

Rasmy Laila, Nigo Masayuki, Kannadath Bijun Sai, Xie Ziqian, Mao Bingyu, Patel Khush, Zhou Yujia, Zhang Wanheng, Ross Angela, Xu Hua, Zhi Degui

2022-Apr-21

Surgery

Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data.

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

BACKGROUND : In the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay.

METHODS : We used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the performance of two stage hybrid classification-regression approach.

RESULTS : Our results show that while it is possible to accurately predict short length of stays, predicting longer length of stay is extremely challenging. As such, we opt for a two-stage model that first classifies patients into long versus short length of stays and then a second stage that fits a regresssor among those predicted to have a short length of stay.

DISCUSSION : The results indicate both the challenges and considerations necessary to applying machine-learning methods to skewed outcomes.

CONCLUSIONS : Two-stage models allow those developing clinical decision support tools to explicitly acknowledge where they can and cannot make accurate predictions.

Xu Zhenhui, Zhao Congwen, Scales Charles D, Henao Ricardo, Goldstein Benjamin A

2022-Apr-24

Clinical decision support, Electronic health records, Machine learning, Surgical outcomes

Radiology

Quantum-classical convolutional neural networks in radiological image classification

ArXiv Preprint
Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts -- which might be particularly beneficial in situations with little training data available. Such situations naturally arise in medical classification tasks. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed. They are applied to two- and three-dimensional medical imaging data, e.g. featuring different, potentially malign, lesions in computed tomography scans. The performance of these QCCNNs is already similar to the one of their classical counterparts -- therefore encouraging further studies towards the direction of applying these algorithms within medical imaging tasks.
Andrea Matic, Maureen Monnet, Jeanette Miriam Lorenz, Balthasar Schachtner, Thomas Messerer

2022-04-26

General

Mapping state-sponsored information operations with multi-view modularity clustering.

In EPJ data science
This paper presents a new computational framework for mapping state-sponsored information operations into distinct strategic units. Utilizing a novel method called multi-view modularity clustering (MVMC), we identify groups of accounts engaged in distinct narrative and network information maneuvers. We then present an analytical pipeline to holistically determine their coordinated and complementary roles within the broader digital campaign. Applying our proposed methodology to disclosed Chinese state-sponsored accounts on Twitter, we discover an overarching operation to protect and manage Chinese international reputation by attacking individual adversaries (Guo Wengui) and collective threats (Hong Kong protestors), while also projecting national strength during global crisis (the COVID-19 pandemic). Psycholinguistic tools quantify variation in narrative maneuvers employing hateful and negative language against critics in contrast to communitarian and positive language to bolster national solidarity. Network analytics further distinguish how groups of accounts used network maneuvers to act as balanced operators, organized masqueraders, and egalitarian echo-chambers. Collectively, this work breaks methodological ground on the interdisciplinary application of unsupervised and multi-view methods for characterizing not just digital campaigns in particular, but also coordinated activity more generally. Moreover, our findings contribute substantive empirical insights around how state-sponsored information operations combine narrative and network maneuvers to achieve interlocking strategic objectives. This bears both theoretical and policy implications for platform regulation and understanding the evolving geopolitical significance of cyberspace.
Uyheng Joshua, Cruickshank Iain J, Carley Kathleen M

2022

COVID-19 pandemic, Information operations, Multi-view modularity clustering, Social cyber-security, State-sponsored disinformation, Unsupervised machine learning

General

Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset.

In Applied soft computing
The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people's emotions. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. Pre-trained transformer-based models BERT, DistilBERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge. The AVEDL Model outperforms standard deep learning and machine learning models by attaining an accuracy of 86.46 percent and an F1-score of 85.20 percent.
Nimmi K, Janet B, Selvan A Kalai, Sivakumaran N

2022-Apr-18

BERT, COVID-19, Deep learning, DistilBERT, Emergency response support system (ERSS), Emotion detection., Ensemble model, Health emergency, RoBERTa

Public Health

COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.

In International journal of imaging systems and technology
Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artificial intelligence (AI) and machine learning (ML) techniques to detect COVID-19 infection in patients through chest X-ray image analysis. The use of medical imaging with different modalities for COVID-19 detection has become an important means of containing the spread of this disease. However, medical images are not sufficiently adequate for routine clinical use; there is, therefore, an increasing need for AI to be applied to improve the diagnostic performance of medical image analysis. Regrettably, due to the evolving nature of the COVID-19 global epidemic, the systematic collection of a large data set for deep neural network (DNN)/ML training is problematic. Inspired by these studies, and to aid in the medical diagnosis and control of this contagious disease, we suggest a novel approach that ensembles the feature selection capability of the optimized artificial immune networks (opt-aiNet) algorithm with deep learning (DL) and ML techniques for better prediction of the disease. In this article, we experimented with a DNN, a convolutional neural network (CNN), bidirectional long-short-term memory, a support vector machine (SVM), and logistic regression for the effective detection of COVID-19 in patients. We illustrate the effectiveness of this proposed technique by using COVID-19 image datasets with a variety of modalities. An empirical study using the COVID-19 image dataset demonstrates that the proposed hybrid approaches, named COVID-opt-aiNet, improve classification accuracy by up to 98%-99% for SVM, 96%-97% for DNN, and 70.85%-71% for CNN, to name a few examples. Furthermore, statistical analysis ensures the validity of our proposed algorithms. The source code can be downloaded from Github: https://github.com/faizakhan1925/COVID-opt-aiNet.
Kanwal Summrina, Khan Faiza, Alamri Sultan, Dashtipur Kia, Gogate Mandar

2022-Mar

COVID‐19, bidirectional long‐short‐term memory, clinical decision support system, convolution neural network, deep learning neural network, feature selection, optimized artificial immune network, support vector machine

General

Detection and diagnosis of COVID-19 infection in lungs images using deep learning techniques.

In International journal of imaging systems and technology
World's science and technologies have been challenged by the COVID-19 pandemic. Each and every community across the globe are trying to find a real-time novel method for accurate treatment and cure of COVID-19 infected patients. The most important lead to take from this pandemic is to detect the infected patients as soon as possible and provide them an accurate treatment. At present, the worldwide methodology to detect COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR). This technique is costly and time taking. For this reason, the implementation of a novel method is required. This paper includes the use of deep learning analysis to develop a system for identifying COVID-19 patients. Proposed technique is based on convolution neural network (CNN) and deep neural network (DNN). This paper proposes two models, first is designing DNN on the basis of fractal feature of the images and second is designing CNN using lungs x-ray images. To find the infected area (tissues) of the lungs image using CNN architecture, segmentation process has been used. Developed CNN architecture gave results of classification with accuracy equal to 94.6% and sensitivity equal to 90.5% which is much better than the proposed DNN method, which gave accuracy 84.11% and sensitivity 84.7%. The outcome of the presented model shows 94.6% accuracy in detecting infected regions. Using this method the growth of the infected regions can be monitored and controlled. The designed model can also be used in post-COVID-19 analysis.
Kumar Arun, Mahapatra Rajendra Prasad

2022-Mar

COVID‐19, classification, convolution neural network, deep neural network, segmentation

General

A lightweight capsule network architecture for detection of COVID-19 from lung CT scans.

In International journal of imaging systems and technology
COVID-19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large-scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT-PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID-19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID-19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android-based app can be deployed using MobileCapsNet model to detect COVID-19 as it is a lightweight model and best suited for handheld devices like a mobile.
Tiwari Shamik, Jain Anurag

2022-Mar

COVID‐19, CapsNet, DenseNet, MobileNet, ResNet, VGG16, deep learning, lung CT scan

Radiology

Can laboratory parameters be an alternative to CT and RT-PCR in the diagnosis of COVID-19? A machine learning approach.

In International journal of imaging systems and technology
In this study, a machine learning-based decision support system that uses routine laboratory parameters has been proposed in order to increase the diagnostic success in COVID-19. The main goal of the proposed method was to reduce the number of misdiagnoses in the RT-PCR and CT scans and to reduce the cost of testing. In this study, we retrospectively reviewed the files of patients who presented to the coronavirus outpatient. The demographic, thoracic CT, and laboratory data of the individuals without any symptoms of the disease, who had negative RT-PCR test and who had positive RT-PCR test were analyzed. CT images were classified using hybrid CNN methods to show the superiority of the decision support system using laboratory parameters. Detection of COVID-19 from CT images achieved an accuracy of 97.56% with the AlexNet-SVM hybrid method, while COVID-19 was classified with an accuracy of 97.86% with the proposed method using laboratory parameters.
Kalaycı Mehmet, Ayyıldız Hakan, Tuncer Seda Arslan, Bozdag Pinar Gundogan, Karlidag Gulden Eser

2022-Mar

COVID‐19, artificial intelligence, laboratory parameters, machine learning

Public Health

MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network.

In Frontiers in microbiology
Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug-virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from https://github.com/Pijiangsheng/MDGNN.
Pi Jiangsheng, Jiao Peishun, Zhang Yang, Li Junyi

2022

SARS-CoV-2, antimicrobial drug prediction, graph convolution networks (GCN), heterogeneous network (Het-Net), representation learning

General

Multimodal covid network: Multimodal bespoke convolutional neural network architectures for COVID-19 detection from chest X-ray's and computerized tomography scans.

In International journal of imaging systems and technology
AI-based tools were developed in the existing works, which focused on one type of image data; either CXR's or computerized tomography (CT) scans for COVID-19 prediction. There is a need for an AI-based tool that predicts COVID-19 detection from chest images such as Chest X-ray (CXR) and CT scans given as inputs. This research gap is considered the core objective of the proposed work. In the proposed work, multimodal CNN architectures were developed based on the parameters and hyperparameters of neural networks. Nine experiments evaluate optimizers, learning rates, and the number of epochs. Based on the experimental results, suitable parameters are fixed for multimodal architecture development for COVID-19 detection. We have constructed a bespoke convolutional neural network (CNN) architecture named multimodal covid network (MMCOVID-NET) by varying the number of layers from two to seven, which can predict covid or normal images from both CXR's and CT scans. In the proposed work, we have experimented by constructing 24 models for COVID-19 prediction. Among them, four models named MMCOVID-NET-I, MMCOVID-NET-II, MMCOVID-NET-III, and MMCOVID-NET-IV performed well by producing an accuracy of 100%. We obtained these results from a small dataset. So we repeated these experiments in a larger dataset. We inferred that MMCOVID-NET-III outperformed all the state-of-the-art methods by producing an accuracy of 99.75%. The experiments carried out in this work conclude that the parameters and hyperparameters play a vital role in increasing or decreasing the model's performance.
Padmapriya Thiyagarajan, Kalaiselvi Thiruvenkatam, Priyadharshini Venugopal

2022-Jan-31

COVID‐19, CT scans, artificial intelligence, chest X‐rays, convolutional neural networks, coronavirus disease, deep neural networks

General

Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

In Frontiers in genetics ; h5-index 62.0
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
Nagpal Sargun, Pal Ridam, Ashima Tyagi, Ananya Tripathi, Sadhana Nagori, Aditya Ahmad, Saad Mishra, Hara Prasad Malhotra, Rishabh Kutum, Rintu Sethi

2022

SARS-CoV-2, genomic surveillance, natural language preprocessing, supervised predictions, unsupervised modeling

General

Twitter Sentiment Analysis Using Ensemble based Deep Learning Model towards COVID-19 in India and European Countries.

In Pattern recognition letters
As of November 2021, more than 24.80 crore people are diagnosed with the coronavirus in that around 50.20 lakhs people lost their lives, because of this infectious disease. By understanding the people's sentiment's expressed in their social media (Facebook, Twitter, Instagram etc.) helps their governments in controlling, monitoring, and eradicating the coronavirus. Compared to other social media's, the twitter data are indispensable in the extraction of useful awareness information related to any crisis. In this article, a sentiment analysis model is proposed to analyze the real time tweets, which are related to coronavirus. Initially, around 3100 Indian and European people's tweets are collected between the time period of 23.03.2020 to 01.11.2021. Next, the data pre-processing and exploratory investigation are accomplished for better understanding of the collected data. Further, the feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF), GloVe, pre-trained Word2Vec, and fast text embedding's. The obtained feature vectors are fed to the ensemble classifier (Gated Recurrent Unit (GRU) and Capsule Neural Network (CapsNet)) for classifying the user's sentiment's as anger, sad, joy, and fear. The obtained experimental outcomes showed that the proposed model achieved 97.28% and 95.20% of prediction accuracy in classifying the both Indian and European people's sentiments.
Sunitha D, Patra Raj Kumar, Babu N V, Suresh A, Gupta Suresh Chand

2022-Apr-18

Public Health

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19.

In Social science & medicine (1982)
With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.
Galetsi Panagiota, Katsaliaki Korina, Kumar Sameer

2022-Apr-12

Artificial intelligence, Big data analytics, Covid-19 pandemic, Diversity equity and inclusion, PRISMA, Prediction models, Public health, Social medicine

General

Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics.

In PloS one ; h5-index 176.0
The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds. To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase (PDB 4B3P), 5-HT2A serotonin receptor (PDB 6A94), and H1 histamine receptor (PDB 3RZE). The Enrichment Factor for each target was, respectively, 102.5, 12.4, 10.6, which are considered significant values. Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-2 Mpro. The best scored poses were the input for MM-PBSA calculations and metadynamics using CHARMM and AMBER force fields to predict the binding energy for each complex. Our work points out six molecules, highlighting the strong interaction obtained for Mpro-mirabegron complex. Among these six, to the best of our knowledge, ambenonium has not yet been described in the literature as a candidate inhibitor for the SARS-COV-2 main protease in its active pocket.
Gomes Isabela de Souza, Santana Charles Abreu, Marcolino Leandro Soriano, Lima Leonardo Henrique França de, Melo-Minardi Raquel Cardoso de, Dias Roberto Sousa, de Paula Sérgio Oliveira, Silveira Sabrina de Azevedo

2022

General

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements.

In Science advances
It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO₂) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO₂ from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO₂ emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO₂ based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen" (APO). We detect and quantify ffCO₂ reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO₂ emissions reductions from three independent lower-frequency U.K. estimates.
Pickers Penelope A, Manning Andrew C, Le Quéré Corinne, Forster Grant L, Luijkx Ingrid T, Gerbig Christoph, Fleming Leigh S, Sturges William T

2022-Apr-22

Oncology

Global Aging and Cancer: Advancing Care Through Innovation.

In American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
The oncology field, like many others, is digitalizing rapidly, a phenomenon that may have been accelerated by the COVID-19 pandemic. This movement is creating opportunities and challenges. Another rapidly developing change is the aging of the global population; because cancer is a disease of aging, there is a need for health systems to adapt to taking care of such patients. In this article, we address how these innovative technologies can be leveraged to improve the care of older patients with cancer beyond academic centers, such as in underserved areas and low- and middle-income countries. We review how digital technologies can be used to enhance the follow-up of patients in low- and middle-income countries. We also tackle the issue of training a global workforce to treat cancer in an aging population and how to leverage innovations in this matter. Finally, we review opportunities to expand the usefulness of big data and machine learning beyond academic centers to support private practices and underserved areas.
Extermann Martine, Hernández-Favela Celia Gabriela, Soto Perez de Celis Enrique, Kanesvaran Ravindran

2022-Apr

Ophthalmology

Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : With the emerging information and communication technology, the field of medical informatics has dramatically evolved in health care and medicine. Thus, it is crucial to explore the global scientific research landscape on medical informatics.

OBJECTIVE : This study aims to present a visual form to clarify the overall scientific research trends of medical informatics in the past decade.

METHODS : A bibliometric analysis of data retrieved and extracted from the Web of Science Core Collection (WoSCC) database was performed to analyze global scientific research trends on medical informatics, including publication year, journals, authors, institutions, countries/regions, references, and keywords, from January 1, 2011, to December 31, 2020.

RESULTS : The data set recorded 34,742 articles related to medical informatics from WoSCC between 2011 and 2020. The annual global publications increased by 193.86% from 1987 in 2011 to 5839 in 2020. Journal of Medical Internet Research (3600 publications and 63,932 citations) was the most productive and most highly cited journal in the field of medical informatics. David W Bates (99 publications), Harvard University (1161 publications), and the United States (12,927 publications) were the most productive author, institution, and country, respectively. The co-occurrence cluster analysis of high-frequency author keywords formed 4 clusters: (1) artificial intelligence in health care and medicine; (2) mobile health; (3) implementation and evaluation of electronic health records; (4) medical informatics technology application in public health. COVID-19, which ranked third in 2020, was the emerging theme of medical informatics.

CONCLUSIONS : We summarize the recent advances in medical informatics in the past decade and shed light on their publication trends, influential journals, global collaboration patterns, basic knowledge, research hotspots, and theme evolution through bibliometric analysis and visualization maps. These findings will accurately and quickly grasp the research trends and provide valuable guidance for future medical informatics research.

He Xuefei, Peng Cheng, Xu Yingxin, Zhang Ye, Wang Zhongqing

2022-Apr-21

VOSviewer, bibliometrics, data visualization, medical informatics

Radiology

COVID-19 pneumonia chest radiographic severity score: Variability assessment among experienced and In-training radiologists and creation of a Multi-reader composite score database for artificial intelligence algorithm development.

In The British journal of radiology

OBJECTIVE : The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on CXR, and to create a multi reader database suitable for AI development.

METHODS : In this study, CXRs from PCR positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intra observer variability. Severity classification was assessed using a four-class system: normal(0), mild, moderate, and severe. A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multi reader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability.

RESULTS : A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ±1), 189 men). Inter observer variability between the radiologists ranges between 0.67-0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower inter observer agreement of 0.66 with each other and between 0.69-0.71 with experienced radiologists. Intra observer agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, two or three class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference.

CONCLUSION : Experienced and in-training radiologists demonstrate good inter and intra observer agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms.

ADVANCES IN KNOWLEDGE : Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

van Assen Marly, Zandehshahvar Mohammadreza, Maleki Hossein, Kiarashi Yashar, Arleo Timothy, Stillman Arthur E, Filev Peter, Davarpanah Amir H, Berkowitz Eugene A, Tigges Stefan, Lee Scott J, Vey Brianna L, Adibi Ali, De Cecco Carlo Nicola

2022-Apr-22

General

Viruses Broaden the Definition of Life by Genomic Incorporation of Artificial Intelligence and Machine Learning Processes.

In Current neuropharmacology
Viruses have been classified as non-living because they require a cellular host to support their replicative processes. Empirical investigations have significantly advanced our understanding of the many strategies employed by viruses to usurp and divert host regulatory and metabolic processes to drive the synthesis and release of infectious particles. The recent emergence of SARS-CoV-2 has permitted us to evaluate and discuss a potentially novel classification of viruses as living entities. The ability of SARS CoV-2 to engender comprehensive regulatory control of integrative cellular processes is strongly suggestive of an inherently dynamic informational registry that is programmatically encoded by linear ssRNA sequences responding to distinct evolutionary constraints. Responses to positive evolutionary constraints have resulted in a single-stranded RNA viral genome that occupies a three-dimensional space defined by conserved base-paring resulting from a complex pattern of both secondary and tertiary structures. Additionally, regulatory control of virus-mediated infectious processes relies on extensive protein-protein interactions that drive conformational matching and shape recognition events to provide a functional link between complementary viral and host nucleic acid and protein domains. We also recognize that the seamless integration of complex replicative processes is highly dependent on the precise temporal matching of complementary nucleotide sequences and their corresponding structural and non-structural viral proteins. Interestingly, the deployment of concerted transcriptional and translational activities within targeted cellular domains may be modeled by artificial intelligence (AI) strategies that are inherently fluid, self-correcting, and adaptive at accommodating temporal changes in host defense mechanisms. In depth understanding of multiple self-correcting AI-associated viral processes will most certainly lead to novel therapeutic development platforms, notably the design of efficacious neuropharmacological agents to treat chronic CNS syndromes associated with long-COVID. In summary, it appears that viruses, notably SARS-CoV-2, are very much alive due to acquired genetic advantages that are intimately entrained to existential host processes via evolutionarily constrained AI-associated learning paradigms.
Stefano George B, Kream Richard M

2022-Apr-20

Artificial Intelligence, Long-COVID, SARS-CoV-2; RNA-dependent RNA polymerase, Virus, eukaryotic genome, mitochondrial genome

General

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning.

In Scientific reports ; h5-index 158.0
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55-0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61-0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.
Pedrosa João, Aresta Guilherme, Ferreira Carlos, Carvalho Catarina, Silva Joana, Sousa Pedro, Ribeiro Lucas, Mendonça Ana Maria, Campilho Aurélio

2022-Apr-21

General

Virtualized Gamified Pharmacy Simulation during COVID-19.

In Pharmacy (Basel, Switzerland)
Extended and immersive gamified pharmacy simulation has been demonstrated to provide transformative learning in pharmacy education, preparing graduates for real-world practice. An international consortium of universities has implemented local adaptations of the Pharmacy Game into their curricula. From early 2020, pharmacy academics modified the delivery of gamified simulation in response to the COVID-19 pandemic, while still aiming to deliver the important learning outcomes of enhanced communication, collaboration, confidence and competence. Australian universities went into full lockdown from March 2020, and the critical gamified simulation at Griffith University was delivered entirely virtually in 2020. An array of synchronous and asynchronous approaches and software platforms was employed, including Microsoft Teams, Forms and Stream plus the online interview platform Big Interview. These allowed for the simulation activities, including dispensing, counselling and clinical cases, to be conducted by students online. In 2021, Griffith University conducted hybrid delivery of its Pharmacy Game, balancing student participation both in person and online. Microsoft Power Apps was added to the hosting platform to enhance the simulation interface, and Power Virtual Agent artificial intelligence chatbots, with natural language processing, were used to enable asynchronous clinical interaction. The combination of learning technologies provided the means to deliver successful gamified simulation in the virtual and hybrid environments while still achieving outstanding learning outcomes from the capstone activity. This paper details the technologies used to virtualize the Australian Pharmacy Game and the analytics available to educators to assess student participation, engagement and performance.
Hope Denise L, Grant Gary D, Rogers Gary D, King Michelle A

2022-Mar-26

active learning, experiential learning, gamification, pharmacy education, simulation, virtualization

General

COVID-19 deaths: Which explanatory variables matter the most?

