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General General

Deep Learning in Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs on CXR and CT Images.

In Journal of medical and biological engineering

Purpose : In this paper, the transfer learning method has been implemented to chest X-ray (CXR) and computed tomography (CT) bio-images of diverse kinds of lungs maladies, including CORONAVIRUS 2019 (COVID-19). COVID-19 identification is a difficult assignment that constantly demands a careful analysis of a patient's clinical images, as COVID-19 is found to be very alike to pneumonic viral lung infection. In this paper, a transfer learning model to accelerate prediction processes and to assist medical professionals is proposed. Finally, the main purpose is to do an accurate classification between Covid-19, pneumonia and, healthy lungs using CXR and CT images.

Methods : Learning transfer gives the possibility to find out about this new illness COVID-19, using the knowledge we have about the pneumonia virus. This demonstrates the apprehensiveness achieved from a new architecture trained to detect virus-related pneumonia that must be transferred for COVID-19 detection. Transfer learning presents a considerable dissimilarity in results when compared to the result of traditional groupings. It is not necessary to create a separate model for the classification of COVID-19. This simplifies complicated issues by adopting the available model for COVID-19 determination. Automated diagnosis of COVID-19 using Haralick texture features is focused on segmented lung images and problematic lung patches. Lung patches are necessary for the augmentation of COVID-19 image data.

Results : The obtained outcomes are quite reliable for all distinctive processes as the proposed architecture can distinguish healthy lungs, pneumonia, COVID-19.

Conclusions : The results suggest that the implemented model is improved considering other existing models because the obtained classification accuracy is over the recently obtained results. It is a belief that the new architecture that is implemented in this study, delivers a petite step in building refined Coronavirus 2019 diagnosis architecture using CXR and CT bio-images.

Lascu Mihaela-Ruxandra

2021-Jun-10

COVID-19, Classification augmentation, Deep learning, Network architecture, Pulmonary disease detection, Segmentation, X-ray/CT imaging

General General

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning.

In Neural computing & applications

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

Madhavan Mangena Venu, Khamparia Aditya, Gupta Deepak, Pande Sagar, Tiwari Prayag, Hossain M Shamim

2021-Jun-09

CNN architecture, COVID-19, Res-CovNet, ResNet-50, Transfer learning, X-ray images

Radiology Radiology

SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.

In Pattern recognition

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.

Zhao Shixuan, Li Zhidan, Chen Yang, Zhao Wei, Xie Xingzhi, Liu Jun, Zhao Di, Li Yongjie

2021-Jun-10

Attention mechanism, COVID-19, Convolutional neural network, Lung opacification, Segmentation

General General

Inferring statistical trends of the COVID19 pandemic from current data.Where probability meets fuzziness.

In Information sciences

We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: 1) a basic tool to deal with samples that are both truncated and non independently drawn, and 2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and ii) the out-performance of the model inference method according to conventional indicators.

Apolloni Bruno

2021-Jun-09

COVID19 pandemic, Explainable Artificial Intelligence, shifted-Pareto distribution, statistics from non-iid samples, two-phase processes

Cardiology Cardiology

AI-guided discovery of the invariant host response to viral pandemics.

In EBioMedicine

BACKGROUND : Coronavirus Disease 2019 (Covid-19) continues to challenge the limits of our knowledge and our healthcare system. Here we sought to define the host immune response, a.k.a, the "cytokine storm" that has been implicated in fatal COVID-19 using an AI-based approach.

METHOD : Over 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a 'seed' gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. An AI-based approach was used to explore the utility of the signature in navigating the uncharted territory of Covid-19, setting therapeutic goals, and finding therapeutic solutions.

FINDINGS : The 166-gene signature was surprisingly conserved across all viral pandemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViP and severe-ViP signatures, respectively. The ViP signatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determine severity/fatality. Precise therapeutic goals could be formulated; these goals were met in high-dose SARS-CoV-2-challenged hamsters using either neutralizing antibodies that abrogate SARS-CoV-2•ACE2 engagement or a directly acting antiviral agent, EIDD-2801. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine prognosticated disease severity.

INTERPRETATION : The ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs.