In PloS one ; h5-index 176.0
More than a year since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), many questions about the disease COVID-19 have been answered; however, many more remain poorly understood. Although the situation continues to evolve, it is crucial to understand what factors may be driving transmission through different populations, both for potential future waves, as well as the implications for future pandemics. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted population density (PWPD), some "stay at home" metrics, monthly temperature and precipitation, race/ethnicity, and chronic low-respiratory death rate, were all statistically significant. Of these, PWPD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWPD. Our results strongly support the idea that the loosening of "lock-down" orders should be tailored to the local PWPD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present.
Riley Pete, Riley Allison, Turtle James, Ben-Nun Michal

2022

General

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update.

In Nucleic acids research ; h5-index 217.0
Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.
**

2022-Apr-21

General

An open-source framework for fast-yet-accurate calculation of quantum mechanical features.

In Physical chemistry chemical physics : PCCP
We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities, the predictive power of the presented method competes against second-order perturbation theory in a converged atomic-orbital basis set at a fraction of its computational costs. The calculation of isotropic molecular polarizabilities is robust for a data set of more than 80 000 molecules. We present furthermore a generally applicable van der Waals radius model that is rooted on atomic static polarizabilites. Efficiency tests show that such radii can even be calculated for small- to medium-size proteins where the largest system (SARS-CoV-2 spike protein) has 42 539 atoms. Following the work of Domingo-Alemenara et al. [Domingo-Alemenara et al., Nat. Commun., 2019, 10, 5811], we present computational predictions for retention times for different chromatographic methods and describe how physicochemical features improve the predictive power of machine-learning models that otherwise only rely on two-dimensional features like molecular fingerprints. Additionally, we developed an internal benchmark set of experimental super-critical fluid chromatography retention times. For those methods, improvements of up to 10.6% are obtained when combining molecular fingerprints with physicochemical descriptors. Shapley additive explanation values show furthermore that the physical nature of the applied features can be retained within the final machine-learning models. We generally recommend the kallisto framework as a robust, low-cost, and physically motivated featurizer for upcoming state-of-the-art machine-learning studies.
Caldeweyher Eike, Bauer Christoph, Tehrani Ali Soltani

2022-Apr-21

General

Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification.

In Journal of computational biology : a journal of computational molecular cell biology
A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features. The optimal feature subset contains all useful and relevant features and excludes any irrelevant feature that allows machine learning models to understand better and differentiate efficiently the patterns in data sets. In this article, we propose a binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset. Concretely, the brain storm optimization algorithm is hybridized by the firefly algorithm and adopted as a wrapper method for feature selection problems on classification data sets. The proposed algorithm is evaluated on 21 data sets and compared with 11 metaheuristic algorithms. In addition, the proposed method is adopted for the coronavirus disease data set. The obtained experimental results substantiate the robustness of the proposed hybrid algorithm. It efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.
Bezdan Timea, Zivkovic Miodrag, Bacanin Nebojsa, Chhabra Amit, Suresh Muthusamy

2022-Apr-19

brain storm optimization algorithm, feature selection and classification, optimization, swarm intelligence

General

MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data.

In International journal of computer assisted radiology and surgery

PURPOSE : Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly.

METHODS : In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results.

RESULTS : The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor.

CONCLUSION : In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.

Han Jiahao, Xiao Ning, Yang Wanting, Luo Shichao, Zhao Jun, Qiang Yan, Chaudhary Suman, Zhao Juanjuan

2022-Apr-20

Attention mechanism, Clinical data, Deep learning, Disease-specific survival prediction, Longitudinal CT images

General

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito.

In Frontiers in big data
Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO₂, PM_2.5, SO₂, and O₃) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: -48.75%, for CO, -45.76%, for SO₂, -42.17%, for PM_2.5, and -63.98%, for NO₂. The reduction of this latter gas has induced an increase of O₃ by +26.54%.
Chau Phuong N, Zalakeviciute Rasa, Thomas Ilias, Rybarczyk Yves

2022

COVID-19, air pollution, data-driven modeling and optimization, deep learning - artificial neural network (DL-ANN), machine learning

General

Speech as a Biomarker for COVID-19 Detection Using Machine Learning.

In Computational intelligence and neuroscience
The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as "asymptomatic" COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the "recall" metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.
Usman Mohammed, Gunjan Vinit Kumar, Wajid Mohd, Zubair Mohammed, Siddiquee Kazy Noor-E-Alam

2022

General

The Evolution and Biology of SARS-CoV-2 Variants.

In Cold Spring Harbor perspectives in medicine
Our understanding of the still unfolding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic would have been extremely limited without the study of the genetics and evolution of this new human coronavirus. Large-scale genome-sequencing efforts have provided close to real-time tracking of the global spread and diversification of SARS-CoV-2 since its entry into the human population in late 2019. These data have underpinned analysis of its origins, epidemiology, and adaptations to the human population: principally immune evasion and increasing transmissibility. SARS-CoV-2, despite being a new human pathogen, was highly capable of human-to-human transmission. During its rapid spread in humans, SARS-CoV-2 has evolved independent new forms, the so-called "variants of concern," that are better optimized for human-to-human transmission. The most important adaptation of the bat coronavirus progenitor of both SARS-CoV-1 and SARS-CoV-2 for human infection (and other mammals) is the use of the angiotensin-converting enzyme 2 (ACE2) receptor. Relaxed structural constraints provide plasticity to SARS-related coronavirus spike protein permitting it to accommodate significant amino acid replacements of antigenic consequence without compromising the ability to bind to ACE2. Although the bulk of research has justifiably concentrated on the viral spike protein as the main determinant of antigenic evolution and changes in transmissibility, there is accumulating evidence for the contribution of other regions of the viral proteome to virus-host interaction. Whereas levels of community transmission of recombinants compromising genetically distinct variants are at present low, when divergent variants cocirculate, recombination between SARS-CoV-2 clades is being detected, increasing the risk that viruses with new properties emerge. Applying computational and machine learning methods to genome sequence data sets to generate experimentally verifiable predictions will serve as an early warning system for novel variant surveillance and will be important in future vaccine planning. Omicron, the latest SARS-CoV-2 variant of concern, has focused attention on step change antigenic events, "shift," as opposed to incremental "drift" changes in antigenicity. Both an increase in transmissibility and antigenic shift in Omicron led to it readily causing infections in the fully vaccinated and/or previously infected. Omicron's virulence, while reduced relative to the variant of concern it replaced, Delta, is very much premised on the past immune exposure of individuals with a clear signal that boosted vaccination protects from severe disease. Currently, SARS-CoV-2 has proven itself to be a dangerous new human respiratory pathogen with an unpredictable evolutionary capacity, leading to a risk of future variants too great not to ensure all regions of the world are screened by viral genome sequencing, protected through available and affordable vaccines, and have non-punitive strategies in place for detecting and responding to novel variants of concern.
Telenti Amalio, Hodcroft Emma B, Robertson David L

2022-Apr-20

General

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

In Briefings in bioinformatics
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
Yang Yanqing, Zhou Deshan, Zhang Xinben, Shi Yulong, Han Jiaxin, Zhou Liping, Wu Leyun, Ma Minfei, Li Jintian, Peng Shaoliang, Xu Zhijian, Zhu Weiliang

2022-Apr-21

COVID-19, D3AI-CoV, deep learning, target prediction, virtual screening

Public Health

Satisfaction with the walking-related environment during COVID-19 in South Korea.

In PloS one ; h5-index 176.0
This study aimed to examine the satisfaction level differences between urban and rural areas with regard to their walking environment during the COVID-19 pandemic in South Korea. This online cross-sectional research was conducted using a mobile health application. Overall, 1,032 local residents who participated in the mobile healthcare program of a public health center were classified as being from either urban (n = 481, 46.6%) or rural areas (n = 551, 53.4%) for the purpose of this study. The Walkability Checklist, which includes sociodemographic information, was employed using a Chi-square test and a multivariate logistic regression to investigate whether or not the participants were satisfied with the environmental factors associated with walking. It was found that both urban and rural areas were more likely to be unsatisfied with walking comfort (adjusted OR: 24.472, 95% CI: 14.937-40.096). Regarding the walking comfort aspects of the walking environment, urban residents chose poor landscape ("needed more grass, flowers, or trees"; aOR: 13.561, 95% CI: 3.619-50.823) as their primary dissatisfaction, and rural residents chose messy streets ("dirty, lots of litter or trash"; aOR: 29.045, 95% CI: 6.202-136.015). Compared with urban residents, rural residents were more discontented with the walking environment. Thus, to promote walking activities at the community level, it is necessary to focus on walking comfort, and implement efforts related to environmental beautification.
Jo Hoon, Lee Ho Hee, Kim Dong-Hyun, Kong In Deok

2022

General

When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?

In Advanced theory and simulations
The COVID-19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent-based models (ABMs) for COVID-19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta-models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root-mean-square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model; 0.07 vs 0.06 in another). However, meta-models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta-models can be used in some scenarios to assist in faster decision-making.
Lutz Christopher B, Giabbanelli Philippe J

2022-Feb

COVID‐19, agent‐based models, machine learning, meta‐modeling, surrogate model

General

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.

In Informatics in medicine unlocked
The quick spread of the COVID-19 virus worldwide turns it into a global pandemic. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients' need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.
Alabbad Dina A, Almuhaideb Abdullah M, Alsunaidi Shikah J, Alqudaihi Kawther S, Alamoudi Fatimah A, Alhobaishi Maha K, Alaqeel Naimah A, Alshahrani Mohammed S

2022-Apr-14

Coronavirus disease 2019 (COVID-19), Intensive care unit (ICU), Length of stay (LoS), Machine learning (ML), Predation, Resource management

General

Think positive: An interpretable neural network for image recognition.

In Neural networks : the official journal of the International Neural Network Society
The COVID-19 pandemic is an ongoing pandemic and is placing additional burden on healthcare systems around the world. Timely and effectively detecting the virus can help to reduce the spread of the disease. Although, RT-PCR is still a gold standard for COVID-19 testing, deep learning models to identify the virus from medical images can also be helpful in certain circumstances. In particular, in situations when patients undergo routine X-rays and/or CT-scans tests but within a few days of such tests they develop respiratory complications. Deep learning models can also be used for pre-screening prior to RT-PCR testing. However, the transparency/interpretability of the reasoning process of predictions made by such deep learning models is essential. In this paper, we propose an interpretable deep learning model that uses positive reasoning process to make predictions. We trained and tested our model over the dataset of chest CT-scan images of COVID-19 patients, normal people and pneumonia patients. Our model gives the accuracy, precision, recall and F-score equal to 99.48%, 0.99, 0.99 and 0.99, respectively.
Singh Gurmail

2022-Apr-04

COVID-19, CT-scan, Interpretable, Pneumonia, Prototypes

General

Development and Applications of Interoperable Biomedical Ontologies for Integrative Data and Knowledge Representation and Multiscale Modeling in Systems Medicine.

In Methods in molecular biology (Clifton, N.J.)
The data FAIR Guiding Principles state that all data should be Findable, Accessible, Interoperable, and Reusable. Ontology is critical to data integration, sharing, and analysis. Given thousands of ontologies have been developed in the era of artificial intelligence, it is critical to have interoperable ontologies to support standardized data and knowledge presentation and reasoning. For interoperable ontology development, the eXtensible ontology development (XOD) strategy offers four principles including ontology term reuse, semantic alignment, ontology design pattern usage, and community extensibility. Many software programs are available to help implement these principles. As a demonstration, the XOD strategy is applied to developing the interoperable Coronavirus Infectious Disease Ontology (CIDO). Various applications of interoperable ontologies, such as COVID-19 and kidney precision medicine research, are also introduced in this chapter.
He Yongqun

2022

COVID-19, Interoperable ontology, Kidney, Precision medicine, eXtensible ontology development

General

Predicting binding affinities of emerging variants of SARS-CoV-2 using spike protein sequencing data: observations, caveats and recommendations.

In Briefings in bioinformatics
Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein-protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance.
Zhang Ruibo, Ghosh Souparno, Pal Ranadip

2022-Apr-18

COVID-19, biological sequence analysis, machine learning, performance evaluation, protein–protein interaction, topological regression

General

Intermedia Agenda Setting amid the Pandemic: A Computational Analysis of China's Online News.

In Computational intelligence and neuroscience
Based on Intermedia Agenda Setting (IAS), the current study examines how official media and semi-privatized commercial media on the Weibo platform covered the COVID-19 pandemic in China. Both supervised machine learning and time series analysis were employed to analyze 350,059 Weibo posts released by 3,883 news sources between December 2019 and April 2020. Our results indicated that, in this nonwestern state-regulated China media environment, official and semi-privatized commercial media had a significant reciprocal relationship in news coverage. Both of them focused on "treatment on patients," "work resumption," and "propaganda and mobilization." Importantly, this paper sheds light on the value of the fine-grained level of agenda in IAS research. Using a fine-grained analysis, we separately investigated the effects of official and semi-privatized commercial media on predicting the pandemic prevalence, referring to the number of confirmed cases reported in real time. Implications and future directions were further discussed.
Wang Hanxiao, Shi Jian

2022

General

Cross-border mobility responses to COVID-19 in Europe: new evidence from facebook data.

In Globalization and health

BACKGROUND : Assessing the impact of government responses to Covid-19 is crucial to contain the pandemic and improve preparedness for future crises. We investigate here the impact of non-pharmaceutical interventions (NPIs) and infection threats on the daily evolution of cross-border movements of people during the Covid-19 pandemic. We use a unique database on Facebook users' mobility, and rely on regression and machine learning models to identify the role of infection threats and containment policies. Permutation techniques allow us to compare the impact and predictive power of these two categories of variables.

RESULTS : In contrast with studies on within-border mobility, our models point to a stronger importance of containment policies in explaining changes in cross-border traffic as compared with international travel bans and fears of being infected. The latter are proxied by the numbers of Covid-19 cases and deaths at destination. Although the ranking among coercive policies varies across modelling techniques, containment measures in the destination country (such as cancelling of events, restrictions on internal movements and public gatherings), and school closures in the origin country (influencing parental leaves) have the strongest impacts on cross-border movements.

CONCLUSION : While descriptive in nature, our findings have policy-relevant implications. Cross-border movements of people predominantly consist of labor commuting flows and business travels. These economic and essential flows are marginally influenced by the fear of infection and international travel bans. They are mostly governed by the stringency of internal containment policies and the ability to travel.

Docquier Fredérić, Golenvaux Nicolas, Nijssen Siegfried, Schaus Pierre, Stips Felix

2022-Apr-18

Containment policies, Covid-19, Cross-border mobility, Non-Parmaceutical interventions

General

Autophagy and evasion of immune system by SARS-CoV-2. Structural features of the Non-structural protein 6 from Wild Type and Omicron viral strains interacting with a model lipid bilayer.

bioRxiv Preprint
The viral cycle of SARS-CoV-2 is based on a complex interplay with the cellular machinery, which is mediated by specific proteins eluding or hijacking the cellular defense mechanisms. Among the complex pathways called by the viral infection autophagy is particularly crucial and is strongly influenced by the action of the non-structural protein 6 (Nsp6) interacting with the endoplasmic reticulum membrane. Importantly, differently from other non-structural proteins Nsp6 is mutated in the recently emerged Omicron variant, suggesting a possible different role of autophagy. In this contribution we explore, for the first time, the structural property of Nsp6 thanks to long-time scale molecular dynamic simulations and machine learning analysis, identifying the interaction patterns with the lipid membrane. We also show how the mutation brought by the Omicron variant may indeed modify some of the specific interactions, and more particularly help anchoring the viral protein to the lipid bilayer interface.
Bignon, E.; Marazzi, M.; Grandemange, S.; Monari, A.

2022-04-20

General

Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus.

In Soft computing
This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the virus in normal life with and without precaution. It is a data-driven parametric dependent function, for which the parameters are extracted from the data and the exponential function derived using multiplicative calculus. The results of the proposed model are compared to real recorded data from different countries and the performance of this model is investigated using error analysis theory. We stress that all statistics, collected data, etc., included in this study were extracted from official website of the World Health Organization (WHO). Therefore, the obtained results demonstrate its applicability and efficiency.
Bilgehan Bülent, Özyapıcı Ali, Hammouch Zakia, Gurefe Yusuf

2022-Apr-09

COVID-19 model, Multiplicative data fitting, Multiplicative least square method, Simulation

General

Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation.

In Soft computing
In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation.
Drias Habiba, Drias Yassine, Houacine Naila Aziza, Bendimerad Lydia Sonia, Zouache Djaafar, Khennak Ilyes

2022-Apr-08

Deep self learning AOA, Deep self learning EHO, Emergency transportation, Quantum machine learning, Quantum ordering points to identify the clustering structure

General

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning.

In Multimedia tools and applications
One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.
Haghanifar Arman, Majdabadi Mahdiyar Molahasani, Choi Younhee, Deivalakshmi S, Ko Seokbum

2022-Apr-07

COVID-19, CheXNet, Chest X-ray, Convolutional neural networks, Imaging features

General

A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms.

In Multimedia tools and applications
Depression has become a global concern, and COVID-19 also has caused a big surge in its incidence. Broadly, there are two primary methods of detecting depression: Task-based and Mobile Crowd Sensing (MCS) based methods. These two approaches, when integrated, can complement each other. This paper proposes a novel approach for depression detection that combines real-time MCS and task-based mechanisms. We aim to design an end-to-end machine learning pipeline, which involves multimodal data collection, feature extraction, feature selection, fusion, and classification to distinguish between depressed and non-depressed subjects. For this purpose, we created a real-world dataset of depressed and non-depressed subjects. We experimented with: various features from multi-modalities, feature selection techniques, fused features, and machine learning classifiers such as Logistic Regression, Support Vector Machines (SVM), etc. for classification. Our findings suggest that combining features from multiple modalities perform better than any single data modality, and the best classification accuracy is achieved when features from all three data modalities are fused. Feature selection method based on Pearson's correlation coefficients improved the accuracy in comparison with other methods. Also, SVM yielded the best accuracy of 86%. Our proposed approach was also applied on benchmarking dataset, and results demonstrated that the multimodal approach is advantageous in performance with state-of-the-art depression recognition techniques.
Thati Ravi Prasad, Dhadwal Abhishek Singh, Kumar Praveen, P Sainaba

2022-Apr-11

Depression detection, Emotion elicitation, Machine learning, Mobile crowd sensing, Multi-modal, Speech elicitation

General

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.

In Gondwana research : international geoscience journal
The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.
Wathore Roshan, Rawlekar Samyak, Anjum Saima, Gupta Ankit, Bherwani Hemant, Labhasetwar Nitin, Kumar Rakesh

2022-Apr-08

COVID-19, Deep Learning, LSTM. Multivariate time series forecasting, SARS-CoV-2

General

An embedded toolset for human activity monitoring in critical environments.

In Expert systems with applications
In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.
Di Benedetto Marco, Carrara Fabio, Ciampi Luca, Falchi Fabrizio, Gennaro Claudio, Amato Giuseppe

2022-Aug-01

Computer vision, Counting, Deep learning, Embedded system, Homography, Machine learning, Personal protective equipment

General

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images.

In Applied intelligence (Dordrecht, Netherlands)
COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT-PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images.
Zheng BingBing, Zhu Yu, Shi Qin, Yang Dawei, Shao Yanmei, Xu Tao

2022-Apr-09

Attention, COVID-19, Computer-aided diagnosis, Deep learning, Mutex attention network

General

Why was this cited? Explainable machine learning applied to COVID-19 research literature.

In Scientometrics
Multiple studies have investigated bibliometric factors predictive of the citation count a research article will receive. In this article, we go beyond bibliometric data by using a range of machine learning techniques to find patterns predictive of citation count using both article content and available metadata. As the input collection, we use the CORD-19 corpus containing research articles-mostly from biology and medicine-applicable to the COVID-19 crisis. Our study employs a combination of state-of-the-art machine learning techniques for text understanding, including embeddings-based language model BERT, several systems for detection and semantic expansion of entities: ConceptNet, Pubtator and ScispaCy. To interpret the resulting models, we use several explanation algorithms: random forest feature importance, LIME, and Shapley values. We compare the performance and comprehensibility of models obtained by "black-box" machine learning algorithms (neural networks and random forests) with models built with rule learning (CORELS, CBA), which are intrinsically explainable. Multiple rules were discovered, which referred to biomedical entities of potential interest. Of the rules with the highest lift measure, several rules pointed to dipeptidyl peptidase4 (DPP4), a known MERS-CoV receptor and a critical determinant of camel to human transmission of the camel coronavirus (MERS-CoV). Some other interesting patterns related to the type of animal investigated were found. Articles referring to bats and camels tend to draw citations, while articles referring to most other animal species related to coronavirus are lowly cited. Bat coronavirus is the only other virus from a non-human species in the betaB clade along with the SARS-CoV and SARS-CoV-2 viruses. MERS-CoV is in a sister betaC clade, also close to human SARS coronaviruses. Thus both species linked to high citation counts harbor coronaviruses which are more phylogenetically similar to human SARS viruses. On the other hand, feline (FIPV, FCOV) and canine coronaviruses (CCOV) are in the alpha coronavirus clade and more distant from the betaB clade with human SARS viruses. Other results include detection of apparent citation bias favouring authors with western sounding names. Equal performance of TF-IDF weights and binary word incidence matrix was observed, with the latter resulting in better interpretability. The best predictive performance was obtained with a "black-box" method-neural network. The rule-based models led to most insights, especially when coupled with text representation using semantic entity detection methods. Follow-up work should focus on the analysis of citation patterns in the context of phylogenetic trees, as well on patterns referring to DPP4, which is currently considered as a SARS-Cov-2 therapeutic target.
Beranová Lucie, Joachimiak Marcin P, Kliegr Tomáš, Rabby Gollam, Sklenák Vilém

2022-Apr-09

Bibliometry, CORD-19: COVID-19 open research dataset, Citation prediction, Interpretability, Phylogenetic distance, SARS-CoV-2, Text analysis, Virus clades

Pathology

When Is Partially Observable Reinforcement Learning Not Scary?

ArXiv Preprint
Applications of Reinforcement Learning (RL), in which agents learn to make a sequence of decisions despite lacking complete information about the latent states of the controlled system, that is, they act under partial observability of the states, are ubiquitous. Partially observable RL can be notoriously difficult -- well-known information-theoretic results show that learning partially observable Markov decision processes (POMDPs) requires an exponential number of samples in the worst case. Yet, this does not rule out the existence of large subclasses of POMDPs over which learning is tractable. In this paper we identify such a subclass, which we call weakly revealing POMDPs. This family rules out the pathological instances of POMDPs where observations are uninformative to a degree that makes learning hard. We prove that for weakly revealing POMDPs, a simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) is sufficient to guarantee polynomial sample complexity. To the best of our knowledge, this is the first provably sample-efficient result for learning from interactions in overcomplete POMDPs, where the number of latent states can be larger than the number of observations.
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin

2022-04-19

Public Health

Innate immune suppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs.

In Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
The mRNA SARS-CoV-2 vaccines were brought to market in response to the public health crises of Covid-19. The utilization of mRNA vaccines in the context of infectious disease has no precedent. The many alterations in the vaccine mRNA hide the mRNA from cellular defenses and promote a longer biological half-life and high production of spike protein. However, the immune response to the vaccine is very different from that to a SARS-CoV-2 infection. In this paper, we present evidence that vaccination induces a profound impairment in type I interferon signaling, which has diverse adverse consequences to human health. Immune cells that have taken up the vaccine nanoparticles release into circulation large numbers of exosomes containing spike protein along with critical microRNAs that induce a signaling response in recipient cells at distant sites. We also identify potential profound disturbances in regulatory control of protein synthesis and cancer surveillance. These disturbances potentially have a causal link to neurodegenerative disease, myocarditis, immune thrombocytopenia, Bell's palsy, liver disease, impaired adaptive immunity, impaired DNA damage response and tumorigenesis. We show evidence from the VAERS database supporting our hypothesis. We believe a comprehensive risk/benefit assessment of the mRNA vaccines questions them as positive contributors to public health.
Seneff Stephanie, Nigh Greg, Kyriakopoulos Anthony M, McCullough Peter A

2022-Apr-15

Cancer, Exosomes, G-quadruplexes, SARS-CoV-2 mRNA vaccines, Type I interferon Response, microRNAs

Ophthalmology

Cytomegalovirus Retinitis Screening Using Machine Learning Technology.

In Retina (Philadelphia, Pa.)

PROPOSE : A screening protocol for cytomegalovirus retinitis (CMVR) by fundus photography was generated and the diagnostic accuracy of machine learning technology for CMVR screening in HIV patients was investigated.

METHODS : One hundred and sixty-five eyes of ninety HIV positive patients were enrolled and evaluated for CMVR with binocular indirect ophthalmoscopy. Then, a single central field of the fundus image was recorded from each eye. All images were then interpreted by both machine learning models, generated by using the Keras application, and by a third-year ophthalmology resident. Diagnostic performance of CMVR screening using a machine learning model and the third-year ophthalmology resident were analyzed and compared.

RESULTS : Machine learning model, Keras application (VGG16) provided 68.8% (95%CI=50-83.9%) sensitivity and 100% (95%CI= 97.2-100%) specificity. The program provided accuracy of 93.94%. While the sensitivity and specificity for the third-year ophthalmology grading were 67.7% (95%CI=48.6-83.3%) and 98.4% (95%CI=94.5-99.8%). The accuracy for CMVR classification was 89.70%. When considering for sight threatening retinitis in zone 1 and excluded zone 2 and 3, the machine learning model provided high sensitivity of 88.2% (95%CI=63.6-98.5%) and high specificity of 100% (95%CI= 97.2-100%).

CONCLUSIONS : This study demonstrated the benefit of the machine learning model VGG16 which provided high sensitivity and specificity for detecting sight threatening CMVR in HIV positive patients. This model is a useful tool for ophthalmologists in clinical practice for preventing blindness from CMVR, especially during the COVID-19 pandemic.

Srisuriyajan Pitchapa, Cheewaruangroj Nontawat, Polpinit Pattarawit, Laovirojjanakul Wipada

2022-Apr-11

Radiology

The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system.

In iScience
The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.
Han Xiaoyang, Yu Ziqi, Zhuo Yaoyao, Zhao Botao, Ren Yan, Lamm Lorenz, Xue Xiangyang, Feng Jianfeng, Marr Carsten, Shan Fei, Peng Tingying, Zhang Xiao-Yong

2022-Apr-08

General

DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks.

In Digital finance

: Determining and minimizing risk exposure pose one of the biggest challenges in the financial industry as an environment with multiple factors that affect (non-)identified risks and the corresponding decisions. Various estimation metrics are utilized towards robust and efficient risk management frameworks, with the most prevalent among them being the Value at Risk (VaR). VaR is a valuable risk-assessment approach, which offers traders, investors, and financial institutions information regarding risk estimations and potential investment insights. VaR has been adopted by the financial industry for decades, but the generated predictions lack efficiency in times of economic turmoil such as the 2008 global financial crisis and the COVID-19 pandemic, which in turn affects the respective decisions. To address this challenge, a variety of well-established variations of VaR models are exploited by the financial community, including data-driven and data analytics models. In this context, this paper introduces a probabilistic deep learning approach, leveraging time-series forecasting techniques with high potential of monitoring the risk of a given portfolio in a quite efficient way. The proposed approach has been evaluated and compared to the most prominent methods of VaR calculation, yielding promising results for VaR 99% for forex-based portfolios.

Supplementary Information** : The online version contains supplementary material available at 10.1007/s42521-022-00050-0.

Fatouros Georgios, Makridis Georgios, Kotios Dimitrios, Soldatos John, Filippakis Michael, Kyriazis Dimosthenis

2022-Apr-13

Finance, Forex, Probabilistic deep neural networks, Risk assessment, Time-series, VaR, VaR prediction

General

SEM-ANN-based approach to understanding students' academic-performance adoption of YouTube for learning during Covid.

In Heliyon
A hybrid analysis of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), through SmartPLS and SPSS software, as well as the importance-performance map analysis (IPMA) were used to examine the impact of YouTube videos content on Jordanian university students' behavioral intention regarding eLearning acceptance, in Jordan. According to the evaluation of both ANN and IPMA, performance expectancy was the most important and, theoretically, several explanations were provided by the suggested model regarding the impact of intention to adopt eLearning from Internet service determinants at a personal level. The findings coincide greatly with prior research indicating that users' behavioral intention to adopt eLearning is significantly affected by their performance expectancy and effort expectancy. The paper contributed to technology adoption e.g., YouTube in academia, especially in Jordan. Respondents showed a willingness to employ and adopt the new technology in their education. Finally, the findings were presented and discussed through the UTAUT and TAM frameworks.
Elareshi Mokhtar, Habes Mohammed, Youssef Enaam, Salloum Said A, Alfaisal Raghad, Ziani Abdulkarim

2022-Apr

Covid-19, Higher education, Jordan, Social media, TAM, YouTube, eLearning

General

Symptom-based analysis of COVID-19 cases using supervised machine learning approaches to improve medical decision-making.

In Informatics in medicine unlocked
The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients' recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors in improving health services to different groups of COVID-19 patients.
Ilbeigipou Sadegh, Albadvi Amir

2022-Apr-12

Association rules mining, COVID-19, Confidence index, Coronavirus, Machine learning, Support index

Public Health

Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning Models.

In Bulletin of the Malaysian Mathematical Sciences Society
The COVID-19 (SARS-CoV-2 virus) pandemic has led to a substantial loss of human life worldwide by providing an unparalleled challenge to the public health system. The economic, psychological, and social disarray generated by the COVID-19 pandemic is devastating. Public health experts and epidemiologists worldwide are struggling to formulate policies on how to control this pandemic as there is no effective vaccine or treatment available which provide long-term immunity against different variants of COVID-19 and to eradicate this virus completely. As the new cases and fatalities are recorded daily or weekly, the responses are likely to be repeated or longitudinally correlated. Thus, studying the impact of available covariates and new cases on deaths from COVID-19 repeatedly would provide significant insights into this pandemic's dynamics. For a better understanding of the dynamics of spread, in this paper, we study the impact of various risk factors on the new cases and deaths over time. To do that, we propose a marginal-conditional based joint modelling approach to predict trajectories, which is crucial to the health policy planners for taking necessary measures. The conditional model is a natural choice to study the underlying property of dependence in consecutive new cases and deaths. Using this model, one can examine the relationship between outcomes and predictors, and it is possible to calculate risks of the sequence of events repeatedly. The advantage of repeated measures is that one can see how individual responses change over time. The predictive accuracy of the proposed model is also compared with various machine learning techniques. The machine learning algorithms used in this paper are extended to accommodate repeated responses. The performance of the proposed model is illustrated using COVID-19 data collected from the Texas Health and Human Services.
Chowdhury Rafiqul I, Hasan M Tariqul, Sneddon Gary

2022-Apr-13

Deep learning techniques, Joint modelling, Model accuracy, Repeated measures, SARS-CoV-2 virus

General

Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering

ArXiv Preprint
Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe. Remote sensing hyperspectral images encode rich structure that has been exploited for the detection of dieback disease in ash trees using supervised machine learning techniques. However, to understand the state of forest health at landscape-scale, accurate unsupervised approaches are needed. This article investigates the use of the unsupervised Diffusion and VCA-Assisted Image Segmentation (D-VIS) clustering algorithm for the detection of ash dieback disease in a forest site near Cambridge, United Kingdom. The unsupervised clustering presented in this work has high overlap with the supervised classification of previous work on this scene (overall accuracy = 71%). Thus, unsupervised learning may be used for the remote detection of ash dieback disease without the need for expert labeling.
Sam L. Polk, Aland H. Y. Chan, Kangning Cui, Robert J. Plemmons, David A. Coomes, James M. Murphy

2022-04-19

Pathology

Challenges of deep learning methods for COVID-19 detection using public datasets.

In Informatics in medicine unlocked
Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train-test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train-test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train-test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The dataset should have an independent test set; for a single machine or hospital source, have a more balanced set of images for all the prediction classes; and have a balanced dataset from several hospitals and demography. Our source codes and model are publicly available for the research community for further improvements.
Hasan Md Kamrul, Alam Md Ashraful, Dahal Lavsen, Roy Shidhartho, Wahid Sifat Redwan, Elahi Md Toufick E, Martí Robert, Khanal Bishesh

2022-Apr-12

COVID-19 disease, Chest computed tomography and X-ray, Convolutional neural networks, Ensemble classifier

General

Cerner real-world data (CRWD) - A de-identified multicenter electronic health records database.

In Data in brief
Cerner Real-World Data ^TM (CRWD) is a de-identified big data source of multicenter electronic health records. Cerner Corporation secured appropriate data use agreements and permissions from more than 100 health systems in the United States contributing to the database as of March 2022. A subset of the database was extracted to include data from only patients with SARS-CoV-2 infections and is referred to as the Cerner COVID-19 Dataset. The December 2021 version of CRWD consists of 100 million patients and 1.5 billion encounters across all care settings. There are 2.3 billion, 2.9 billion, 486 million, and 11.5 billion records in the condition, medication, procedure, and lab (laboratory test) tables respectively. The 2021 Q3 COVID-19 Dataset consists of 130.1 million encounters from 3.8 million patients. The size and longitudinal nature of CRWD can be leveraged for advanced analytics and artificial intelligence in medical research across all specialties and is a rich source of novel discoveries on a wide range of conditions including but not limited to COVID-19.
Ehwerhemuepha Louis, Carlson Kimberly, Moog Ryan, Bondurant Ben, Akridge Cheryl, Moreno Tatiana, Gasperino Gary, Feaster William

2022-Jun

COVID-19, Cerner Real-World DataTM(CRWD), Cerner learning Health NetworkSM (LHN), Electronic Health Records (EHR), HealtheDataLab™, HealtheIntent, SARS-CoV-2

Radiology

A convolutional neural network-based COVID-19 detection method using chest CT images.

In Annals of translational medicine

Background : High-throughput population screening for the novel coronavirus disease (COVID-19) is critical to controlling disease transmission. Convolutional neural networks (CNNs) are a cutting-edge technology in the field of computer vision and may prove more effective than humans in medical diagnosis based on computed tomography (CT) images. Chest CT images can show pulmonary abnormalities in patients with COVID-19.

Methods : In this study, CT image preprocessing are firstly performed using fuzzy c-means (FCM) algorithm to extracted the region of the pulmonary parenchyma. Through multiscale transformation, the preprocessed image is subjected to multi scale transformation and RGB (red, green, blue) space construction. After then, the performances of GoogLeNet and ResNet, as the most advanced CNN architectures, were compared in COVID-19 detection. In addition, transfer learning (TL) was employed to solve overfitting problems caused by limited CT samples. Finally, the performance of the models were evaluated and compared using the accuracy, recall rate, and F1 score.

Results : Our results showed that the ResNet-50 method based on TL (ResNet-50-TL) obtained the highest diagnostic accuracy, with a rate of 82.7% and a recall rate of 79.1% for COVID-19. These results showed that applying deep learning technology to COVID-19 screening based on chest CT images is a very promising approach. This study inspired us to work towards developing an automatic diagnostic system that can quickly and accurately screen large numbers of people with COVID-19.

Conclusions : We tested a deep learning algorithm to accurately detect COVID-19 and differentiate between healthy control samples, COVID-19 samples, and common pneumonia samples. We found that TL can significantly increase accuracy when the sample size is limited.

Cao Yi, Zhang Chen, Peng Cheng, Zhang Guangfeng, Sun Yi, Jiang Xiaoxue, Wang Zhan, Zhang Die, Wang Lifei, Liu Jikui

2022-Mar

Coronavirus disease 2019 (COVID-19), computed tomography (CT), convolutional neural network, transfer learning

Public Health

A Retrospective and Multicenter Study on COVID-19 in Inner Mongolia: Evaluating the Influence of Sampling Locations on Nucleic Acid Test and the Dynamics of Clinical and Prognostic Indexes.

In Frontiers in medicine
COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.
Yu Lan, Wang Ailan, Li Tianbao, Jin Wen, Tian Geng, Yun Chunmei, Gao Fei, Fan Xiuzhen, Wang Huimin, Zhang Huajun, Sun Dejun

2022

COVID-19, clinical characteristics, feces testing, machine learning, prognosis model

General

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images.

In Health information science and systems
The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.
Pan Liangrui, Ji Boya, Wang Hetian, Wang Lian, Liu Mingting, Chongcheawchamnan Mitchai, Peng Shaolaing

2022-Dec

COVID19, Chest X-ray, MFDNN, Multi-channel feature

General

Selective covalent targeting of SARS-CoV-2 main protease by enantiopure chlorofluoroacetamide.

In Chemical science
The coronavirus disease 2019 (COVID-19) pandemic has necessitated the development of antiviral agents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The main protease (M^pro) is a promising target for COVID-19 treatment. Here, we report an irreversible SARS-CoV-2 M^pro inhibitor possessing chlorofluoroacetamide (CFA) as a warhead for the covalent modification of M^pro. Ugi multicomponent reaction using chlorofluoroacetic acid enabled the rapid synthesis of dipeptidic CFA derivatives that identified 18 as a potent inhibitor of SARS-CoV-2 M^pro. Among the four stereoisomers, (R,R)-18 exhibited a markedly higher inhibitory activity against M^pro than the other isomers. Reaction kinetics and computational docking studies suggest that the R configuration of the CFA warhead is crucial for the rapid covalent inhibition of M^pro. Our findings highlight the prominent influence of the CFA chirality on the covalent modification of proteinous cysteines and provide the basis for improving the potency and selectivity of CFA-based covalent inhibitors.
Yamane Daiki, Onitsuka Satsuki, Re Suyong, Isogai Hikaru, Hamada Rui, Hiramoto Tadanari, Kawanishi Eiji, Mizuguchi Kenji, Shindo Naoya, Ojida Akio

2022-Mar-09

General

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud.

In Journal of healthcare engineering
COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
Mir Mahmood Hussain, Jamwal Sanjay, Mehbodniya Abolfazl, Garg Tanya, Iqbal Ummer, Samori Issah Abubakari

2022

Public Health

Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images.

In Biomedical signal processing and control
The widespread of highly infectious disease, i.e., COVID-19, raises serious concerns regarding public health, and poses significant threats to the economy and society. In this study, an efficient method based on deep learning, deep feature fusion classification network (DFFCNet), is proposed to improve the overall diagnosis accuracy of the disease. The method is divided into two modules, deep feature fusion module (DFFM) and multi-disease classification module (MDCM). DFFM combines the advantages of different networks for feature fusion and MDCM uses support vector machine (SVM) as a classifier to improve the classification performance. Meanwhile, the spatial attention (SA) module and the channel attention (CA) module are introduced into the network to improve the feature extraction capability of the network. In addition, the multiple-way data augmentation (MDA) is performed on the images of chest X-ray images (CXRs), to improve the diversity of samples. Similarly, the utilized Grad-CAM++ is to make the features more intuitive, and the deep learning model more interpretable. On testing of a collection of publicly available datasets, results from experimentation reveal that the proposed method achieves 99.89% accuracy in a triple classification of COVID-19, pneumonia, and health X-ray images, there by outperforming the eight state-of-the-art classification techniques.
Liu Jingyao, Sun Wanchun, Zhao Xuehua, Zhao Jiashi, Jiang Zhengang

2022-Jul

COVID-19, Classification, Deep learning, Feature fusion, SVM

General

IoMT-fog-cloud based architecture for Covid-19 detection.

In Biomedical signal processing and control

Limitations of available literature : Nowadays, coronavirus disease 2019 (COVID-19) is the world-wide pandemic due to its mutation over time. Several works done for covid-19 detection using different techniques however, the use of small datasets and the lack of validation tests still limit their works. Also, they depend only on the increasing the accuracy and the precision of the model without giving attention to their complexity which is one of the main conditions in the healthcare application. Moreover, the majority of healthcare applications with cloud computing use centralization transmission process of various and vast volumes of information what make the privacy and security of personal patient's data easy for hacking. Furthermore, the traditional architecture of the cloud showed many weaknesses such as the latency and the low persistent performance.

Method proposed by the author with technical information : In our system, we used Discrete Wavelet transform (DWT) and Principal Component Analysis (PCA) and different energy tracking methods such as Teager Kaiser Energy Operator (TKEO), Shannon Wavelet Entropy Energy (SWEE), Log Energy Entropy (LEE) for preprocessing the dataset. For the first step, DWT used to decompose the image into coefficients where each coefficient is vector of features. Then, we apply PCA for reduction the dimension by choosing the most essential features in features map. Moreover, we used TKEO, SHEE, LEE to track the energy in the features in order to select the best and the most optimal features to reduce the complexity of the model. Also, we used CNN model that contains convolution and pooling layers due to its efficacity in image processing. Furthermore, we depend on deep neurons using small kernel windows which provide better features learning and minimize the model's complexity.The used DWT-PCA technique with TKEO filtering technique showed great results in terms of noise measure where the Peak Signal-to-Noise Ratio (PSNR) was 3.14 dB and the Signal-to-Noise Ratio (SNR) of original and preprocessed image was 1.48, 1.47 respectively which guaranteed the performance of the filtering techniques.The experimental results of the CNN model ensure the high performance of the proposed system in classifying the covid-19, pneumonia and normal cases with 97% of accuracy, 100% of precession, 97% of recall, 99% of F1-score, and 98% of AUC.

Advantages and application of proposed method : The use of DWT-PCA and TKEO optimize the selection of the optimal features and reduce the complexity of the model.The proposed system achieves good results in identifying covid-19, pneumonia and normal cases.The implementation of fog computing as an intermediate layer to solve the latency problem and computational cost which improve the Quality of Service (QoS) of the cloud.Fog computing ensure the privacy and security of the patients' data.With further refinement and validation, the IFC-Covid system will be real-time and effective application for covid-19 detection, which is user friendly and costless.

Mohamed Akram Khelili, Sihem Slatnia, Okba Kazar, Harous Saad

2022-Jul

Cloud computing, Covid-19, Deep learning, Fog computing, Internet of Medical Things (IoMT), Quality of Service (QoS)

General

Gender Difference in Psychological, Cognitive, and Behavioral Patterns Among University Students During COVID-19: A Machine Learning Approach.

In Frontiers in psychology ; h5-index 92.0
The COVID-19 pandemic affects all population segments and is especially detrimental to university students because social interaction is critical for a rewarding campus life and valuable learning experiences. In particular, with the suspension of in-person activities and the adoption of virtual teaching modalities, university students face drastic changes in their physical activities, academic careers, and mental health. Our study applies a machine learning approach to explore the gender differences among U.S. university students in response to the global pandemic. Leveraging a proprietary survey dataset collected from 322 U.S. university students, we employ association rule mining (ARM) techniques to identify and compare psychological, cognitive, and behavioral patterns among male and female participants. To formulate our task under the conventional ARM framework, we model each unique question-answer pair of the survey questionnaire as a market basket item. Consequently, each participant's survey report is analogous to a customer's transaction on a collection of items. Our findings suggest that significant differences exist between the two gender groups in psychological distress and coping strategies. In addition, the two groups exhibit minor differences in cognitive patterns and consistent preventive behaviors. The identified gender differences could help professional institutions to facilitate customized advising or counseling for males and females in periods of unprecedented challenges.
Zhao Yijun, Ding Yi, Shen Yangqian, Liu Wei

2022

COVID-19, association rule mining, gender difference, mental health, university student

Oncology

Abnormal global alternative RNA splicing in COVID-19 patients.

In PLoS genetics ; h5-index 96.0
Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.
Wang Changli, Chen Lijun, Chen Yaobin, Jia Wenwen, Cai Xunhui, Liu Yufeng, Ji Fenghu, Xiong Peng, Liang Anyi, Liu Ren, Guan Yuanlin, Cheng Zhongyi, Weng Yejing, Wang Weixin, Duan Yaqi, Kuang Dong, Xu Sanpeng, Cai Hanghang, Xia Qin, Yang Dehua, Wang Ming-Wei, Yang Xiangping, Zhang Jianjun, Cheng Chao, Liu Liang, Liu Zhongmin, Liang Ren, Wang Guopin, Li Zhendong, Xia Han, Xia Tian

2022-Apr-14

Radiology

Using Artificial Intelligence to Improve the Diagnostic Efficiency of Pulmonologists in Differentiating COVID-19 Pneumonia from Community-Acquired Pneumonia.