FUNDING : This work was supported by the National Institutes for Health (NIH) [grants CA151673 and GM138385 (to DS) and AI141630 (to P.G), DK107585-05S1 (SD) and AI155696 (to P.G, D.S and S.D), U19-AI142742 (to S.

C, CCHI : Cooperative Centers for Human Immunology)]; Research Grants Program Office (RGPO) from the University of California Office of the President (UCOP) (R00RG2628 & R00RG2642 to P.G, D.S and S.D); the UC San Diego Sanford Stem Cell Clinical Center (to P.G, D.S and S.D); LJI Institutional Funds (to S.C); the VA San Diego Healthcare System Institutional funds (to L.C.A). GDK was supported through The American Association of Immunologists Intersect Fellowship Program for Computational Scientists and Immunologists.

ONE SENTENCE SUMMARY : The host immune response in COVID-19.

Sahoo Debashis, Katkar Gajanan D, Khandelwal Soni, Behroozikhah Mahdi, Claire Amanraj, Castillo Vanessa, Tindle Courtney, Fuller MacKenzie, Taheri Sahar, Rogers Thomas F, Beutler Nathan, Ramirez Sydney I, Rawlings Stephen A, Pretorius Victor, Smith Davey M, Burton Dennis R, Alexander Laura E Crotty, Duran Jason, Crotty Shane, Dan Jennifer M, Das Soumita, Ghosh Pradipta

2021-Jun-11

Angiotensin converting enzyme (ACE)-2, Artificial intelligence/machine learning, Boolean equivalent clusters, Coronavirus COVID-19, Immune response, Interleukin 15 (IL15), Lung alveoli, Natural Killer (NK) cells

General General

Improved individual and population-level HbA1c estimation using CGM data and patient characteristics.

In Journal of diabetes and its complications ; h5-index 41.0

Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.

Grossman Joshua, Ward Andrew, Crandell Jamie L, Prahalad Priya, Maahs David M, Scheinker David

2021-May-17

Continuous glucose monitoring, HbA1c estimation

oncology Oncology

NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis.

In Artificial intelligence in medicine ; h5-index 34.0

During pandemics (e.g., COVID-19) physicians have to focus on diagnosing and treating patients, which often results in that only a limited amount of labeled CT images is available. Although recent semi-supervised learning algorithms may alleviate the problem of annotation scarcity, limited real-world CT images still cause those algorithms producing inaccurate detection results, especially in real-world COVID-19 cases. Existing models often cannot detect the small infected regions in COVID-19 CT images, such a challenge implicitly causes that many patients with minor symptoms are misdiagnosed and develop more severe symptoms, causing a higher mortality. In this paper, we propose a new method to address this challenge. Not only can we detect severe cases, but also detect minor symptoms using real-world COVID-19 CT images in which the source domain only includes limited labeled CT images but the target domain has a lot of unlabeled CT images. Specifically, we adopt Network-in-Network and Instance Normalization to build a new module (we term it NI module) and extract discriminative representations from CT images from both source and target domains. A domain classifier is utilized to implement infected region adaptation from source domain to target domain in an Adversarial Learning manner, and learns domain-invariant region proposal network (RPN) in the Faster R-CNN model. We call our model NIA-Network (Network-in-Network, Instance Normalization and Adversarial Learning), and conduct extensive experiments on two COVID-19 datasets to validate our approach. The experimental results show that our model can effectively detect infected regions with different sizes and achieve the highest diagnostic accuracy compared with existing SOTA methods.

Li Wei, Chen Jinlin, Chen Ping, Yu Lequan, Cui Xiaohui, Li Yiwei, Cheng Fang, Ouyang Wen

2021-Jul

Adversarial learning, COVID-19 diagnosis, Instance normalization, Network-in-Network, Semi-supervised learning

Public Health Public Health

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence-Belgium as a study case.

In International journal of health geographics

BACKGROUND : The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection.

METHODS : To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio-temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence.

RESULTS : Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence.

CONCLUSION : Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.