In Journal of medical virology
Coronavirus Disease 2019 (COVID-19) has quickly turned into a global health problem. Computed Tomography (CT) findings of COVID-19 pneumonia and community-acquired pneumonia (CAP) may be similar. Artificial intelligence (AI) is a popular topic among medical imaging techniques and has caused significant developments in diagnostic techniques. This retrospective study aims to analyze the contribution of AI to the diagnostic performance of pulmonologists in distinguishing COVID-19 pneumonia from CAP using CT scans. A deep learning-based AI model was created to be utilized in the detection of COVID-19, which extracted visual data from volumetric CT scans. The final dataset covered a total of 2496 scans (887 patients), which included 1428 (57.2%) from the COVID-19 group and 1068 (42.8%) from the CAP group. CT slices were classified into training, validation, and test datasets in an 8:1:1. The independent test dataset was analyzed by comparing the performance of four pulmonologists in differentiating COVID-19 pneumonia both with and without the help of the AI. The accuracy, sensitivity, and specificity values of the proposed AI model for determining COVID-19 in the independent test dataset were 93.2%, 85.8%, and 99.3%, respectively, with the area under the ROC curve of 0.984. With the assistance of the AI, the pulmonologists accomplished a higher mean accuracy (88.9% vs 79.9%,p<0.001), sensitivity (79.1% vs 70%, p<0.001), and specificity (96.5% vs 87.5%,p<0.001). AI support significantly increases the diagnostic efficiency of pulmonologists in the diagnosis of COVID-19 via CT. Studies in the future should focus on real-time applications of AI to fight the COVID-19 infection. This article is protected by copyright. All rights reserved.
İn Erdal, Altıntop Geçkil Ayşegül, Kavuran Gürkan, Şahin Mahmut, Berber Nurcan Kırıcı, Kuluöztürk Mutlu

2022-Apr-13

Artificial Intelligence (AI), Community-Acquired Pneumonia, Computed Tomography (CT), Coronavirus Disease 2019 (COVID-19), Deep Learning

General

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework.

In Scientific reports ; h5-index 158.0
Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
Gunaratne Chathika, Reyes Rene, Hemberg Erik, O’Reilly Una-May

2022-Apr-13

Public Health

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data.

In BMC complementary medicine and therapies

BACKGROUND : Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integrative medicines (CAIMs) have been being touted as both preventative and curative. We conducted sentiment and emotion analysis with the intent of understanding CAIM content related to COVID-19 being generated on Twitter across 9 months.

METHODS : Tweets relating to CAIM and COVID-19 were extracted from the George Washington University Libraries Dataverse Coronavirus tweets dataset from March 03 to November 30, 2020. We trained and tested a machine learning classifier using a large, pre-labelled Twitter dataset, which was applied to predict the sentiment of each CAIM-related tweet, and we used a natural language processing package to identify the emotions based on the words contained in the tweets.

RESULTS : Our dataset included 28 713 English-language Tweets. The number of CAIM-related tweets during the study period peaked in May 2020, then dropped off sharply over the subsequent three months; the fewest CAIM-related tweets were collected during August 2020 and remained low for the remainder of the collection period. Most tweets (n = 15 612, 54%) were classified as positive, 31% were neutral (n = 8803) and 15% were classified as negative (n = 4298). The most frequent emotions expressed across tweets were trust, followed by fear, while surprise and disgust were the least frequent. Though volume of tweets decreased over the 9 months of the study, the expressed sentiments and emotions remained constant.

CONCLUSION : The results of this sentiment analysis enabled us to establish key CAIMs being discussed at the intersection of COVID-19 across a 9-month period on Twitter. Overall, the majority of our subset of tweets were positive, as were the emotions associated with the words found within them. This may be interpreted as public support for CAIM, however, further qualitative investigation is warranted. Such future directions may be used to combat misinformation and improve public health strategies surrounding the use of social media information.

Ng Jeremy Y, Abdelkader Wael, Lokker Cynthia

2022-Apr-13

COVID-19, Complementary and alternative medicine, Sentiment analysis, Social media, Twitter

General

COVID-19 breakthrough infections and hospitalizations among vaccinated patients with dementia in the United States between December 2020 and August 2021.

In Alzheimer's & dementia : the journal of the Alzheimer's Association

INTRODUCTION : There is lack of data on COVID-19 breakthrough infections in vaccinated patients with dementia in the United States.

METHODS : This is a retrospective cohort study of 262,847 vaccinated older adults (age 73.8 ± 6.81 years old) between December 2020 and August 2021.

RESULTS : Among the fully vaccinated patients with dementia, the overall risk of COVID-19 breakthrough infections ranged from 8.6% to 12.4%. Patients with dementia were at increased risk for breakthrough infections compared with patients without dementia, with the highest odds for patients with Lewy body dementia (LBD) (adjusted odds ratio or AOR: 3.06, 95% confidence interval or CI [1.45 to 6.66]), followed by vascular dementia (VD) (AOR: 1.99, 95% CI [1.42 to 2.80]), Alzheimer's disease (AD) (1.53, 95% CI [1.22 to 1.92]), and mild cognitive impairment (MCI) (AOR: 1.78, 95% CI [1.51 to 2.11]). The incidence rate of breakthrough infections among fully vaccinated patients with dementia increased since December 2020 and accelerated after May 2021. The overall risk for hospitalization after breakthrough infections in patients with dementia was 39.5% for AD, 46.2% for VD, and 30.4% for MCI.

DISCUSSION : These results highlight the need to continuously monitor breakthrough severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and outcomes in vaccinated patients with dementia.

Wang Lindsey, Davis Pamela B, Kaelber David C, Xu Rong

2022-Apr-13

“Alzheimers Disease”, COVID-19, Lewy body dementia, breakthrough infections, dementia, frontotemporal dementia, mild cognitive impairment, patient electronic health records, vaccine, vascular dementia

Public Health

Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna.

In Wiener klinische Wochenschrift

BACKGROUND : Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting.

METHODS : The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna's official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.

RESULTS : Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74.

CONCLUSION : This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.

Munsch Nicolas, Gruarin Stefanie, Nateqi Jama, Lutz Thomas, Binder Michael, Aberle Judith H, Martin Alistair, Knapp Bernhard

2022-Apr-13

Chatbot, Machine learning, Self-reported, Symptom assessment, Symptom checker

Pathology

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.

In Annals of biomedical engineering ; h5-index 52.0
Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray images.
Zouch Wassim, Sagga Dhouha, Echtioui Amira, Khemakhem Rafik, Ghorbel Mohamed, Mhiri Chokri, Hamida Ahmed Ben

2022-Apr-12

COVID-19, CT, Chest X-ray, Convolutional neural network, Deep learning

General

COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease.

In Scientific data
This paper introduces the COVID-19 Open Dataset (COD), available at goo.gle/covid-19-open-data . A static copy is of the dataset is also available at https://doi.org/10.6084/m9.figshare.c.5399355 . This is a very large "meta-dataset" of COVID-related data, containing epidemiological information, from 22,579 unique locations within 232 different countries and independent territories. For 62 of these countries we have state-level data, and for 23 of these countries we have county-level data. For 15 countries, COD includes cases and deaths stratified by age or sex. COD also contains information on hospitalizations, vaccinations, and other relevant factors such as mobility, non-pharmaceutical interventions and static demographic attributes. Each location is tagged with a unique identifier so that these different types of information can be easily combined. The data is automatically extracted from 121 different authoritative sources, using scalable open source software. This paper describes the format and construction of the dataset, and includes a preliminary statistical analysis of its content, revealing some interesting patterns.
Wahltinez Oscar, Cheung Aurora, Alcantara Ruth, Cheung Donny, Daswani Mayank, Erlinger Anthony, Lee Matt, Yawalkar Pranali, Lê Paula, Navarro Ofir Picazo, Brenner Michael P, Murphy Kevin

2022-Apr-12

General

Acoustery System for Differential Diagnosing of Coronavirus COVID-19 Disease.

In IEEE open journal of engineering in medicine and biology
Goal: Because of the outbreak of coronavirus infection, healthcare systems are faced with the lack of medical professionals. We present a system for the differential diagnosis of coronavirus disease, based on deep learning techniques, which can be implemented in clinics. Methods: A recurrent network with a convolutional neural network as an encoder and an attention mechanism is used. A database of about 3000 records of coughing was collected. The data was collected through the Acoustery mobile application in hospitals in Russia, Belarus, and Kazakhstan from April 2020 to October 2020. Results: The model classification accuracy reaches 85%. Values of precision and recall metrics are 78.5% and 73%. Conclusions: We reached satisfactory results in solving the problem. The proposed model is already being tested by doctors to understand the ways of improvement. Other architectures should be considered that use a larger training sample and all available patient information.
Mitrofanova Anastasia, Mikhaylov Dmitry, Shaznaev Ilman, Chumanskaia Vera, Saveliev Valeri

2021

Attention mechanism, COVID-19, convolutional neural network, preliminary diagnosis, recurrent neural network

General

A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19.

In Annals of medicine and surgery (2012)
The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used 'web of Science', 'Scopus' and 'goggle scholar' with the keywords of "SARS-CoV-2 detection" or "coronavirus 2019 detection" or "COVID 2019 detection" or "COVID 19 detection" "corona virus techniques for detection of COVID-19", "audio techniques for detection of COVID-19", "speech techniques for detection of COVID-19", for period of 2019-2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single "perfect" COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.
Anand Satyajit, Sharma Vikrant, Pourush Rajeev, Jaiswal Sandeep

2022-Apr

Artificial intelligence, Audio, COVID 19, Signal processing, Speech

General

Emerging early diagnostic methods for acute kidney injury.

In Theranostics
Many factors such as trauma and COVID-19 cause acute kidney injury (AKI). Late AKI have a very high incidence and mortality rate. Early diagnosis of AKI provides a critical therapeutic time window for AKI treatment to prevent progression to chronic renal failure. However, the current clinical detection based on creatinine and urine output isn't effective in diagnosing early AKI. In recent years, the early diagnosis of AKI has made great progress with the advancement of information technology, nanotechnology, and biomedicine. These emerging methods are mainly divided into two aspects: First, predicting AKI through models construct by machine learning; Second, early diagnosis of AKI through detection of newly-discovered early biomarkers. Currently, these methods have shown great potential and become an attractive tool for the early diagnosis of AKI. Therefore, it is very important to discuss and summarize these methods for the early diagnosis of AKI. In this review, we first systematically summarize the application of machine learning in AKI prediction algorithms and specific scenarios. In addition, we introduce the key role of early biomarkers in the progress of AKI, and then comprehensively summarize the application of emerging detection technologies for early AKI. Finally, we discuss current challenges and prospects of machine learning and biomarker detection. The review is expected to provide new insights for early diagnosis of AKI, and provided important inspiration for the design of early diagnosis of other major diseases.
Xiao Zuoxiu, Huang Qiong, Yang Yuqi, Liu Min, Chen Qiaohui, Huang Jia, Xiang Yuting, Long Xingyu, Zhao Tianjiao, Wang Xiaoyuan, Zhu Xiaoyu, Tu Shiqi, Ai Kelong

2022

Acute kidney injury, Early diagnosis., Machine learning, Neutrophil gelatinase-associated lipocalin, Reactive oxygen species and nitrogen species, kidney injury molecule-1, miRNA-21, γ-glutamyl transpeptidase

General

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.

In Computational intelligence and neuroscience
Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine.
Khan Irfan Ullah, Aslam Nida, Chrouf Sara, Atef Israa, Merah Ikram, AlMulhim Latifah, AlShuaifan Raghad

2022

General

Monitoring social-distance in wide areas during pandemics: a density map and segmentation approach.

In Applied intelligence (Dordrecht, Netherlands)
With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public spaces is of great importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by assessing social distancing in corridors up to small crowds by detecting each person individually, considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating social-distancing in wide areas, where important occlusions may be present. Our framework consists in the creation of new ground truth social distance labels, based on the ground truth density maps, and the proposal of two different solutions, a density-map-based and a segmentation-based, to detect crowds violating social-distancing constraints. We assess the results of both approaches by using the generated ground truth from the PET2009 and CityStreet datasets. We show that our framework performs well at providing the zones where people are not following the social-distance, even when heavily occluded or far away from the camera, compared to current detection and tracking approaches.
Gonzalez-Trejo Javier Antonio, Mercado-Ravell Diego A, Jaramillo-Avila Uziel

2022-Apr-05

COVID-19, Crowds monitoring, Deep learning, Density maps, Segmentation, Visual social distancing

General

Automated infrastructure: COVID-19 and the shifting geographies of supply chain capitalism.

In Progress in human geography
In recent years, geographers have evinced how infrastructure constitutes the bedrock of supply chain capitalism and its oppressions. This article interrogates how advanced automation - comprising robotics, artificial intelligence and software - is poised to politicize this infrastructural space further on the heels of the COVID-19 pandemic. Reflecting on COVID-19 developments, the article shows how logistics is turning to advanced automation to drive productivity outside labour, spur self-service consumption through digital technologies and contest labour's future. As automated infrastructure threatens to take hold, a configuration of exchange that increasingly places labour, but not profits, outside of capital's circulations will need to be challenged.
Lin Weiqiang

2022-Apr

COVID-19, automation, digital technologies, infrastructure, labour, logistics, supply chain capitalism

General

Expecting Individuals' Body Reaction to Covid-19 Based On Statistical Naïve Bayes Technique.

In Pattern recognition
Covid-19, what a strange, unpredictable mutated virus. It has baffled many scientists, as no firm rule has yet been reached to predict the effect that the virus can inflict on people if they are infected with it. Recently, many researches have been introduced for diagnosing Covid-19; however, none of them pay attention to predict the effect of the virus on the person's body if the infection occurs but before the infection really takes place. Predicting the extent to which people will be affected if they are infected with the virus allows for some drastic precautions to be taken for those who will suffer from serious complications, while allowing some freedom for those who expect not to be affected badly. This paper introduces Covid-19 Prudential Expectation Strategy (CPES) as a new strategy for predicting the behavior of the person's body if he has been infected with Covid-19. The CPES composes of three phases called Outlier Rejection Phase (ORP), Feature Selection Phase (FSP), and Classification Phase (CP). For enhancing the classification accuracy in CP, CPES employs two proposed techniques for outlier rejection in ORP and feature selection in FSP, which are called Hybrid Outlier Rejection (HOR) method and Improved Binary Genetic Algorithm (IBGA) method respectively. In ORP, HOR rejects outliers in the training data using a hybrid method that combines standard division and Binary Gray Wolf Optimization (BGWO) method. On the other hand, in FSP, IBGA as a hybrid method selects the most useful features for the prediction process. IBGA includes Fisher Score (F_Score) as a filter method to quickly select the features and BGA as a wrapper method to accurately select the features based on the average accuracy value from several classification models as a fitness function to guarantee the efficiency of the selected subset of features with any classifier. In CP, CPES has the ability to classify people based on their bodies' reaction to Covid-19 infection, which is built upon a proposed Statistical Naïve Bayes (SNB) classifier after performing the previous two phases. CPES has been compared against recent related strategies in terms of accuracy, error, recall, precision, and run-time using Covid-19 dataset [1].This dataset contains routine blood tests collected from people before and after their infection with covid-19 through a Web-based form created by us. CPES outperforms the competing methods in experimental results because it provides the best results with values of 0.87, 0.13, 0.84, and 0.79 for accuracy, error, precision, and recall.
Rabie Asmaa H, Mansour Nehal A, Saleh Ahmed I, Takieldeen Ali E

2022-Apr-06

Covid-19, Naïve Bayes, Prediction, Prudential Expectation

General

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network.

In Computers & electrical engineering : an international journal
The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.
Mohammedqasem Roa’a, Mohammedqasim Hayder, Ata Oguz

2022-May

ANN, Artificial Neural Network, AUC, Area Under Curve, CNN, Convolutional Neural Network, COVID-19, COVID-19, Coronavirus disease, DL, Deep learning, Imbalanced Dataset, Internet of Things, IoT, Internet of Things, ML, Machine learning, RFE, Recursive Feature Elimination, RNN, Recurrent Neural Network, Recursive feature elimination, SMOTE, Synthetic Minority Oversampling Technique, Synthetic minority oversampling technique

Public Health

COVID-19 personal health mention detection from tweets using dual convolutional neural network.

In Expert systems with applications
Twitter offers extensive and valuable information on the spread of COVID-19 and the current state of public health. Mining tweets could be an important supplement for public health departments in monitoring the status of COVID-19 in a timely manner and taking the appropriate actions to minimize its impact. Identifying personal health mentions (PHM) is the first step of social media public health surveillance. It aims to identify whether a person's health condition is mentioned in a tweet, and it serves as a crucial method in tracking pandemic conditions in real time. However, social media texts contain noise, many creative and novel phrases, sarcastic emoji expressions, and misspellings. In addition, the class imbalance issue is usually very serious. To address these challenges, we built a COVID-19 PHM dataset containing more than 11,000 annotated tweets, and we proposed a dual convolutional neural network (CNN) framework using this dataset. An auxiliary CNN in the dual CNN structure provides supplemental information for the primary CNN in order to detect PHMs from tweets more effectively. The experiment shows that the proposed structure could alleviate the effect of class imbalance and could achieve promising results. This automated approach could monitor public health in real time and save disease-prevention departments from the tedious manual work in public health surveillance.
Luo Linkai, Wang Yue, Liu Hai

2022-Aug-15

CNN, Deep learning, Health monitoring, Social media, Text mining

General

The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets

bioRxiv Preprint
Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of RNA known for their role in guiding ribosomal RNA 2'-O-ribose methylation through base pairing with targeted sequences. Recently, C/D snoRNAs were also implicated in regulating the expression of non-ribosomal genes through different modes of binding. Large scale RNA-RNA interaction datasets detect many snoRNAs binding messenger RNA. However, these studies provide a narrow portrait of snoRNA targets forming under specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA machine learning interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, and position and sequence of the interactions, enabling it to outperform existing predictors. Interestingly, for specific snoRNAs, snoGloBe identifies strong enrichment of interactions near gene expression regulatory elements including splice sites. Abundance and splicing of predicted targets were altered upon the knockdown of their associated snoRNA. Strikingly, the predicted snoRNA interactions often overlap with the binding sites of functionally related RNA binding proteins, reinforcing their role in gene expression regulation. The interactions of snoRNAs are not randomly distributed but often accumulate in functionally related transcripts sharing common regulatory elements suggesting coordinated regulatory function. The wide scope of snoGloBe makes it an excellent tool for discovering viral RNA targets, which is evident from its capacity to identify snoRNAs targeting SARS-CoV-2 RNA, known to be heavily methylated. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function.
Deschamps-Francoeur, G.; Couture, S.; Abou Elela, S.; Scott, M. S.

2022-04-12

Radiology

Reduced Chest Computed Tomography Scan Length for Patients Positive for Coronavirus Disease 2019: Dose Reduction and Impact on Diagnostic Utility.

In Journal of computer assisted tomography

METHODS : This study used the Personalized Rapid Estimation of Dose in CT (PREDICT) tool to estimate patient-specific organ doses from CT image data. The PREDICT is a research tool that combines a linear Boltzmann transport equation solver for radiation dose map generation with deep learning algorithms for organ contouring. Computed tomography images from 74 subjects in the Medical Imaging Data Resource Center-RSNA International COVID-19 Open Radiology Database data set (chest CT of adult patients positive for COVID-19), which included expert annotations including "infectious opacities," were analyzed. First, the full z-scan length of the CT image data set was evaluated. Next, the z-scan length was reduced from the left hemidiaphragm to the top of the aortic arch. Generic dose reduction based on dose length product (DLP) and patient-specific organ dose reductions were calculated. The percentage of infectious opacities excluded from the reduced z-scan length was used to quantify the effect on diagnostic utility.

RESULTS : Generic dose reduction, based on DLP, was 69%. The organ dose reduction ranged from approximately equal to 18% (breasts) to approximately equal to 64% (bone surface and bone marrow). On average, 12.4% of the infectious opacities were not included in the reduced z-coverage, per patient, of which 5.1% were above the top of the arch and 7.5% below the left hemidiaphragm.

CONCLUSIONS : Limiting z-scan length of chest CTs reduced radiation dose without significantly compromising diagnostic utility in COVID-19 patients. The PREDICT demonstrated that patient-specific organ dose reductions varied from generic dose reduction based on DLP.

Principi Sara, O’Connor Stacy, Frank Luba, Schmidt Taly Gilat

2022-Apr-08

Radiology

Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study.

In Journal of computer assisted tomography

OBJECTIVE : We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images.

METHODS : This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115).

RESULTS : In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035).

CONCLUSIONS : Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

Yoo Seung-Jin, Qi Xiaolong, Inui Shohei, Kim Hyungjin, Jeong Yeon Joo, Lee Kyung Hee, Lee Young Kyung, Lee Bae Young, Kim Jin Yong, Jin Kwang Nam, Lim Jae-Kwang, Kim Yun-Hyeon, Kim Ki Beom, Jiang Zicheng, Shao Chuxiao, Lei Junqiang, Zou Shengqiang, Pan Hongqiu, Gu Ye, Zhang Guo, Goo Jin Mo, Yoon Soon Ho

2022-Apr-08

Surgery

Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies.

In EBioMedicine

BACKGROUND : Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe coronavirus disease 2019 (COVID-19), indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, are unique to COVID-19, and if vascular permeability can be therapeutically targeted.

METHODS : Prospectively evaluating the prevalence of circulating inflammatory, cardiac, and EC activation markers as well as developing a microRNA atlas in 241 unvaccinated patients with suspected SARS-CoV-2 infection allowed for prognostic value assessment using a Random Forest model machine learning approach. Subsequent ex vivo experiments assessed EC permeability responses to patient plasma and were used to uncover modulated gene regulatory networks from which rational therapeutic design was inferred.

FINDINGS : Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients, while dysregulation of specific microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating the datasets using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction, which was ameliorated using angiopoietin-1 mimetic or recombinant Slit2-N.

INTERPRETATION : Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis.

FUNDING INFORMATION : This work was directly supported by grant funding from the Ted Rogers Center for Heart Research, Toronto, Ontario, Canada and the Peter Munk Cardiac Center, Toronto, Ontario, Canada.

Gustafson Dakota, Ngai Michelle, Wu Ruilin, Hou Huayun, Schoffel Alice Carvalhal, Erice Clara, Mandla Serena, Billia Filio, Wilson Michael D, Radisic Milica, Fan Eddy, Trahtemberg Uriel, Baker Andrew, McIntosh Chris, Fan Chun-Po S, Dos Santos Claudia C, Kain Kevin C, Hanneman Kate, Thavendiranathan Paaladinesh, Fish Jason E, Howe Kathryn L

2022-Apr-08

Biomarkers, COVID-19, Cardiovascular risk, Endothelium, Inflammation, MicroRNA

General

Bioacoustic signal analysis through complex network features.