Dellicour Simon, Linard Catherine, Van Goethem Nina, Da Re Daniele, Artois Jean, Bihin Jérémie, Schaus Pierre, Massonnet François, Van Oyen Herman, Vanwambeke Sophie O, Speybroeck Niko, Gilbert Marius

2021-Jun-14

Belgium, Boosted regression trees, COVID-19, Hospitalisation incidence, Spatial covariates, Temporal covariates

General General

On the Objective Evaluation of Post Hoc Explainers

ArXiv Preprint

Many applications of data-driven models demand transparency of decisions, especially in health care, criminal justice, and other high-stakes environments. Modern trends in machine learning research have led to algorithms that are increasingly intricate to the degree that they are considered to be black boxes. In an effort to reduce the opacity of decisions, methods have been proposed to construe the inner workings of such models in a human-comprehensible manner. These post hoc techniques are described as being universal explainers - capable of faithfully augmenting decisions with algorithmic insight. Unfortunately, there is little agreement about what constitutes a "good" explanation. Moreover, current methods of explanation evaluation are derived from either subjective or proxy means. In this work, we propose a framework for the evaluation of post hoc explainers on ground truth that is directly derived from the additive structure of a model. We demonstrate the efficacy of the framework in understanding explainers by evaluating popular explainers on thousands of synthetic and several real-world tasks. The framework unveils that explanations may be accurate but misattribute the importance of individual features.

Zachariah Carmichael, Walter J. Scheirer

2021-06-15

General General

Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction

ArXiv Preprint

The rapidly evolving literature of COVID-19 related articles makes it challenging for NLP models to be effectively trained for information retrieval and extraction with the corresponding labeled data that follows the current distribution of the pandemic. On the other hand, due to the uncertainty of the situation, human experts' supervision would always be required to double check the decision making of these models highlighting the importance of interpretability. In the light of these challenges, this study proposes an interpretable self-supervised multi-task learning model to jointly and effectively tackle the tasks of information retrieval (IR) and extraction (IE) during the current emergency health crisis situation. Our results show that our model effectively leverage the multi-task and self-supervised learning to improve generalization, data efficiency and robustness to the ongoing dataset shift problem. Our model outperforms baselines in IE and IR tasks, respectively by micro-f score of 0.08 (LCA-F score of 0.05), and MAP of 0.05 on average. In IE the zero- and few-shot learning performances are on average 0.32 and 0.19 micro-f score higher than those of the baselines.

Nima Ebadi, Peyman Najafirad

2021-06-15

General General

COVID-19 Diagnosis on CT Scan Images Using a Generative Adversarial Network and Concatenated Feature Pyramid Network with an Attention Mechanism.

In Medical physics ; h5-index 59.0

OBJECTIVE : Coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of infections and deaths. Efficient diagnostic methods could help curb its global spread. The purpose of this study was to develop and evaluate a method for accurately diagnosing COVID-19 based on computed tomography (CT) scans in real time.

METHODS : We propose an architecture named "concatenated feature pyramid network" ("Concat-FPN") with an attention mechanism, by concatenating feature maps of multiple. The proposed architecture is then used to form two networks, which we call COVID-CT-GAN and COVID-CT-DenseNet, the former for data augmentation and the latter for data classification.

RESULTS : The proposed method is evaluated on 3 different numbers of magnitude of COVID-19 CT datasets. Compared with the method without GANs for data augmentation or the original network auxiliary classifier generative adversarial network, COVID-CT-GAN increases the accuracy by 2% to 3%, the recall by 2% to 4%, the precision by 1% to 3%, the F1-score by 1% to 3%, and the area under the curve by 1% to 4%. Compared with the original network DenseNet-201, COVID-CT-DenseNet increases the accuracy by 1% to 3%, the recall by 4% to 9%, the precision by 1%, the F1-score by 1% to 3%, and the area under the curve by 2%.

CONCLUSION : The experimental results show that our method improves the efficiency of diagnosing COVID-19 on CT images, and helps overcome the problem of limited training data when using deep learning methods to diagnose COVID-19.

SIGNIFICANCE : Our method can help clinicians build deep learning models using their private datasets to achieve automatic diagnosis of COVID-19 with a high precision.

Li Zonggui, Zhang Junhua, Li Bo, Gu Xiaoying, Luo Xudong

2021-Jun-12

COVID-19, CT images, attention mechanism, concatenated feature pyramid network, generative adversarial network

Public Health Public Health

COVID-19 pandemic in Africa: Is it time for water, sanitation and hygiene to climb up the ladder of global priorities?