In Computers in biology and medicine
The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features - the number of edges (E), graph density (D), transitivity (T), degree centrality (D_cg) and eigenvector centrality (E_cg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of D_cg and E_cg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.
Raj Vimal, Swapna M S, Sankararaman S

2022-Apr-05

Bioacoustic signal, Complex network, Graph theory, Lung auscultation

General

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

ArXiv Preprint
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models. Current research focuses on optimizing policies w.r.t. a single objective, such as the pathogen's attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting criteria (i.a., prevalence, mortality, morbidity, cost), a multi-objective approach is warranted to learn balanced policies. To lift this decision-making process to real-world epidemic models, we apply deep multi-objective reinforcement learning and build upon a state-of-the-art algorithm, Pareto Conditioned Networks (PCN), to learn a set of solutions that approximates the Pareto front of the decision problem. We consider the first wave of the Belgian COVID-19 epidemic, which was mitigated by a lockdown, and study different deconfinement strategies, aiming to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden that is induced by the applied mitigation measures. We contribute a multi-objective Markov decision process that encapsulates the stochastic compartment model that was used to inform policy makers during the COVID-19 epidemic. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution returned by PCN, and observe that it correctly learns to reduce the social burden whenever the hospitalization rates are sufficiently low. In this work, we thus show that multi-objective reinforcement learning is attainable in complex epidemiological models and provides essential insights to balance complex mitigation policies.
Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Rădulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin

2022-04-11

General

Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada

ArXiv Preprint
Over the last decade, Twitter has emerged as one of the most influential forums for social, political, and health discourse. In this paper, we introduce a massive dataset of more than 45 million geo-located tweets posted between 2015 and 2021 from US and Canada (TUSC), especially curated for natural language analysis. We also introduce Tweet Emotion Dynamics (TED) -- metrics to capture patterns of emotions associated with tweets over time. We use TED and TUSC to explore the use of emotion-associated words across US and Canada; across 2019 (pre-pandemic), 2020 (the year the pandemic hit), and 2021 (the second year of the pandemic); and across individual tweeters. We show that Canadian tweets tend to have higher valence, lower arousal, and higher dominance than the US tweets. Further, we show that the COVID-19 pandemic had a marked impact on the emotional signature of tweets posted in 2020, when compared to the adjoining years. Finally, we determine metrics of TED for 170,000 tweeters to benchmark characteristics of TED metrics at an aggregate level. TUSC and the metrics for TED will enable a wide variety of research on studying how we use language to express ourselves, persuade, communicate, and influence, with particularly promising applications in public health, affective science, social science, and psychology.
Krishnapriya Vishnubhotla, Saif M. Mohammad

2022-04-11

Radiology

Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect.

In Japanese journal of radiology

PURPOSE : Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting.

MATERIALS AND METHODS : From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan-Meier method followed by Wilcoxon's signed-rank test.

RESULTS : % GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008).

CONCLUSION : ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.

Ohno Yoshiharu, Aoyagi Kota, Arakita Kazumasa, Doi Yohei, Kondo Masashi, Banno Sumi, Kasahara Kei, Ogawa Taku, Kato Hideaki, Hase Ryota, Kashizaki Fumihiro, Nishi Koichi, Kamio Tadashi, Mitamura Keiko, Ikeda Nobuhiro, Nakagawa Atsushi, Fujisawa Yasuko, Taniguchi Akira, Ikeda Hirotaka, Hattori Hidekazu, Murayama Kazuhiro, Toyama Hiroshi

2022-Apr-09

COVID-19, CT, Favipiravir, Machine learning

Public Health

Explainable death toll motion modeling: COVID-19 data-driven narratives.

In PloS one ; h5-index 176.0
Models have gained the spotlight in many discussions surrounding COVID-19. The urgency for timely decisions resulted in a multitude of models as informed policy actions must be made even when so many uncertainties about the pandemic still remain. In this paper, we use machine learning algorithms to build intuitive country-level COVID-19 motion models described by death toll velocity and acceleration. Model explainability techniques provide insightful data-driven narratives about COVID-19 death toll motion models-while velocity is explained by factors that are increasing/reducing death toll pace now, acceleration anticipates the effects of public health measures on slowing the death toll pace. This allows policymakers and epidemiologists to understand factors driving the outbreak and to evaluate the impacts of different public health measures.
Veloso Adriano, Ziviani Nivio

2022

Internal Medicine

Breakthrough SARS-CoV-2 Infections, Hospitalizations, and Mortality in Vaccinated Patients With Cancer in the US Between December 2020 and November 2021.

In JAMA oncology ; h5-index 85.0

Importance : Limited data have been presented to examine breakthrough SARS-CoV-2 infections, hospitalizations, and mortality in vaccinated patients with cancer in the US.

Objectives : To examine the risk of breakthrough SARS-CoV-2 infection, hospitalizations, and mortality in vaccinated patients with cancer between December 2020 and November 2021.

Design, Setting, and Participants : Retrospective cohort study of electronic health records (EHRs) of vaccinated patients from a multicenter and nationwide database in the US during the period of December 2020 through November 2021. The study population comprised patients who had documented evidence of vaccination (2 doses of Moderna or Pfizer-BioNTech or single dose of Janssen/Johnson & Johnson vaccines) in their EHRs from December 2020 to November 2021 and had no SARS-CoV-2 infection prior to vaccination.

Exposures : The 12 most common cancers combined and separately; recent vs no recent encounter for cancer; and breakthrough SARS-CoV-2 infection.

Main Outcomes and Measures : Time trends of incidence proportions of breakthrough SARS-CoV-2 infections from December 2020 to November 2021 in vaccinated patients with all cancer; cumulative risks of breakthrough infections in vaccinated patients for all cancer and 12 common cancer types; hazard ratios (HRs) and 95% CIs of breakthrough infections between propensity score-matched patients with vs without cancer and between propensity score-matched patients with cancer who had a recent medical encounter for cancer vs those who did not; overall risks, HRs, and 95% CIs of hospitalizations and mortality in patients with cancer who had breakthrough infections vs those who did not.

Results : Among 45 253 vaccinated patients with cancer (mean [SD] age, 68.7 [12.4] years), 53.5% were female, 3.8% were Asian individuals, 15.4% were Black individuals, 4.9% were Hispanic individuals, and 74.1% were White individuals. Breakthrough SARS-CoV-2 infections in patients with cancer increased from December 2020 to November 2021 and reached 52.1 new cases per 1000 persons in November 2021. The cumulative risk of breakthrough infections in patients with all cancer was 13.6%, with highest risk for pancreatic (24.7%), liver (22.8%), lung (20.4%), and colorectal (17.5%) cancers, and lowest risk for thyroid (10.3%), endometrial (11.9%), and breast (11.9%) cancers, vs 4.9% in the noncancer population (P < .001). Patients with cancer had significantly increased risk for breakthrough infections vs patients without cancer (HR, 1.24; 95% CI, 1.19-1.29), with greatest risk for liver (HR, 1.78; 95% CI, 1.38-2.29), lung (HR, 1.73; 95% CI, 1.50-1.99), pancreatic (HR, 1.64; 95% CI, 1.24-2.18), and colorectal (HR, 1.53; 95% CI, 1.32-1.77) cancers and lowest risk for thyroid (HR, 1.07; 95% CI, 0.88-1.30) and skin (HR, 1.17; 95% CI, 0.99-1.38) cancers. Patients who had medical encounters for cancer within the past year had higher risk for breakthrough infections than those who did not (HR, 1.24; 95% CI, 1.18-1.31). Among patients with cancer, the overall risk for hospitalizations and mortality was 31.6% and 3.9%, respectively, in patients with breakthrough infections, vs 6.7% and 1.3% in those without breakthrough infections (HR for hospitalization: 13.48; 95% CI, 11.42-15.91; HR for mortality: 6.76; 95% CI, 4.97-9.20).

Conclusions and Relevance : This cohort study showed significantly increased risks for breakthrough infection in vaccinated patients with cancer, especially those undergoing active cancer care, with marked heterogeneity among specific cancer types. Breakthrough infections in patients with cancer were associated with significant and substantial risks for hospitalizations and mortality.

Wang William, Kaelber David C, Xu Rong, Berger Nathan A

2022-Apr-08

General

A Hypothesis-Free Bridging of Disease Dynamics and Non-pharmaceutical Policies.

In Bulletin of mathematical biology
Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction power. Typically, a hypothesis is made on the form of the transmission rate with respect to time. Yet the real form is too complex to be mechanistically modeled due to the unknown dynamics of many influential factors. We tackle this problem by using a hypothesis-free machine-learning algorithm to estimate the transmission rate from data on non-pharmaceutical policies, and in turn forecast the confirmed cases using a mechanistic disease model. More specifically, we build a hybrid model consisting of a mechanistic ordinary differential equation (ODE) model and a gradient boosting model (GBM). To calibrate the parameters, we develop an "inverse method" that obtains the transmission rate inversely from the other variables in the ODE model and then feed it into the GBM to connect with the policy data. The resulting model forecasted the number of daily confirmed cases up to 35 days in the future in the USA with an averaged mean absolute percentage error of 27%. It can identify the most informative predictive variables, which can be helpful in designing improved forecasters as well as informing policymakers.
Wang Xiunan, Wang Hao, Ramazi Pouria, Nah Kyeongah, Lewis Mark

2022-Apr-08

COVID-19, Generalized boosting model, Hypothesis-free, Inverse method, Machine Learning, Non-pharmaceutical policies

General

Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features.

In Scientific reports ; h5-index 158.0
SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.
Khojasteh Hakimeh, Khanteymoori Alireza, Olyaee Mohammad Hossein

2022-Apr-07

General

Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.

In Computers in biology and medicine

BACKGROUND : Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning.

METHOD : Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.

RESULTS : The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.

CONCLUSION : While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.

Fallahpoor Maryam, Chakraborty Subrata, Heshejin Mohammad Tavakoli, Chegeni Hossein, Horry Michael James, Pradhan Biswajeet

2022-Apr-01

3D CT scan, 3D convolutional neural network, COVID-19, Deep learning, Generalizability, Lung involvement detection

Public Health

Early prediction of SARS-CoV-2 reproductive number from environmental, atmospheric and mobility data: A supervised machine learning approach.

In International journal of medical informatics ; h5-index 49.0

INTRODUCTION : SARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results.

MATERIAL AND METHODS : Our primary goal was to predict Rt of the current day in the twelve provinces of Lombardy with the highest possible accuracy, and with no influence of the local testing strategies. We gathered data about mobility, weather, and pollution from different public sources as a proxy of human behavior and public health measures. We built four supervised machine learning algorithms with different strategies: the outcome variable was the daily median Rt values per province obtained from officially adopted algorithms.

RESULTS : Data from 243 days for every province were presented to our four models (from February 15th, 2020, to October 14th, 2020). Two models using differential calculation of Rt instead of the raw values showed the highest mean coefficient of determination (0.93 for both) and residuals reported the lowest mean error (-0.03 and 0.01) and standard deviation (0.13 for both) as well. The one with access to the value of Rt of the day before heavily relied on that feature for prediction, while the other one had more distributed weights.

DISCUSSION : The model that had not access to the Rt value of the previous day and used Rt differential value as outcome (FDRt) was considered the most robust according to the metrics. Its forecasts were able to predict the trend that Rt values would have developed over different weeks, but it was not particularly accurate in predicting the precise value of Rt. A correlation among mobility, atmospheric, features, pollution and Rt values is plausible, but further testing should be performed.

Caruso Pier Francesco, Angelotti Giovanni, Greco Massimiliano, Guzzetta Giorgio, Cereda Danilo, Merler Stefano, Cecconi Maurizio

2022-Apr-01

COVID-19, Data science, Environmental data, Epidemiology, Machine learning, Mobility data, Rt prediction

General

Global User-Level Perception of COVID-19 Contact Tracing Applications: A Data-Driven Approach Using Natural Language Processing.

In JMIR formative research

BACKGROUND : Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method which, although in normal circumstances is optimal, but not optimal in emergency situations where a mobile device needs to be deployed immediately with little to no user input from the beginning for the greater public good.

OBJECTIVE : In this paper, we aim to analyze the efficacy of AI models and Natural Language Processing (NLP) techniques in automatically extracting and classifying the polarity of users' sentiments by proposing a sentiment analysis framework to automatically analyze users' reviews on COVID-19 contact tracing mobile applications. We also aim to provide a large-scale annotated benchmark dataset to facilitate future research in the domain. As a proof of concepts, we also develop a potential web application, based on the proposed solutions, with a user-friendly interface to automatically analyze and classify users' reviews on the COVID-19 contact tracing applications. The proposed framework combined with the interface which is expected to help the community in quickly analyzing users' perception about such mobile applications and can be used as a rapid surveillance tool to monitor effectiveness of mobile applications and to make immediate changes without going through an intense participatory design method in emergency situations.

METHODS : We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews. In detail, we collected and annotated a large- scale dataset of Android and iOS mobile applications users' reviews for COVID-19 contact tracing. After manually analyzing and annotating users' reviews, we employed both classical (i.e., Naïve Bayes, SVM, Random Forest) and deep learning (i.e., fastText, and different transformers) methods for classification experiments. This resulted in eight different classification models.

RESULTS : We employed eight different methods on three different tasks achieving up to an average F1-Scores 94.8% indicating the feasibility and applicability of automatic sentiment analysis of users' reviews on the COVID-19 contact tracing applications. Moreover, the crowd-sourcing activity resulted in a large-scale benchmark dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The resulted dataset is also made publicly available for research usage.

CONCLUSIONS : The existing literature mostly relies on the manual/exploratory analysis of users' reviews on the application, which is a tedious and time-consuming process. Moreover, in the existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and NLP techniques provide good results in analyzing and classifying users' sentiments' polarity, and that the automatic sentiment analysis can help in analyzing users' responses to the application more quickly with a significant accuracy. Moreover, we also provided a large-scale benchmark dataset composed of 34,534 reviews from 47 different applications. We believe the presented analysis, dataset, and the proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile applications deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.

CLINICALTRIAL :

Ahmad Kashif, Alam Firoj, Qadir Junaid, Qolomany Basheer, Khan Imran, Khan Talhat, Suleman Muhammad, Said Naina, Hassan Syed Zohaib, Gul Asma, Househ Mowafa, Al-Fuqaha Ala

2022-Mar-16

General

Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients.

In Scientific reports ; h5-index 158.0
Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients' data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient's evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.
Reina Reina Alejandro, Barrera José M, Valdivieso Bernardo, Gas María-Eugenia, Maté Alejandro, Trujillo Juan C

2022-Apr-06

General

Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression.

In Heliyon
SARS-CoV-2 infection causes a wide spectrum of disease severity. Identifying the immunological characteristics of severe disease and the risk factors for their development are important in the management of COVID-19. This study aimed to identify and rank clinical and immunological features associated with progression to severe COVID-19 in order to investigate an immunological signature of severe disease. One hundred and eight patients with positive SARS-CoV-2 PCR were recruited. Routine clinical and laboratory markers were measured, as well as myeloid and lymphoid whole-blood immunophenotyping and measurement of the pro-inflammatory cytokines IL-6 and soluble CD25. All analysis was carried out in a routine hospital diagnostic laboratory. Univariate analysis demonstrated that severe disease was most strongly associated with elevated CRP and IL-6, loss of DLA-DR expression on monocytes and CD10 expression on neutrophils. Unbiased machine learning demonstrated that these four features were strongly associated with severe disease, with an average prediction score for severe disease of 0.925. These results demonstrate that these four markers could be used to identify patients developing severe COVID-19 and allow timely delivery of therapeutics.
Townsend Liam, Dyer Adam H, Naughton Aifric, Imangaliyev Sultan, Dunne Jean, Kiersey Rachel, Holden Dean, Mooney Aoife, Leavy Deirdre, Ridge Katie, Sugrue Jamie, Aldoseri Mubarak, Kelliher Jo Hannah, Hennessy Martina, Byrne Declan, Browne Paul, Bacon Christopher L, Doyle Catriona, O’Riordan Ruth, McLaughlin Anne-Marie, Bannan Ciaran, Martin-Loeches Ignacio, White Arthur, McLoughlin Rachel M, Bergin Colm, Bourke Nollaig M, O’Farrelly Cliona, Conlon Niall, Cheallaigh Clíona Ní

2022-Apr

Biomarkers, COVID-19, Immune phenotype, Machine learning, Neutrophil maturity

General

Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences.

In Current genomics

Background : A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe, on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspectives for treatment.

Methods : We developed an alignment-free framework that utilizes machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of human-adapted coronavirus using genomic sequences. We performed extensive experiments through six different feature transformation and machine learning algorithms combining digital signal processing to identify the lethality of possible future novel coronaviruses using existing strains.

Results : The results tested on SARS-CoV, MERS-CoV and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our framework achieves high levels of prediction performance that is alignment-free and based on RNA sequences alone without genome annotations and specialized biological knowledge.

Conclusion : The results demonstrate that, for any novel human coronavirus strains, this study can offer a reliable real-time estimation for its viral lethality.

Yin Rui, Luo Zihan, Kwoh Chee Keong

2021-Dec-31

Coronavirus, SARS-CoV, alignment-free, genomic nucleotide, lethality inference, machine learning

Radiology

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine-related axillary lymphadenopathy.

MATERIALS AND METHODS : We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes. Analysis was performed on PET, CT, and combined PET/CT inputs. Lymph nodes were randomly assigned to a training (n = 132) and validation cohort (n = 33) by 5-fold cross-validation. K-nearest neighbors (KNN) and random forest (RF) machine learning models were used. Performance was evaluated using an area under the receiver-operator characteristic curve (AUC-ROC) score.

RESULTS : Axillary lymph nodes from breast cancer patients (n = 85) and COVID-19-vaccinated individuals (n = 80) were analyzed. Analysis of first-order features showed statistically significant differences (p < 0.05) in all combined PET/CT features, most PET features, and half of the CT features. The KNN model showed the best performance score for combined PET/CT and PET input with 0.98 (± 0.03) and 0.88 (± 0.07) validation AUC, and 96% (± 4%) and 85% (± 9%) validation accuracy, respectively. The RF model showed the best result for CT input with 0.96 (± 0.04) validation AUC and 90% (± 6%) validation accuracy.

CONCLUSION : Radiomics features can differentiate between FDG-avid breast cancer metastatic and FDG-avid COVID-19 vaccine-related axillary lymphadenopathy. Such a model may have a role in differentiating benign nodes from malignant ones.

KEY POINTS : • Patients who were vaccinated with the COVID-19 mRNA vaccine have shown FDG-avid reactive axillary lymph nodes in PET-CT scans. • We evaluated if radiomics and machine learning can distinguish between FDG-avid metastatic axillary lymphadenopathy in breast cancer patients and FDG-avid reactive axillary lymph nodes. • Combined PET and CT radiomics data showed good test AUC (0.98) for distinguishing between metastatic axillary lymphadenopathy and post-COVID-19 vaccine-associated axillary lymphadenopathy. Therefore, the use of radiomics may have a role in differentiating between benign from malignant FDG-avid nodes.

Eifer Michal, Pinian Hodaya, Klang Eyal, Alhoubani Yousef, Kanana Nayroz, Tau Noam, Davidson Tima, Konen Eli, Catalano Onofrio A, Eshet Yael, Domachevsky Liran

2022-Apr-06

Breast cancer, COVID-19 vaccine, Lymphadenopathy, Machine learning, PET-CT

General

The impact of AlphaFold on experimental structure solution

bioRxiv Preprint
AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, posttranslational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning based fold prediction are a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.
Edich, M.; Briggs, D. C.; Kippes, O.; Gao, Y.; Thorn, A.

2022-04-08

General

Reappraising the Value of HIV-1 Vaccine Correlates of Protection Analyses.

In Journal of virology ; h5-index 77.0
With the much-debated exception of the modestly reduced acquisition reported for the RV144 efficacy trial, HIV-1 vaccines have not protected humans against infection, and a vaccine of similar design to that tested in RV144 was not protective in a later trial, HVTN 702. Similar vaccine regimens have also not consistently protected nonhuman primates (NHPs) against viral acquisition. Conversely, experimental vaccines of different designs have protected macaques from viral challenges but then failed to protect humans, while many other HIV-1 vaccine candidates have not protected NHPs. While efficacy varies more in NHPs than humans, vaccines have failed to protect in the most stringent NHP model. Intense investigations have aimed to identify correlates of protection (CoPs), even in the absence of net protection. Unvaccinated animals and humans vary vastly in their susceptibility to infection and in their innate and adaptive responses to the vaccines; hence, merely statistical associations with factors that do not protect are easily found. Systems biological analyses, including artificial intelligence, have identified numerous candidate CoPs but with no clear consistency within or between species. Proposed CoPs sometimes have only tenuous mechanistic connections to immune protection. In contrast, neutralizing antibodies (NAbs) are a central mechanistic CoP for vaccines that succeed against other viruses, including SARS-CoV-2. No HIV-1 vaccine candidate has yet elicited potent and broadly active NAbs in NHPs or humans, but narrow-specificity NAbs against the HIV-1 isolate corresponding to the immunogen do protect against infection by the autologous virus. Here, we analyze why so many HIV-1 vaccines have failed, summarize the outcomes of vaccination in NHPs and humans, and discuss the value and pitfalls of hunting for CoPs other than NAbs. We contrast the failure to find a consistent CoP for HIV-1 vaccines with the identification of NAbs as the principal CoP for SARS-CoV-2.
Klasse P J, Moore John P

2022-Apr-06

COVID-19, HIV-1, SARS-CoV-2, SIV, clinical trials, correlates of protection, neutralizing antibodies, nonhuman primates, nonneutralizing antibodies, systems biology

Public Health

Artificial Intelligence in Pharmacovigilance and Covid-19.

In Current drug safety
The history of pharmacovigilance started back 169 years ago with the death of a 15-year-old girl, Hannah greener. However, the Thalidomide incident of 1961 brought a sharp change in the pharmacovigilance process, with adverse drug reaction reporting being systematic, spontaneous, and regulated timely. Therefore, continuous monitoring of marketed drugs was essential to ensure the safety of public health. Any observed adverse drug reaction detected by signals was to be reported by the health profession. Moreover, signal detection became the primary goal of pharmacovigilance generate based on reported cases. Among various methods used for signal detection, the Spontaneous Reporting System was most widely preferred; although, it had the limitation of "under-reporting". Gradually, the world health organization collaborating centre and "Uppsala Monitoring Centre" was established in 1978 for international monitoring of drug. The centre was responsible for operating various databases like vigiflow, vigibase, vigilyze, and vigiaccess. Recently, huge data could be generated through spontaneous reporting linked with computational methods such as Bayesian Framework, E-Synthesis. Furthermore, drug safety surveillance at an early stage prior to the official alerts or regulatory changes was made possible through social media. In addition, India created a National Pharmacovigilance Program, and Schedule Y of the Drug and Cosmetic Act 1945 was reviewed and amended in 2005. The collaboration of Information Technology and Pharmaceutical Company can further enhance the awareness regarding artificial intelligence in pharmacovigilance, which was in its infancy until 2017. Artificial intelligence helps improve the quality and accuracy of information much quicker.
Bhardwaj Kamini, Alam Rabnoor, Pandeya Ajay, Sharma Pankaj Kumar

2022-Apr-05

Adversedrugreaction(ADR), Artificial intelligence(AI), NationalPharmacovigilance Program (NPP), Pharmacovigilance(PV), Spontaneous Reporting System (SRS), Uppsala Monitoring Centre (UMC), World health organization (WHO).

General

Assessment of COVID-19 risk and prevention effectiveness among spectators of mass gathering events.

In Microbial risk analysis
There is a need to evaluate and minimise the risk of novel coronavirus infections at mass gathering events, such as sports. In particular, to consider how to hold mass gathering events, it is important to clarify how the local infection prevalence, the number of spectators, the capacity proportion, and the implementation of preventions affect the infection risk. In this study, we used an environmental exposure model to analyse the relationship between infection risk and infection prevalence, the number of spectators, and the capacity proportion at mass gathering events in football and baseball games. In addition to assessing risk reduction through the implementation of various preventive measures, we assessed how face-mask-wearing proportion affects infection risk. Furthermore, the model was applied to estimate the number of infectors who entered the stadium and the number of newly infected individuals, and to compare them with actual reported cases. The model analysis revealed an 86%-95% reduction in the infection risk due to the implementation of face-mask wearing and hand washing. Under conditions in which vaccine effectiveness was 20% and 80%, the risk reduction rates of infection among vaccinated spectators were 36% and 96%, respectively. Among the individual measures, face-mask wearing was particularly effective, and the infection risk increased as the face-mask-wearing proportion decreased. A linear relationship was observed between infection risk at mass gathering events and the infection prevalence. Furthermore, the number of newly infected individuals was also dependent on the number of spectators and the capacity proportion independent of the infection prevalence, confirming the importance of considering spectator capacity in infection risk management. These results highlight that it is beneficial for organisers to ensure prevention compliance and to mitigate or limit the number of spectators according to the prevalence of local infection. Both the estimated and reported numbers of newly infected individuals after the events were small, below 10 per 3-4 million spectators, despite a small gap between these numbers.
Yasutaka Tetsuo, Murakami Michio, Iwasaki Yuichi, Naito Wataru, Onishi Masaki, Fujita Tsukasa, Imoto Seiya

2022-Mar-31

COVID-19, infection risk, mass gatherings, novel corona virus, quantitative microbial risk assessment

General

Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images.

In Concurrent engineering, research, and applications
Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.
Subhalakshmi R T, Balamurugan S Appavu Alias, Sasikala S

2022-Mar

COVID-19, Deep learning multimodal fusion, Gaussian Naïve Bayes, convolutional neural network, deeplearning, weiner filtering

General

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review.

In Multimedia tools and applications
Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learning-based, active learning-based, transfer learning-based, and evolutionary learning-based mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.
Rahmani Amir Masoud, Azhir Elham, Naserbakht Morteza, Mohammadi Mokhtar, Aldalwie Adil Hussein Mohammed, Majeed Mohammed Kamal, Taher Karim Sarkhel H, Hosseinzadeh Mehdi

2022-Mar-31

Artificial intelligence, COVID-19, Literature review, Machine learning, Medical image

Internal Medicine

Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients.

In Metabolism: clinical and experimental

BACKGROUND : Lipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases.

METHODS : We analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools.

RESULTS : We identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients.

CONCLUSION : This study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.

Castañé Helena, Iftimie Simona, Baiges-Gaya Gerard, Rodríguez-Tomàs Elisabet, Jiménez-Franco Andrea, López-Azcona Ana Felisa, Garrido Pedro, Castro Antoni, Camps Jordi, Joven Jorge

2022-Apr-02

Artificial intelligence, COVID-19, Lipid metabolism, Lipidomics, Machine learning

Internal Medicine

Model-based cost-effectiveness analysis of oral antivirals against SARS-CoV-2 in Korea.

In Epidemiology and health

Objectives : Countries authorized the emergency use of oral antiviral agents in patients with mild-to-moderate COVID-19. We assessed the cost-effectiveness of introducing these novel oral antiviral agents to reduce the number of severe patients infected with SARS-CoV-2 and the burden of the medical systems.

Methods : From the existing COVID-19 Epidemiology Model, we projected the number of people who require hospital/ICU admissions in Korea in 2022. Treatment scenarios included (i) all adults, (ii) elderly, and (iii) adult patients with underlying diseases administered molnupiravir or nirmatrelvir/ritonavir vis-a-vis standard care. Under the current health systems capacity, we calculate the incremental cost per severe patient averted and per net admission for each scenario relative to standard care.

Results : An estimated, 236,510 COVID-19 patients would require hospital/ICU in Korea in 2022 with standard care. Nirmatrelvir/ritonavir (87% efficacy) is expected to reduce the number of severe patients requiring hospital/ICU admissions by 80%, 24%, and 17% (25%, 8%, and 4% by molnupiravir with 30% efficacy) when targeting all adults, adults with underlying diseases, and elderly patients, respectively. Administration of Nirmatrelvir/ritonavir may be cost-effective as $1,454, $8,878, and $8,964 (while molnupiravir may be less likely cost-effective as $7,915, $28,492, $29,575) per severe patient averted if targeted respectively to the target group mentioned above, compared to standard care.

Conclusion : In Korea, oral nirmatrelvir/ritonavir treatment of symptomatic COVID-19 patients can be highly cost-effective if targeted to elderly patients while substantially reducing hospital admission demand below the health systems capacity limit if all adult patients are targeted compared to standard care.

Jo Youngji, Kim Sun Bean, Radnaabaatar Munkhzul, Huh Kyungmin, Yoo Jin-Hong, Peck Kyong Ran, Park Hojun, Jung Jaehun

2022-Mar-12

COVID-19, Cost-effectiveness analysis, Hospital admissions, Oral antivirals, SARS-CoV-2

General

Pre-processing methods in chest X-ray image classification.

In PloS one ; h5-index 176.0

BACKGROUND : The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount.

METHODS : This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization.

RESULTS : We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively.

CONCLUSION : Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.

Giełczyk Agata, Marciniak Anna, Tarczewska Martyna, Lutowski Zbigniew

2022

General

Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract.

In PloS one ; h5-index 176.0
Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.
Farr Ryan J, Rootes Christina L, Stenos John, Foo Chwan Hong, Cowled Christopher, Stewart Cameron R

2022

General

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

In Scientific reports ; h5-index 158.0
Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.
Gourdeau Daniel, Potvin Olivier, Archambault Patrick, Chartrand-Lefebvre Carl, Dieumegarde Louis, Forghani Reza, Gagné Christian, Hains Alexandre, Hornstein David, Le Huy, Lemieux Simon, Lévesque Marie-Hélène, Martin Diego, Rosenbloom Lorne, Tang An, Vecchio Fabrizio, Yang Issac, Duchesne Nathalie, Duchesne Simon

2022-Apr-04

Public Health

The effectiveness of governmental nonpharmaceutical interventions against COVID-19 at controlling seasonal influenza transmission: an ecological study.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : A range of strict nonpharmaceutical interventions (NPIs) were implemented in many countries to combat the coronavirus 2019 (COVID-19) pandemic. These NPIs may also be effective at controlling seasonal influenza virus infections, as influenza viruses have the same transmission path as severe acute respiratory syndrome coronavirus 2. The aim of this study was to evaluate the effects of different NPIs on the control of seasonal influenza.

METHODS : Data for 14 NPIs implemented in 33 countries and the corresponding influenza virological surveillance data were collected. The influenza suppression index was calculated as the difference between the influenza positivity rate during its period of decline from 2019 to 2020 and during the influenza epidemic seasons in the previous 9 years. A machine learning model was developed using an extreme gradient boosting tree regressor to fit the NPI and influenza suppression index data. The SHapley Additive exPlanations tool was used to characterize the NPIs that suppressed the transmission of influenza.

RESULTS : Of all NPIs tested, gathering limitations had the greatest contribution (37.60%) to suppressing influenza transmission during the 2019-2020 influenza season. The three most effective NPIs were gathering limitations, international travel restrictions, and school closures. For these three NPIs, their intensity threshold required to generate an effect were restrictions on the size of gatherings less than 1000 people, ban of travel to all regions or total border closures, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask-wearing requirements and gathering limitations, whereas merely implementing a mask-wearing requirement, and not other NPIs, diluted the effectiveness of mask-wearing requirements at suppressing influenza transmission.

CONCLUSIONS : Gathering limitations, ban of travel to all regions or total border closures, and closing some levels of schools were found to be the most effective NPIs at suppressing influenza transmission. It is recommended that the mask-wearing requirement be combined with gathering limitations and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and other potential pandemics.

Qiu Zekai, Cao Zicheng, Zou Min, Tang Kang, Zhang Chi, Tang Jing, Zeng Jinfeng, Wang Yaqi, Sun Qianru, Wang Daoze, Du Xiangjun

2022-Apr-04

Global, Influenza, Machine learning, Nonpharmaceutical interventions

General

Low-Dose COVID-19 CT Image Denoising Using CNN and its Method Noise Thresholding.

In Current medical imaging

: Noise in computed tomography (CT) images may occur due to low radiation dose. Hence, the main aim of this paper is to reduce the noise from low dose CT images so that the risk of high radiation dose can be reduced.

BACKGROUND : The novel corona virus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected.

OBJECTIVE : The COVID-19 attacks people who have less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images.

METHOD : In this paper, a new denoising technique for such low dose Covid-19 CT images has been introduced using a convolution neural network (CNN) and the method noise-based thresholding. The major concern of the methodology for reducing the risk associated with radiation while diagnosing.

RESULTS : The results are evaluated visually and also by using standard performance metrics. From comparative analysis, it was observed that proposed works gives better outcomes.

CONCLUSIONS** : The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in terms of noise suppression and clinical edge preservation.

Diwakar Manoj, Pandey Neeraj Kumar, Singh Ravinder, Sisodia Dilip, Arya Chandrakala, Singh Prabhishek, Chakraborty Chinmay

2022-Apr-04

CNN, COVID-19, CT Images, DWT, Image processing, deep learning

General

X-ray image based COVID-19 detection using evolutionary deep learning approach.

In Expert systems with applications
Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient.
Jalali Seyed Mohammad Jafar, Ahmadian Milad, Ahmadian Sajad, Hedjam Rachid, Khosravi Abbas, Nahavandi Saeid

2022-Mar-30

COVID-19, Convolutional neural network, Coronavirus, Deep neuroevolution learning, Image classification, K-nearest neighbour classifier

General

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.

In Journal of urban health : bulletin of the New York Academy of Medicine
The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.
Itzhak Nevo, Shahar Tomer, Moskovich Robert, Shahar Yuval

2022-Apr-04

Coronavirus, Disparities, Ethnicity, Socio-economic, Temporal distribution, Urbanity, Vulnerability

General

LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

bioRxiv Preprint
One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified novel therapeutics for cancer, lifestyle-related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will bring about a paradigm shift in translational medicine, providing a bridge from bench side to bedside.
Shimizu, H.; Kodama, M.; Matsumoto, M.; Orba, Y.; Sasaki, M.; Sato, A.; Sawa, H.; Nakayama, K. I.

2022-04-06

General

Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates.

In Computers in biology and medicine
The development of a new vaccine is a challenging exercise involving several steps including computational studies, experimental work, and animal studies followed by clinical studies. To accelerate the process, in silico screening is frequently used for antigen identification. Here, we present Vaxi-DL, web-based deep learning (DL) software that evaluates the potential of protein sequences to serve as vaccine target antigens. Four different DL pathogen models were trained to predict target antigens in bacteria, protozoa, fungi, and viruses that cause infectious diseases in humans. Datasets containing antigenic and non-antigenic sequences were derived from known vaccine candidates and the Protegen database. Biological and physicochemical properties were computed for the datasets using publicly available bioinformatics tools. For each of the four pathogen models, the datasets were divided into training, validation, and testing subsets and then scaled and normalised. The models were constructed using Fully Connected Layers (FCLs), hyper-tuned, and trained using the training subset. Accuracy, sensitivity, specificity, precision, recall, and AUC (Area under the Curve) were used as metrics to assess the performance of these models. The models were benchmarked using independent datasets of known target antigens against other prediction tools such as VaxiJen and Vaxign-ML. We also tested Vaxi-DL on 219 known potential vaccine candidates (PVC) from 37 different pathogens. Our tool predicted 175 PVCs correctly out of 219 sequences. We also tested Vaxi-DL on different datasets obtained from multiple resources. Our tool has demonstrated an average sensitivity of 93% and will thus be a useful tool for prioritising PVCs for preclinical studies.
Rawal Kamal, Sinha Robin, Nath Swarsat Kaushik, Preeti P, Kumari Priya, Gupta Srijanee, Sharma Trapti, Strych Ulrich, Hotez Peter, Bottazzi Maria Elena

2022-Mar-22

Antigen prediction, Artificial intelligence, COVID-19, Coronavirus, Deep learning, In silico vaccine development, Machine learning, SARS-CoV-2, Vaccine, Vaccine design, Vaxi-DL server, mRNA vaccines

General

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing.

In Journal of healthcare engineering
Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.
Sharma Chandra Mani, Goyal Lakshay, Chariar Vijayaraghavan M, Sharma Navel

2022

Radiology

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

In Journal of healthcare engineering
Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.
Ragab Mahmoud, Alshehri Samah, Alhakamy Nabil A, Alsaggaf Wafaa, Alhadrami Hani A, Alyami Jaber

2022

General

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm.

In Journal of healthcare engineering
This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person's life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu's maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.
Shivahare Basu Dev, Gupta S K

2022

General

COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches.

In Journal of healthcare engineering
Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20-50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner's advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes.
Aggarwal Alok, Chakradar Madam, Bhatia Manpreet Singh, Kumar Manoj, Stephan Thompson, Gupta Sachin Kumar, Alsamhi S H, Al-Dois Hatem

2022

General

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence.

In Cluster computing
Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively.
Hayyolalam Vahideh, Otoum Safa, Özkasap Öznur

2022-Mar-26

Artificial intelligence, COVID 19, Connected healthcare, Fault prevention, IoT, Meta-heuristics

General

Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence.

In Measurement : journal of the International Measurement Confederation
Today COVID-19 pandemic articulates high stress on clinical resources around the world. At present, physical and viral tests are slowly emerging, and there is a need for robust pandemic detection that biomedical sensors can aid. The utility of biomedical sensors is correlated with the medical instruments with physiological metrics. These Biomedical sensors are integrated with the systematic device to track the target analytes with a biomedical component. The COVID-19 patients' samples are collected, and biomarkers are detected using four sensors: blood pressure sensor, G-FET based biosensor, electrochemical sensor, and potentiometric sensor with different quantifiable measures. The imputed data is then profiled with chest X-ray images from the Covid-19 patients.Multi-Layer Perceptron (MLP), an AI model, is deployed to identify the hidden signatures with biomarkers. The performance of the biosensor is measured with three parameters such as sensitivity, specificity and detection limit by generating the calibration plots that accurately fits the model.
Hemamalini V, Anand L, Nachiyappan S, Geeitha S, Ramana Motupalli Venkata, Kumar R, Ahilan A, Rajesh M

2022-May-15

Artificial intelligence, Biomarkers, Biomedical sensors, COVID-19, Hidden signatures, Medical instruments, Quantifiable Measures

General

Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient.

In Multimedia tools and applications
COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.
Kumar Rahul, Arora Ridhi, Bansal Vipul, Sahayasheela Vinodh J, Buckchash Himanshu, Imran Javed, Narayanan Narayanan, Pandian Ganesh N, Raman Balasubramanian

2022-Mar-28

COVID-19, Deep learning, Feature extraction, Feature selection, Machine learning, Pearson correlation coefficient, X-ray images

General

Ridge count thresholding to uncover coordinated networks during onset of the Covid-19 pandemic.

In Social network analysis and mining
In order to combat information operations (IO) and disinformation campaigns, one must look at the behaviors of the accounts pushing specific narratives and stories through social media, not at the content itself. In this work, we present a new process for extracting tweet storms and uncovering networks of accounts that are working in a coordinated fashion using ridge count thresholding (RCT). To do this, we started with a dataset of 60 million individual tweets from the early weeks of the Covid-19 pandemic. Coherent topics are extracted from this data by testing three different preprocessing pipelines and applying Orthogonal Nonnegative Matrix Factorization (ONMF). The most effective preprocessing pipeline used hashtag preclustering to downselect the total dataset to the 7 million tweets that included the top hashtags. Each topic identified by ONMF is described by a topic-tweet signal, crafted using the time stamp included in each tweet's metadata. These signals were broken down into tweet storms using RCT, which is calculated from the Dynamic Wavelet Fingerprint transform of each topic-tweet signal. Each tweet storm described a time of increased activity around a topic. Tweet storms identified in this way each represent some behavior in the underlying network. In total, we identified 39,817 total tweet storms that included about 2 million unique tweets. These tweet storms were used to identify networks of accounts that commonly co-occur within tweet storms to isolate those communities most responsible for driving narratives and pushing stories through social media. Through this process, we were able to identify 22 unique networks of accounts that were densely connected based on RCT tweet storm identification. Many of the identified networks exhibit obvious inauthentic behaviors that are potentially a part of an IO campaign.
Kirn Spencer Lee, Hinders Mark K

2022

Covid-19, Information operations, Machine learning, Network analysis, Topic modeling, Twitter

Surgery

Ethnicity-Specific Features of COVID-19 Among Arabs, Africans, South Asians, East Asians, and Caucasians in the United Arab Emirates.

In Frontiers in cellular and infection microbiology ; h5-index 53.0

Background : Dubai (United Arab Emirates; UAE) has a multi-national population which makes it exceptionally interesting study sample because of its unique demographic factors.

Objective : To stratify the risk factors for the multinational society of the UAE.

Methods : A retrospective chart review of 560 patients sequentially admitted to inpatient care with laboratory confirmed COVID-19 was conducted. We studied patients' demographics, clinical features, laboratory results, disease severity, and outcomes. The parameters were compared across different ethnic groups using tree-based estimators to rank the ethnicity-specific disease features. We trained ML classification algorithms to build a model of ethnic specificity of COVID-19 based on clinical presentation and laboratory findings on admission.

Results : Out of 560 patients, 43.6% were South Asians, 26.4% Middle Easterns, 16.8% East Asians, 10.7% Caucasians, and 2.5% are under others. UAE nationals represented half of the Middle Eastern patients, and 13% of the entire cohort. Hypertension was the most common comorbidity in COVID-19 patients. Subjective complaint of fever and cough were the chief presenting symptoms. Two-thirds of the patients had either a mild disease or were asymptomatic. Only 20% of the entire cohort needed oxygen therapy, and 12% needed ICU admission. Forty patients (~7%) needed invasive ventilation and fifteen patients died (2.7%). We observed differences in disease severity among different ethnic groups. Caucasian or East-Asian COVID-19 patients tended to have a more severe disease despite a lower risk profile. In contrast to this, Middle Eastern COVID-19 patients had a higher risk factor profile, but they did not differ markedly in disease severity from the other ethnic groups. There was no noticeable difference between the Middle Eastern subethnicities-Arabs and Africans-in disease severity (p = 0.81). However, there were disparities in the SOFA score, D-dimer (p = 0.015), fibrinogen (p = 0.007), and background diseases (hypertension, p = 0.003; diabetes and smoking, p = 0.045) between the subethnicities.

Conclusion : We observed variations in disease severity among different ethnic groups. The high accuracy (average AUC = 0.9586) of the ethnicity classification model based on the laboratory and clinical findings suggests the presence of ethnic-specific disease features. Larger studies are needed to explore the role of ethnicity in COVID-19 disease features.

Al Zahmi Fatmah, Habuza Tetiana, Awawdeh Rasha, Elshekhali Hossam, Lee Martin, Salamin Nassim, Sajid Ruhina, Kiran Dhanya, Nihalani Sanjay, Smetanina Darya, Talako Tatsiana, Neidl-Van Gorkom Klaus, Zaki Nazar, Loney Tom, Statsenko Yauhen

2021

COVID-19, Gulf region, Middle East, UAE, ethnicity, host organism, machine learning, viral pneumonia

General

Short- and Long-Term Recovery after Moderate/Severe AKI in Patients with and without COVID-19.

In Kidney360

Background : Severe AKI is strongly associated with poor outcomes in coronavirus disease 2019 (COVID-19), but data on renal recovery are lacking.

Methods : We retrospectively analyzed these associations in 3299 hospitalized patients (1338 with COVID-19 and 1961 with acute respiratory illness but who tested negative for COVID-19). Uni- and multivariable analyses were used to study mortality and recovery after Kidney Disease Improving Global Outcomes Stages 2 and 3 AKI (AKI-2/3), and Machine Learning was used to predict AKI and recovery using admission data. Long-term renal function and other outcomes were studied in a subgroup of AKI-2/3 survivors.