In The Science of the total environment

In the current pandemic context, it is necessary to remember the lessons learned from previous outbreaks in Africa, where the incidence of other diseases could rise if most resources are directed to tackle the emergency. Improving the access to water, sanitation and hygiene (WASH) could be a win-win strategy, because the lack of these services not only hampers the implementation of preventive measures against SARS-CoV-2 (e.g. proper handwashing), but it is also connected to high mortality diseases (for example, diarrhoea and lower respiratory infections (LRI)). This study aims to build on the evidence-based link between other LRI and WASH as a proxy for exploring the potential vulnerability of African countries to COVID-19, as well as the role of other socioeconomic variables such as financial sources or demographic factors. The selected methodology combines several machine learning techniques to single out the most representative variables for the analysis, classify the countries according to their capacity to tackle public health emergencies and identify behavioural patterns for each group. Besides, conditional dependences between variables are inferred through a Bayesian network. Results show a strong relationship between low access to WASH services and high LRI mortality rates, and that migrant remittances could significantly improve the access to healthcare and WASH services. However, the role of Official Development Assistance (ODA) in enhancing WASH facilities in the most vulnerable countries cannot be disregarded, but it is unevenly distributed: for each 50-100 US$ of ODA per capita, the probability of directing more than 3 US$ to WASH ranges between 48% (Western Africa) and 8% (Central Africa).

Marcos-Garcia P, Carmona-Moreno C, López-Puga J, Ruiz-Ruano García A M

2021-Jun-03

Africa, COVID-19, Migrant remittances, Official development assistance, Respiratory infections, WASH

Pathology Pathology

Prediction of false positive SARS-CoV-2 molecular results in a high-throughput open platform system.

In The Journal of molecular diagnostics : JMD

Widespread high-throughput testing for identification of SARS-CoV-2 infection by RT-PCR has been a foundation in the response to the COVID-19 pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as testing becomes widely implemented. As testing increases, it is important to understand performance characteristics and the errors associated with these tests. Here, we investigate a high-throughput, laboratory developed SARS-CoV-2 RT-PCR assay to determine if modeling can generate quality control metrics that identify false positive (FP) results due to contamination. This study reviewed repeated clinical samples focusing on positive samples that test negative upon re-extraction and PCR, likely representing false positives. To identify and predict false positive samples, we constructed machine learning derived models based on the extraction methodology used. These models identified variables associated with false positive results across all methodologies, with sensitivities for predicting FP results ranging between 67-100%. Application of the models to all results predicted a total FP rate of 0.08% across all samples, or 2.3% of positive results, similar to reports for other RT-PCR tests for RNA viruses. These models can predict quality control parameters, enabling laboratories to generate decision trees that reduce interpretation errors, allow for automated reflex testing of samples with a high FP probability, improve workflow efficiency and increase diagnostic accuracy for patient care.

Martinez Ryan John, Pankratz Nathan, Schomaker Matthew, Daniel Jerry, Beckman Kenneth, Karger Amy Beth, Thyagarajan Bharat, Ferreri Patricia, Yohe Sophia Louise, Nelson Andrew Cook

2021-Jun-08

Pathology Pathology

Clinical utility and functionality of an artificial intelligence application to predict mortality in COVID-19: a mixed methods analysis.

In JMIR formative research

BACKGROUND : The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging AI technology in the healthcare setting has been the relative inability to translate models into clinician workflow.

OBJECTIVE : Here we demonstrate the development of a COVID-19 outcome prediction application which utilises an ANN and assesses its usability in the clinical setting.

METHODS : Usability assessment was conducted on the application followed by a semi-structured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analysed using the thematic framework method, which allowed for the development of themes from the interview narratives.

RESULTS : Thirty-one Nation Health Service (NHS) physicians at a West London teaching hospital, including foundation doctors, senior house officers, registrars, and consultants. All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 seconds (standard deviation (SD) = 10.35). Mean system usability scale (SUS) score was 91.94 (SD = 8.54), which corresponds with an adjective rating of "Excellent". The clinicians found the application intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern related to use of the application in isolation as opposed to in conjunction with other clinical parameters. However, most clinicians felt that the application could positively reinforce or validate their clinical judgement.

CONCLUSIONS : Translating AI technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web application designed to predict COVID-19 patient outcomes from an ANN.