Results : Among the 172 COVID-19-negative patients with AKI-2/3, 74% had partial and 44% complete renal recovery, whereas 12% died. Among 255 COVID-19 positive patients with AKI-2/3, lower recovery and higher mortality were noted (51% partial renal recovery, 25% complete renal recovery, 24% died). On multivariable analysis, intensive care unit admission and acute respiratory distress syndrome were associated with nonrecovery, and recovery was significantly associated with survival in COVID-19-positive patients. With Machine Learning, we were able to predict recovery from COVID-19-associated AKI-2/3 with an average precision of 0.62, and the strongest predictors of recovery were initial arterial partial pressure of oxygen and carbon dioxide, serum creatinine, potassium, lymphocyte count, and creatine phosphokinase. At 12-month follow-up, among 52 survivors with AKI-2/3, 26% COVID-19-positive and 24% COVID-19-negative patients had incident or progressive CKD.

Conclusions : Recovery from COVID-19-associated moderate/severe AKI can be predicted using admission data and is associated with severity of respiratory disease and in-hospital death. The risk of CKD might be similar between COVID-19-positive and -negative patients.

Sun Siao, Annadi Raji R, Chaudhri Imran, Munir Kiran, Hajagos Janos, Saltz Joel, Hoai Minh, Mallipattu Sandeep K, Moffitt Richard, Koraishy Farrukh M

2022-Feb-24

AKI, AKI and ICU nephrology, CKD, COVID-19, Machine Learning, mortality, recovery

General

Psychotropic Medication Use Is Associated With Greater 1-Year Incidence of Dementia After COVID-19 Hospitalization.

In Frontiers in medicine

Background : COVID-19 has been associated with an increased risk of incident dementia (post-COVID dementia). Establishing additional risk markers may help identify at-risk individuals and guide clinical decision-making.

Methods : We investigated pre-COVID psychotropic medication use (exposure) and 1-year incidence of dementia (outcome) in 1,755 patients (≥65 years) hospitalized with COVID-19. Logistic regression models were used to examine the association, adjusting for demographic and clinical variables. For further confirmation, we applied the Least Absolute Shrinkage and Selection Operator (LASSO) regression and a machine learning (Random Forest) algorithm.

Results : One-year incidence rate of post-COVID dementia was 12.7% (N = 223). Pre-COVID psychotropic medications (OR = 2.7, 95% CI: 1.8-4.0, P < 0.001) and delirium (OR = 3.0, 95% CI: 1.9-4.6, P < 0.001) were significantly associated with greater 1-year incidence of post-COVID dementia. The association between psychotropic medications and incident dementia remained robust when the analysis was restricted to the 423 patients with at least one documented neurological or psychiatric diagnosis at the time of COVID-19 admission (OR = 3.09, 95% CI: 1.5-6.6, P = 0.002). Across different drug classes, antipsychotics (OR = 2.8, 95% CI: 1.7-4.4, P < 0.001) and mood stabilizers/anticonvulsants (OR = 2.4, 95% CI: 1.39-4.02, P = 0.001) displayed the greatest association with post-COVID dementia. The association of psychotropic medication with dementia was further confirmed with Random Forest and LASSO analysis.

Conclusion : Confirming prior studies we observed a high dementia incidence in older patients after COVID-19 hospitalization. Pre-COVID psychotropic medications were associated with higher risk of incident dementia. Psychotropic medications may be risk markers that signify neuropsychiatric symptoms during prodromal dementia, and not mutually exclusive, contribute to post-COVID dementia.

Freudenberg-Hua Yun, Makhnevich Alexander, Li Wentian, Liu Yan, Qiu Michael, Marziliano Allison, Carney Maria, Greenwald Blaine, Kane John M, Diefenbach Michael, Burns Edith, Koppel Jeremy, Sinvani Liron

2022

COVID-19, cognitive impairment, dementia, geriatric, post-COVID, psychotropic medication

Public Health

Socio-Demographic Factors Associated With COVID-19 Vaccine Hesitancy Among Middle-Aged Adults During the Quebec's Vaccination Campaign.

In Frontiers in public health

Introduction : The objective of this study was to characterize the combinations of demographic and socioeconomic characteristics associated to the unwillingness to receive the COVID-19 vaccines during the 2021 Quebec's vaccination campaign.

Materials and Methods : In March-June 2021, we conducted an online survey of the participants of the CARTaGENE population-based cohort, composed of middle-aged and older adults. After comparing the vaccinated and unvaccinated participants, we investigated vaccine hesitancy among participants who were unvaccinated. For identifying homogeneous groups of individuals with respect to vaccine hesitancy, we used a machine learning approach based on a hybrid tree-based model.

Results : Among the 6,105 participants of the vaccine cohort, 3,553 (58.2%) had at least one dose of COVID-19 vaccine. Among the 2,552 participants, 221 (8.7%) did not want to be vaccinated (91) or were uncertain (130). The median age for the unvaccinated participants was 59.3 years [IQR 54.7-63.9]. The optimal hybrid tree-based model identified seven groups. Individuals having a household income lower than $100,000 and being born outside of Canada had the highest rate of vaccine hesitancy (28% [95% CI 19.8-36.3]). For those born in Canada, the vaccine hesitancy rate among the individuals who have a household income below $50,000 before the pandemic or are Non-retired was of 12.1% [95% CI 8.7-15.5] and 10.6% [95% CI 7.6-13.7], respectively. For the participants with a high household income before the pandemic (more than $100,000) and a low level of education, those who experienced a loss of income during the pandemic had a high level of hesitancy (19.2% [8.5-29.9]) whereas others who did not experience a loss of income had a lower level of hesitancy (6.0% [2.8-9.2]). For the other groups, the level of hesitancy was low of around 3% (3.2% [95% CI 1.9-4.4] and 3.4% [95% CI 1.5-5.2]).

Discussion : Public health initiatives to tackle vaccine hesitancy should take into account these socio-economic determinants and deliver personalized messages toward people having socio-economic difficulties and/or being part of socio-cultural minorities.

Jantzen Rodolphe, Maltais Mathieu, Broët Philippe

2022

CARTaGENE, COVID-19, Quebec vaccination campaign, population-based cohort, tree-based model, vaccine hesitancy

General

A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities.

In Signal, image and video processing
Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid patient care and terminate viral spread. Lung infection segmentations are useful to identify unique infection patterns that may support rapid diagnosis, severity assessment, and patient prognosis prediction, but manual segmentations are time-consuming and depend on radiologic expertise. Deep learning-based methods have been explored to reduce the burdens of segmentation; however, their accuracies are limited due to the lack of large, publicly available annotated datasets that are required to establish ground truths. For these reasons, we propose a semi-automatic, threshold-based segmentation method to generate region of interest (ROI) segmentations of infection visible on lung computed tomography (CT) scans. Infection masks are then used to calculate the percentage of lung abnormality (PLA) to determine COVID-19 severity and to analyze the disease progression in follow-up CTs. Compared with other COVID-19 ROI segmentation methods, on average, the proposed method achieved improved precision ( 47.49 % ) and specificity ( 98.40 % ) scores. Furthermore, the proposed method generated PLAs with a difference of ± 3.89 % from the ground-truth PLAs. The improved ROI segmentation results suggest that the proposed method has potential to assist radiologists in assessing infection severity and analyzing disease progression in follow-up CTs.
Khan Azrin, Garner Rachael, Rocca Marianna La, Salehi Sana, Duncan Dominique

2022-Mar-28

General

Design of Online Music Education System Based on Artificial Intelligence and Multiuser Detection Algorithm.

In Computational intelligence and neuroscience
With the development of information technology, online music education has become a mainstream education method. Especially after the outbreak of COVID-19, music teachers have to teach through online. Therefore, an online music education system that can improve the quality of teaching is particularly important. Multiuser detection algorithms and artificial intelligence have important applications in many fields, and the field of music online education is no exception. This paper takes the music teaching of the music distance teaching unit as the goal and conducts sufficient research on the educational subjects such as teachers, students, and administrators. And with the help of the SCMA system multiuser detection algorithm and artificial intelligence technology, the system analysis and design method is used to analyze and design the music teaching function system. The system module involves basic information management, student music assignments, online courses, and other levels, providing an excellent educational system design example for music online education. The conclusion analysis shows that the music online education system based on SCMA system multiuser detection algorithm and artificial intelligence designed in this paper can significantly improve the audience's music learning efficiency and has obvious benefits to the student group.
Yan Hua

2022

General

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.

In Applied soft computing
Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. To this end, we propose a novel attention framework to estimate COVID-19 severity as a regression score from a weakly annotated CT scan dataset. It takes a non-locality approach that correlates features across different parts and spatial scales of the 3D scan. An explicit guidance mechanism from limited infection labeling drives attention refinement and feature modulation. The resulting encoded representation is further enriched through cross-channel attention. The attention model also infuses global contextual awareness into the deep voxel features by querying the base CT scan to mine relevant features. Consequently, it learns to effectively localize its focus region and chisel out the infection precisely. Experimental validation on the MosMed dataset shows that the proposed architecture has significant potential in augmenting existing methods as it achieved a 0.84 R-squared score and 0.133 mean absolute difference.
Karthik R, Menaka R, Hariharan M, Won Daehan

2022-May

3D CNN, COVID-19 severity, Deep learning, Non-local attention, Squeeze

General

Digital twin framework for reconfigurable manufacturing systems (RMSs): design and simulation.

In The International journal, advanced manufacturing technology
Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs).
Kombaya Touckia Jesus, Hamani Nadia, Kermad Lyes

2022-Mar-30

Digital twin (DT), Generic model, Modular framework, Reconfigurable manufacturing system (RMS), SysML

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

2021-Nov-09

Artificial intelligence, COVID-19, Chest X-Ray, Mortality, Predictive models, Prognosis

General

COVID-WideNet-A capsule network for COVID-19 detection.

In Applied soft computing
Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.
Gupta P K, Siddiqui Mohammad Khubeb, Huang Xiaodi, Morales-Menendez Ruben, Pawar Harsh, Terashima-Marin Hugo, Wajid Mohammad Saif

2022-Mar-29

CNN, COVID-19, COVID-19: Virus variants, Capsule Networks, Deep learning, RT-PCR, X-Rays

General

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

In Journal of healthcare engineering
Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.
Abdulkareem Karrar Hameed, Mostafa Salama A, Al-Qudsy Zainab N, Mohammed Mazin Abed, Al-Waisy Alaa S, Kadry Seifedine, Lee Jinseok, Nam Yunyoung

2022

General

Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning

bioRxiv Preprint
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases two months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
Nagpal, S.; Pal, R.; Ashima, ; Tyagi, A.; Tripathi, S.; Nagori, A.; Ahmad, S.; Mishra, H. P.; Kutum, R.; Sethi, T.

2022-04-04

General

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain.

In Computers in biology and medicine
With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
Heidari Arash, Toumaj Shiva, Navimipour Nima Jafari, Unal Mehmet

2022-Mar-28

Blockchain, CNN, COVID-19, Chest CT, Deep learning, Transfer learning

Public Health

Ecology of middle east respiratory syndrome coronavirus, 2012-2020: A machine learning modeling analysis.

In Transboundary and emerging diseases ; h5-index 40.0
The ongoing enzootic circulation of the Middle East respiratory syndrome coronavirus (MERS-CoV) in the Middle East and North Africa is increasingly raising the concern about the possibility of its recombination with other human-adapted coronaviruses, particularly the pandemic SARS-CoV-2. We aim to provide an updated picture about ecological niches of MERS-CoV and associated socio-environmental drivers. Based on 356 confirmed MERS cases with animal contact reported to the WHO and 63 records of animal infections collected from the literature as of May 30, 2020, we assessed ecological niches of MERS-CoV using an ensemble model integrating three machine learning algorithms. With a high predictive accuracy (Area under receiver operating characteristic curve = 91.66% in test data), the ensemble model estimated that ecologically suitable areas span over the Middle East, South Asia and the whole North Africa, much wider than the range of reported locally infected MERS cases and test-positive animal samples. Ecological suitability for MERS-CoV was significantly associated with high levels of bareland coverage (relative contribution = 30.06%), population density (7.28%), average temperature (6.48%), and camel density (6.20%). Future surveillance and intervention programs should target the high-risk populations and regions informed by updated quantitative analyses. This article is protected by copyright. All rights reserved.
Zhang An-Ran, Li Xin-Lou, Wang Tao, Liu Kun, Liu Ming-Jin, Zhang Wen-Hui, Zhao Guo-Ping, Chen Jin-Jin, Zhang Xiao-Ai, Miao Dong, Ma Wei, Fang Li-Qun, Yang Yang, Liu Wei

2022-Apr-02

MERS-CoV, Middle East respiratory syndrome, machine learning, predicted map, risk factors

Surgery

Critically Ill COVID-19 Patients Exhibit Anti-SARS-CoV-2 Serological Responses.

In Pathophysiology : the official journal of the International Society for Pathophysiology
Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is a global health care emergency. Anti-SARS-CoV-2 serological profiling of critically ill COVID-19 patients was performed to determine their humoral response. Blood was collected from critically ill ICU patients, either COVID-19 positive (+) or COVID-19 negative (-), to measure anti-SARS-CoV-2 immunoglobulins: IgM; IgA; IgG; and Total Ig (combined IgM/IgA/IgG). Cohorts were similar, with the exception that COVID-19+ patients had a greater body mass indexes, developed bilateral pneumonias more frequently and suffered increased hypoxia when compared to COVID-19- patients (p < 0.05). The mortality rate for COVID-19+ patients was 50%. COVID-19 status could be determined by anti-SARS-CoV-2 serological responses with excellent classification accuracies on ICU day 1 (89%); ICU day 3 (96%); and ICU days 7 and 10 (100%). The importance of each Ig isotype for determining COVID-19 status on combined ICU days 1 and 3 was: Total Ig, 43%; IgM, 27%; IgA, 24% and IgG, 6%. Peak serological responses for each Ig isotype occurred on different ICU days (IgM day 13 > IgA day 17 > IgG persistently increased), with the Total Ig peaking at approximately ICU day 18. Those COVID-19+ patients who died had earlier or similar peaks in IgA and Total Ig in their ICU stay when compared to patients who survived (p < 0.005). Critically ill COVID-19 patients exhibit anti-SARS-CoV-2 serological responses, including those COVID-19 patients who ultimately died, suggesting that blunted serological responses did not contribute to mortality. Serological profiling of critically ill COVID-19 patients may aid disease surveillance, patient cohorting and help guide antibody therapies such as convalescent plasma.
Fraser Douglas D, Cepinskas Gediminas, Slessarev Marat, Martin Claudio M, Daley Mark, Patel Maitray A, Miller Michael R, Patterson Eric K, O’Gorman David B, Gill Sean E, Higgins Ian, John Julius P P, Melo Christopher, Nini Lylia, Wang Xiaoqin, Zeidler Johannes, Cruz-Aguado Jorge A

2021-May-17

COVID-19, humoral response, immunoglobulins, intensive care unit, outcome, serology

Public Health

A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe.

In Scientific data
During the second half of 2020, many European governments responded to the resurging transmission of SARS-CoV-2 with wide-ranging non-pharmaceutical interventions (NPIs). These efforts were often highly targeted at the regional level and included fine-grained NPIs. This paper describes a new dataset designed for the accurate recording of NPIs in Europe's second wave to allow precise modelling of NPI effectiveness. The dataset includes interventions from 114 regions in 7 European countries during the period from the 1st August 2020 to the 9th January 2021. The paper includes NPI definitions tailored to the second wave following an exploratory data collection. Each entry has been extensively validated by semi-independent double entry, comparison with existing datasets, and, when necessary, discussion with local epidemiologists. The dataset has considerable potential for use in disentangling the effectiveness of NPIs and comparing the impact of interventions across different phases of the pandemic.
Altman George, Ahuja Janvi, Monrad Joshua Teperowski, Dhaliwal Gurpreet, Rogers-Smith Charlie, Leech Gavin, Snodin Benedict, Sandbrink Jonas B, Finnveden Lukas, Norman Alexander John, Oehm Sebastian B, Sandkühler Julia Fabienne, Kulveit Jan, Flaxman Seth, Gal Yarin, Mishra Swapnil, Bhatt Samir, Sharma Mrinank, Mindermann Sören, Brauner Jan Markus

2022-Apr-01

Public Health

Study protocol: a survey exploring patients' and healthcare professionals' expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots in nursing.

In BMJ open

INTRODUCTION : Population ageing, the rise of chronic diseases and the emergence of new viruses are some of the factors that contribute to an increasing share of gross domestic product dedicated to health spending. COVID-19 has shown that nursing staff represents the critical part of hospitalisation. Technological developments in robotics and artificial intelligence can significantly reduce costs and lead to improvements in many hospital processes. The proposed study aims to assess expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots into hospitalised care workflow from patients' and healthcare professionals' perspectives and to compare them with the results of similar studies.

METHODS/DESIGN : The study is designed as a cross-sectional survey, which will include three previously validated questionnaires, the Technology-Specific Expectation Scale (TSES), the Ethical Acceptability Scale (EAS) and the Negative Attitudes towards Robots Scale (NARS). The employees of a regional clinical centre will be asked to participate via an electronic survey and respond to TSES and EAS questionaries. Patients will respond to TSES and NARS questionaries. The survey will be conducted online.

ETHICS AND DISSEMINATION : Ethical approval for the study was obtained by the Medical Ethics Commission of the University Medical Center Maribor. Results will be published in a relevant scientific journal and communicated to participants and relevant institutions through dissemination activities and the ecosystem of the Horizon 2020 funded project HosmartAI (grant no. 101016834).

ETHICAL APPROVAL DATE : 06 May 2021.

ESTIMATED START OF THE STUDY : December 2021.

Mlakar Izidor, Kampič Tadej, Flis Vojko, Kobilica Nina, Molan Maja, Smrke Urška, Plohl Nejc, Bergauer Andrej

2022-Apr-01

Organisation of health services, PUBLIC HEALTH, QUALITATIVE RESEARCH, Quality in health care

Radiology

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.

In European radiology ; h5-index 62.0

OBJECTIVE : To develop an automatic COVID-19 Reporting and Data System (CO-RADS)-based classification in a multi-demographic setting.

METHODS : This multi-institutional review boards-approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18-100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS-based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography.

RESULTS : The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the "wavelet_(LH)_GLCM_Imc1" feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types.

CONCLUSION : Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment.

KEYPOINTS : • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92.

Nagaraj Yeshaswini, de Jonge Gonda, Andreychenko Anna, Presti Gabriele, Fink Matthias A, Pavlov Nikolay, Quattrocchi Carlo C, Morozov Sergey, Veldhuis Raymond, Oudkerk Matthijs, van Ooijen Peter M A

2022-Apr-01

COVID-19, Deep learning, Diagnostic imaging, SARS-CoV-2, Tomography X-ray computed

Public Health

Who Is at Risk of Poor Mental Health Following Coronavirus Disease-19 Outpatient Management?

In Frontiers in medicine

Background : Coronavirus Disease-19 (COVID-19) convalescents are at risk of developing a de novo mental health disorder or worsening of a pre-existing one. COVID-19 outpatients have been less well characterized than their hospitalized counterparts. The objectives of our study were to identify indicators for poor mental health following COVID-19 outpatient management and to identify high-risk individuals.

Methods : We conducted a binational online survey study with adult non-hospitalized COVID-19 convalescents (Austria/AT: n = 1,157, Italy/IT: n = 893). Primary endpoints were positive screening for depression and anxiety (Patient Health Questionnaire; PHQ-4) and self-perceived overall mental health (OMH) and quality of life (QoL) rated with 4 point Likert scales. Psychosocial stress was surveyed with a modified PHQ stress module. Associations of the mental health and QoL with socio-demographic, COVID-19 course, and recovery variables were assessed by multi-parameter Random Forest and Poisson modeling. Mental health risk subsets were defined by self-organizing maps (SOMs) and hierarchical clustering algorithms. The survey analyses are publicly available (https://im2-ibk.shinyapps.io/mental_health_dashboard/).

Results : Depression and/or anxiety before infection was reported by 4.6% (IT)/6% (AT) of participants. At a median of 79 days (AT)/96 days (IT) post-COVID-19 onset, 12.4% (AT)/19.3% (IT) of subjects were screened positive for anxiety and 17.3% (AT)/23.2% (IT) for depression. Over one-fifth of the respondents rated their OMH (AT: 21.8%, IT: 24.1%) or QoL (AT: 20.3%, IT: 25.9%) as fair or poor. Psychosocial stress, physical performance loss, high numbers of acute and sub-acute COVID-19 complaints, and the presence of acute and sub-acute neurocognitive symptoms (impaired concentration, confusion, and forgetfulness) were the strongest correlates of deteriorating mental health and poor QoL. In clustering analysis, these variables defined subsets with a particularly high propensity of post-COVID-19 mental health impairment and decreased QoL. Pre-existing depression or anxiety (DA) was associated with an increased symptom burden during acute COVID-19 and recovery.

Conclusion : Our study revealed a bidirectional relationship between COVID-19 symptoms and mental health. We put forward specific acute symptoms of the disease as "red flags" of mental health deterioration, which should prompt general practitioners to identify non-hospitalized COVID-19 patients who may benefit from early psychological and psychiatric intervention.

Clinical Trial Registration : [ClinicalTrials.gov], identifier [NCT04661462].

Hüfner Katharina, Tymoszuk Piotr, Ausserhofer Dietmar, Sahanic Sabina, Pizzini Alex, Rass Verena, Galffy Matyas, Böhm Anna, Kurz Katharina, Sonnweber Thomas, Tancevski Ivan, Kiechl Stefan, Huber Andreas, Plagg Barbara, Wiedermann Christian J, Bellmann-Weiler Rosa, Bachler Herbert, Weiss Günter, Piccoliori Giuliano, Helbok Raimund, Loeffler-Ragg Judith, Sperner-Unterweger Barbara

2022

COVID-19, SARS-CoV-2, anxiety, depression, long COVID, machine learning, mental stress, neurocognitive

Ophthalmology

Applications of Artificial Intelligence in Myopia: Current and Future Directions.

In Frontiers in medicine
With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.
Zhang Chenchen, Zhao Jing, Zhu Zhe, Li Yanxia, Li Ke, Wang Yuanping, Zheng Yajuan

2022

artificial intelligence, deep learning, machine learning, myopia, telemedicine

General

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network.

In IEEE access : practical innovations, open solutions
Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.
Chharia Aviral, Upadhyay Rahul, Kumar Vinay, Cheng Chao, Zhang Jing, Wang Tianyang, Xu Min

2022

COVID-19, Deep learning, computer-aided diagnosis, medical imaging, pandemics

Radiology

COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning.

In Frontiers in medicine
The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of computed tomography (CT) imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a deep neural network tailored for detection of COVID-19 cases from chest CT images, along with a large curated benchmark dataset comprising 1,489 patient cases as part of the open-source COVID-Net initiative. However, one potential limiting factor is restricted data quantity and diversity given the single nation patient cohort used in the study. To address this limitation, in this study we introduce enhanced deep neural networks for COVID-19 detection from chest CT images which are trained using a large, diverse, multinational patient cohort. We accomplish this through the introduction of two new CT benchmark datasets, the largest of which comprises a multinational cohort of 4,501 patients from at least 16 countries. To the best of our knowledge, this represents the largest, most diverse multinational cohort for COVID-19 CT images in open-access form. Additionally, we introduce a novel lightweight neural network architecture called COVID-Net CT S, which is significantly smaller and faster than the previously introduced COVID-Net CT architecture. We leverage explainability to investigate the decision-making behavior of the trained models and ensure that decisions are based on relevant indicators, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The best-performing deep neural network in this study achieved accuracy, COVID-19 sensitivity, positive predictive value, specificity, and negative predictive value of 99.0%/99.1%/98.0%/99.4%/99.7%, respectively. Moreover, explainability-driven performance validation shows consistency with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 and the associated benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.
Gunraj Hayden, Sabri Ali, Koff David, Wong Alexander

2021

COVID-19, SARS-CoV-2, computed tomography, deep learning, image classification, pneumonia, radiology

Public Health

Can anti-parasitic drugs help control COVID-19?

In Future virology
Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.
Panahi Yasin, Dadkhah Masoomeh, Talei Sahand, Gharari Zahra, Asghariazar Vahid, Abdolmaleki Arash, Matin Somayeh, Molaei Soheila

2022-Mar

COVID-19, SARS-CoV-2, anti-parasitic drugs, artificial intelligence, drug repositioning, epidemic, pandemic

General

Exploring New Characteristics: Using Deep Learning and 3D Reconstruction to Compare the Original COVID-19 and Its Delta Variant Based on Chest CT.

In Frontiers in molecular biosciences
Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient's lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI). Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0-3 days), progressive and peak stage (4-16 days), and absorption stage (17-42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17-30] vs. 39 [31-44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.
Bai Na, Lin Ruikai, Wang Zhiwei, Cai Shengyan, Huang Jianliang, Su Zhongrui, Yao Yuanzhen, Wen Fang, Li Han, Huang Yuxin, Zhao Yi, Xia Tao, Lei Mingsheng, Yang Weizhen, Qiu Zhaowen

2022

Delta Variant, Original COVID-19, chest CT, deep learning, quantitative analysis, stereoscopic segmentation, three-dimensional reconstruction

General

A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit.

In Digital health

Background : Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy.

Methods : We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead.

Results : All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R ² as model assessment metrics showed that ANFIS model had better predictive power among all models.

Conclusion : Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.

Shafiekhani Sajad, Namdar Peyman, Rafiei Sima

Coronavirus disease 2019 (COVID-19), adaptive neuro-fuzzy inference system (ANFIS), demand forecasting, hospitalization, intensive care unit (ICU), long short-term memory (LSTM) network

General

RNA editing increases the nucleotide diversity of SARS-CoV-2 in human host cells.

In PLoS genetics ; h5-index 96.0
SARS-CoV-2 is a positive-sense, single-stranded RNA virus responsible for the COVID-19 pandemic. It remains unclear whether and to what extent the virus in human host cells undergoes RNA editing, a major RNA modification mechanism. Here we perform a robust bioinformatic analysis of metatranscriptomic data from multiple bronchoalveolar lavage fluid samples of COVID-19 patients, revealing an appreciable number of A-to-I RNA editing candidate sites in SARS-CoV-2. We confirm the enrichment of A-to-I RNA editing signals at these candidate sites through evaluating four characteristics specific to RNA editing: the inferred RNA editing sites exhibit (i) stronger ADAR1 binding affinity predicted by a deep-learning model built from ADAR1 CLIP-seq data, (ii) decreased editing levels in ADAR1-inhibited human lung cells, (iii) local clustering patterns, and (iv) higher RNA secondary structure propensity. Our results have critical implications in understanding the evolution of SARS-CoV-2 as well as in COVID-19 research, such as phylogenetic analysis and vaccine development.
Peng Xinxin, Luo Yikai, Li Hongyue, Guo Xuejiao, Chen Hu, Ji Xuwo, Liang Han

2022-Mar-30

General

A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients.

In Medical & biological engineering & computing ; h5-index 32.0
In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests from 1,004 patients, of which 18% were admitted to intensive care unit (ICU), we created four ML models, by adopting a robust development procedure which was designed to minimize risks of bias and over-fitting, according to reference guidelines. The best model, a support vector machine, had an AUC of .85, a Brier score of .14, and a standardized net benefit of .69: these scores indicate that the model performed well over a variety of prediction criteria. We also conducted an interpretability study to back up our findings, showing that the data on which the developed model is based is consistent with the current medical literature. This also demonstrates that CBC data and ML methods can be used to predict COVID-19 patients' ICU admission at a relatively low cost: in particular, since CBC data can be quickly obtained by means of routine blood exams, our models could be used in resource-constrained settings and provide health practitioners with rapid and reliable indications.
Famiglini Lorenzo, Campagner Andrea, Carobene Anna, Cabitza Federico

2022-Mar-30

COVID-19, Complete blood count, Machine learning, Prognostic models, eXplainable AI

General

Effectiveness evaluation of different feature extraction methods for classification of covid-19 from computed tomography images: A high accuracy classification study.

In Biomedical signal processing and control
Rapid diagnosis of the Covid-19 disease is the best way to prevent infection. In this paper, it is proposed to use machine learning methods to aid diagnoses quickly Covid-19 and focused on effect of several features on classification accuracy. In the proposed method 746 axial computed tomography (CT) images of the lung; 349 Covid-19 (positives) and 397 non-Covid-19 (negative) are used. Gray-level texture, shape and first order statistical features were extracted from the images. The feature vector for model training is constructed with one feature group or combination of more than one group. We then classified with Support Vector Machine, Random Forest, k-nearest neighbor and XGBoost classifier models. The hyperparameter of the models were controlled by the tuning test. Experimental results obtained with 10-fold cross-validation. The results of cross-validation verified with the additionally independent test. The best overall accuracy was 98.65% with first order statistics features classified with XGBoost. In the gray level features, the best individual results given by GLSZM as 81.25%, and the best combination result is with GLDM, GLRLM and GLSZM features as 85.52%. An important finding of this paper is that, for Covid-19 classification, the shape and first order statistics features are more valuable than gray level features. The proposed results compared with the literature studies under some Covid-19 dataset for accuracy, precision, sensitivity and F1-score metrics. Also, the literature studies which used the different Covid-19 dataset were compared with the proposed study. Our results have the significant superiority when compared with the literature studies.
Al-Areqi Farid, Konyar Mehmet Zeki

2022-Jul

CT images, Covid-19, Diagnosis, Features, Machine learning

General

[Feasibility of using artificial intelligence for screening COVID-19 patients in ParaguayViabilidade do uso de inteligência artificial na triagem de pacientes com COVID-19 no Paraguai].

In Revista panamericana de salud publica = Pan American journal of public health

Objective : Study the feasibility of using artificial intelligence as a sensitive and specific method for COVID-19 screening in patients with respiratory conditions, using chest CT scan images and a telemedicine platform.

Methods : From March 2020 to June 2021, the authors conducted an observational descriptive multicenter feasibility study based on artificial intelligence (AI) for COVID-19 screening using chest images of patients with respiratory conditions who presented at public hospitals. The AI platform was used to diagnose chest CT scan images; this was then compared with molecular diagnosis (RT-PCR) to determine whether they matched and to analyze the feasibility of AI for screening patients with suspected COVID-19. A telemedicine platform was used to send images and diagnostic results.

Results : Screening of 3 514 patients with a suspected COVID-19 diagnosis was performed in 14 hospitals around the country. Most patients were aged 27 to 59 years, followed by those over 60. The average age was 48.6 years; 52.8% were male. The most frequent findings were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, and diffuse ground glass opacity, among others. There was an average of 93% matching and 7% mismatching between images analyzed by AI and RT-PCR. Sensitivity and specificity of the AI system, obtained by comparing AI and RT-PCR screening results, were 93% and 80% respectively.

Conclusions : The use of sensitive and specific AI for stratified rapid detection of COVID-19 in patients with respiratory conditions by using chest CT scan images and a telemedicine platform in public hospitals in Paraguay is feasible.

Galván Pedro, Fusillo José, González Felipe, Vukujevic Oraldo, Recalde Luciano, Rivas Ronald, Ortellado José, Portillo Juan, Borba Julio, Hilario Enrique

2022

COVID-19, Paraguay, Screening, artificial intelligence, digital technology, telediagnostics, telemedicine

General

Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq.

In bioRxiv : the preprint server for biology
Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-Seq for autoantigen discovery, including our previous work (Vazquez et al. 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and finally, mild and severe forms of COVID19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.
Vazquez Sara E, Mann Sabrina A, Bodansky Aaron, Kung Andrew F, Quandt Zoe, Ferré Elise M N, Landegren Nils, Eriksson Daniel, Bastard Paul, Zhang Shen-Ying, Liu Jamin, Mitchell Anthea, Mandel-Brehm Caleigh, Miao Brenda, Sowa Gavin, Zorn Kelsey, Chan Alice Y, Shimizu Chisato, Tremoulet Adriana, Lynch Kara, Wilson Michael R, Kampe Olle, Dobbs Kerry, Delmonte Ottavia M, Notarangelo Luigi D, Burns Jane C, Casanova Jean-Laurent, Lionakis Michail S, Torgerson Troy R, Anderson Mark S, DeRisi Joseph L

2022-Mar-24

Public Health

Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation.

In IEEE journal of biomedical and health informatics
Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical in public health, especially during the COVID-19 pandemic, as institutions and government agencies rely on death reports to analyze vital statistics to formulate responses to communicable diseases. Unfortunately, determining the true causes of death is challenging even for experienced physicians. The novel coronavirus and its variants may further complicate the task, as physicians and researchers are still investigating COVID-related complications. To facilitate physicians in accurately reporting causes of death, an advanced artificial intelligence (AI) approach is presented to determine a chronically ordered sequence of clinical conditions that lead to death (named as the causal sequence of death), based on decedents last hospital discharge record. The key technical issue of this problem is to learn the causal relationship between clinical codes and to identify death-related conditions. Specifically, three challenges in determining the causal sequence of death are identified: multiple clinical coding system versions, medical domain knowledge constraint, and data interoperability. To overcome the first challenge, the advanced neural machine translation models with various attention mechanisms are applied to generate target sequences. The BLEU (BiLingual Evaluation Understudy) score is used along with three accuracy metrics to evaluate the quality of generated sequences. We achieve state-of-art results. To address the second challenge, expert-verified medical domain knowledge is incorporated as constraints during cause of death sequence generation. Lastly, a Fast Healthcare Interoperability Resources (FHIR) interface demonstrates the usability of this work in clinical practice. During this ongoing pandemic, this work can potentially benefit physicians in understanding comorbidities contributing to coronavirus morbidity and mortality.
Zhu Yuanda, Sha Ying, Wu Hang, Li Mai, Hoffman Ryan, Wang May Dongmei

2022-Mar-29

General

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.

In Journal of medical systems ; h5-index 48.0
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
Roland Theresa, Böck Carl, Tschoellitsch Thomas, Maletzky Alexander, Hochreiter Sepp, Meier Jens, Klambauer Günter

2022-Mar-29

Blood test, COVID-19, Domain shift, Machine learning

General

Prognosis patients with COVID-19 using deep learning.

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

BACKGROUND : The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards.

METHODS : For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour.

RESULTS : A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93.

CONCLUSION : The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.

Guadiana-Alvarez José Luis, Hussain Fida, Morales-Menendez Ruben, Rojas-Flores Etna, García-Zendejas Arturo, Escobar Carlos A, Ramírez-Mendoza Ricardo A, Wang Jianhong

2022-Mar-26

COVID-19, Coronavirus, Deep learning, Mortality risk prediction, Prognosis, Random forest

General

Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.

In Neurocomputing
Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpaşa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.
Ali Ahmed Sara Atito, Yavuz Mehmet Can, Şen Mehmet Umut, Gülşen Fatih, Tutar Onur, Korkmazer Bora, Samancı Cesur, Şirolu Sabri, Hamid Rauf, Eryürekli Ali Ergun, Mammadov Toghrul, Yanikoglu Berrin

2022-Jun-01

COVID-19, Computed Tomography, Deep Learning, Detection, Ensemble

General

Towards applying Internet of Things and Machine Learning for the Risk Prediction of COVID-19 in pandemic situation using Naive Bayes Classifier for improving Accuracy.

In Materials today. Proceedings
Infections such as COVID-19 are affecting the entire world and measures such as social distancing can be done so that the contact among people is reduced. IoT devices usage keeps on increasing every day thereby connecting the environments physically. Among the current technologies, machine learning can be employed along with IoT devices. Predicting the risk related with COVID-19, a novel method employing machine learning is proposed. Random forest and Naive Bayes classifier are used for the prediction from the data collected with the help of sensors. Groups of people are recognized and the disease impact can be reduced for the particular group with more population. The accuracy of RF is 97% and for NB it is 99%.
Deepa N, Sathya Priya J, Devi T

2022-Mar-24

Accuracy, COVID-19, Naive Bayes Classifier, Prediction, Random Forest

General

An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection

ArXiv Preprint
Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor mask-wearing states by utilizing suitable automatic detectors. However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Then we use BiFPN as the feature fusion neck. Furthermore, we replace the loss function of localization with -CIoU to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results show the performance and effectiveness of the proposed model. On the basis of the original YOLOv5 model, our work increases the inference speed by 28.3% while still improving the precision by 0.58% on the AIZOO face mask dataset. It achieves a mean average precision of 95.2%, which is 4.4% higher than the baseline and is also more accurate compared with other existing models.
Sheng Xu

2022-03-30

Radiology

Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011-2021: a bibliometric analysis of highly cited papers.

In Japanese journal of radiology

PURPOSE : To spotlight the trends and hot topics looming from the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging with bibliometric analysis.

MATERIALS AND METHODS : Based on the Essential Science Indicators, this study employed a bibliometric method to examine the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging in Web of Science (WoS) Categories, both quantitatively and qualitatively. In total, 1325 highly cited papers were retrieved and assessed spanning from the years of 2011 to 2021. In particular, the bibliometric information of the highly cited papers based on WoS database such as the main publication venues, the most productive countries, and the top cited publications was presented. An Abstract corpus was built to help identify the most frequently explored topics. VoSviewer was used to visualize the co-occurrence networks of author keywords.

RESULTS : The top three active journals are Neuroimage, Radiology and IEEE T Med Imaging. The United States, Germany and England have the most influential publications. The top cited publications unrelated to COVID-19 can be grouped in three categories: recommendations or guidelines, processing software, and analysis methods. The top cited publications on COVID-19 are dominantly in China. The most frequently explored topics based on the Abstract corpus and the author keywords with the great link strengths overlap to a great extent. Specifically, phrases such as magnetic resonance imaging, deep learning, prostate cancer, chest CT, computed tomography, CT images, coronavirus disease, convolutional neural network(s) are among the most frequently mentioned.

CONCLUSION : The bibliometric analysis of the highly cited papers provided the most updated trends and hot topics which may provide insights and research directions for medical researchers and healthcare practitioners in the future.

Yan Sheng, Zhang Huiting, Wang Jun

2022-Mar-28

Bibliometric analysis, Highly cited papers, Medical imaging, Nuclear medicine, Radiology

General

Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images.

In Informatics in medicine unlocked
COVID-19 cases are putting pressure on healthcare systems all around the world. Due to the lack of available testing kits, it is impractical for screening every patient with a respiratory ailment using traditional methods (RT-PCR). In addition, the tests have a high turn-around time and low sensitivity. Detecting suspected COVID-19 infections from the chest X-ray might help isolate high-risk people before the RT-PCR test. Most healthcare systems already have X-ray equipment, and because most current X-ray systems have already been computerized, there is no need to transfer the samples. The use of a chest X-ray to prioritize the selection of patients for subsequent RT-PCR testing is the motivation of this work. Transfer learning (TL) with fine-tuning on deep convolutional neural network-based ResNet50 model has been proposed in this work to classify COVID-19 patients from the COVID-19 Radiography Database. Ten distinct pre-trained weights, trained on varieties of large-scale datasets using various approaches such as supervised learning, self-supervised learning, and others, have been utilized in this work. Our proposed i N a t 2021 _ M i n i _ S w A V _ 1 k model, pre-trained on the iNat2021 Mini dataset using the SwAV algorithm, outperforms the other ResNet50 TL models. For COVID instances in the two-class (Covid and Normal) classification, our work achieved 99.17% validation accuracy, 99.95% train accuracy, 99.31% precision, 99.03% sensitivity, and 99.17% F1-score. Some domain-adapted ( I m a g e N e t _ C h e s t X - r a y 14 ) and in-domain (ChexPert, ChestX-ray14) models looked promising in medical image classification by scoring significantly higher than other models.
Hossain Md Belal, Iqbal S M Hasan Sazzad, Islam Md Monirul, Akhtar Md Nasim, Sarker Iqbal H

2022

COVID-19, Deep learning, ResNet50, Transfer learning

General

Evaporation of liquid nanofilms: A minireview.

In Physics of fluids (Woodbury, N.Y. : 1994)
Evaporation of virus-loaded droplets and liquid nanofilms plays a significant role in the pandemic of COVID-19. The evaporation mechanism of liquid nanofilms has attracted much attention in recent decades. In this minireview, we first introduce the relationship between the evaporation process of liquid nanofilms and the pandemic of COVID-19. Then, we briefly provide the frontiers of liquid droplet/nanofilm evaporation on solid surfaces. In addition, we discuss the potential application of machine learning in liquid nanofilm evaporation studies, which is expected to be helpful to build up a more accurate molecular model and to investigate the evaporation mechanism of liquid nanofilms on solid surfaces.
Zhang Kaixuan, Fang Wei, Lv Cunjing, Feng Xi-Qiao

2022-Feb

General

Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies.

In Technology in society
As the COVID-19 pandemic expanded over the globe, governments implemented a series of technological measures to prevent the disease's spread. The development of the COVID Tracing Application (CTA) was one of these measures. In this study, we employed bibliometric and topic-based content analysis to determine the most significant entities and research topics. Additionally, we identified significant privacy concerns posed by CTAs, which gather, store, and analyze data in partnership with large technology corporations using proximity measurement technologies, artificial intelligence, and blockchain. We examined a series of key privacy threats identified in our study. These privacy risks include anti-democratic and discriminatory behaviors, politicization of care, derogation of human rights, techno governance, citizen distrust and refusal to adopt, citizen surveillance, and mandatory legislation of the apps' installation. Finally, sixteen research gaps were identified. Then, based on the identified theoretical gaps, we recommended fourteen prospective study strands. Theoretically, this study contributes to the growing body of knowledge about the privacy of mobile health applications that are embedded with cutting-edge technologies and are employed during global pandemics.
Saheb Tahereh, Sabour Elham, Qanbary Fatimah, Saheb Tayebeh

2022-May

Covid, Ethics, Privacy, Proximity measurement, Surveillance, Tracing apps

General

A deep learning-based framework for detecting COVID-19 patients using chest X-rays.

In Multimedia systems
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused outbreaks of new coronavirus disease (COVID-19) around the world. Rapid and accurate detection of COVID-19 coronavirus is an important step in limiting the spread of the COVID-19 epidemic. To solve this problem, radiography techniques (such as chest X-rays and computed tomography (CT)) can play an important role in the early prediction of COVID-19 patients, which will help to treat patients in a timely manner. We aimed to quickly develop a highly efficient lightweight CNN architecture for detecting COVID-19-infected patients. The purpose of this paper is to propose a robust deep learning-based system for reliably detecting COVID-19 from chest X-ray images. First, we evaluate the performance of various pre-trained deep learning models (InceptionV3, Xception, MobileNetV2, NasNet and DenseNet201) recently proposed for medical image classification. Second, a lightweight shallow convolutional neural network (CNN) architecture is proposed for classifying X-ray images of a patient with a low false-negative rate. The data set used in this work contains 2,541 chest X-rays from two different public databases, which have confirmed COVID-19 positive and healthy cases. The performance of the proposed model is compared with the performance of pre-trained deep learning models. The results show that the proposed shallow CNN provides a maximum accuracy of 99.68% and more importantly sensitivity, specificity and AUC of 99.66%, 99.70% and 99.98%. The proposed model has fewer parameters and low complexity compared to other deep learning models. The experimental results of our proposed method show that it is superior to the existing state-of-the-art methods. We believe that this model can help healthcare professionals to treat COVID-19 patients through improved and faster patient screening.
Asif Sohaib, Zhao Ming, Tang Fengxiao, Zhu Yusen

2022-Mar-22

COVID-19 detection , Chest X-ray, Convolutional neural network (CNN), Deep learning, Transfer learning

General

A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization.

In PeerJ

Background and aim : COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization.

Methods : Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant.

Results : A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST.

Conclusion : SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.

Lipták Peter, Banovcin Peter, Rosoľanka Róbert, Prokopič Michal, Kocan Ivan, Žiačiková Ivana, Uhrik Peter, Grendar Marian, Hyrdel Rudolf

2022

Artificial intelligence, COVID-19, Hospitalization, Liver, Machine learning, Predictors, Random forest, SARS-CoV-2, Symptoms

General

An adaptive feature extraction method for classification of Covid-19 X-ray images.

In Signal, image and video processing
This study aims to detect Covid-19 disease in the fastest and most accurate way from X-ray images by developing a new feature extraction method and deep learning model . Partitioned Tridiagonal Enhanced Multivariance Products Representation (PTMEMPR) method is proposed as a new feature extraction method by using matrix partition in TMEMPR method which is known as matrix decomposition method in the literature. The proposed method which provides 99.9% data reduction is used as a preprocessing method in the scheme of the Covid-19 diagnosis. To evaluate the performance of the proposed method, it is compared with the state-of-the-art feature extraction methods which are Singular Value Decomposition(SVD), Discrete Wavelet Transform(DWT) and Discrete Cosine Transform(DCT). Also new deep learning models which are called FSMCov, FSMCov-N and FSMCov-L are developed in this study. The experimental resu