CLINICALTRIAL :

Abdulaal Ahmed, Patel Aatish, Al-Hindawi Ahmed, Charani Esmita, Alqahtani Saleh A, Davies Gary W, Mughal Nabeela, Moore Luke Stephen Prockter

2021-May-31

Radiology Radiology

COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans.

In Frontiers in artificial intelligence

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the "COVID-FACT". COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82 % , a sensitivity of 94.55 % , a specificity of 86.04 % , and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.

Heidarian Shahin, Afshar Parnian, Enshaei Nastaran, Naderkhani Farnoosh, Rafiee Moezedin Javad, Babaki Fard Faranak, Samimi Kaveh, Atashzar S Farokh, Oikonomou Anastasia, Plataniotis Konstantinos N, Mohammadi Arash

2021

COVID-19, capsule networks, computed tomography scans, deep learning, fully automated classification

Radiology Radiology

Autonomous Robotic Point-of-Care Ultrasound Imaging for Monitoring of COVID-19-Induced Pulmonary Diseases.

In Frontiers in robotics and AI

The COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients' lungs for diagnosis and staging. An algorithm was developed for approximating the optimal position of an ultrasound probe on a patient from prior CT scans to reach predefined lung infiltrates. In the absence of prior CT scans, a deep learning method was developed for predicting 3D landmark positions of a human ribcage given a torso surface model. The landmarks, combined with the surface model, are subsequently used for estimating optimal ultrasound probe position on the patient for imaging infiltrates. These algorithms, combined with a force-displacement profile collection methodology, enabled the system to successfully image all points of interest in a simulated experimental setup with an average accuracy of 20.6 ± 14.7 mm using prior CT scans, and 19.8 ± 16.9 mm using only ribcage landmark estimation. A study on a full torso ultrasound phantom showed that autonomously acquired ultrasound images were 100% interpretable when using force feedback with prior CT and 88% with landmark estimation, compared to 75 and 58% without force feedback, respectively. This demonstrates the preliminary feasibility of the system, and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments.

Al-Zogbi Lidia, Singh Vivek, Teixeira Brian, Ahuja Avani, Bagherzadeh Pooyan Sahbaee, Kapoor Ankur, Saeidi Hamed, Fleiter Thorsten, Krieger Axel

2021

3D deep convolutional network, 3D landmark estimation, COVID-19, autonomous robotics, force feedback, point-of-care ultrasound

Public Health Public Health

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

In NPJ digital medicine

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Syrowatka Ania, Kuznetsova Masha, Alsubai Ava, Beckman Adam L, Bain Paul A, Craig Kelly Jean Thomas, Hu Jianying, Jackson Gretchen Purcell, Rhee Kyu, Bates David W

2021-Jun-10

General General

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects.

In Briefings in bioinformatics

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

Huo Lu, Jiao Li Jiao, Chen Ling, Yu Zuguo, Hutvagner Gyorgy, Li Jinyan

2021-Jun-10

COVID-19, graph-based algorithms, integrative methods, single-cell multi-omics sequencing, single-cell sequencing, variational inference

Ophthalmology Ophthalmology

Efficacy and safety of Dihydroorotate dehydrogenase (DHODH) inhibitors "Leflunomide" and "Teriflunomide" in Covid-19: A narrative review.

In European journal of pharmacology ; h5-index 57.0

Dihydroorotate dehydrogenase (DHODH) is rate-limiting enzyme in biosynthesis of pyrimidone which catalyzes the oxidation of dihydro-orotate to orotate. Orotate is utilized in the biosynthesis of uridine-monophosphate. DHODH inhibitors have shown promise as antiviral agent against Cytomegalovirus, Ebola, Influenza, Epstein Barr and Picornavirus. Anti-SARS-CoV-2 action of DHODH inhibitors are also coming up. In this review, we have reviewed the safety and efficacy of approved DHODH inhibitors (leflunomide and teriflunomide) against COVID-19. In target-centered in silico studies, leflunomide showed favorable binding to active site of MPro and spike: ACE2 interface. In artificial-intelligence/machine-learning based studies, leflunomide was among the top 50 ligands targeting spike: ACE2 interaction. Leflunomide is also found to interact with differentially regulated pathways [identified by KEGG (Kyoto Encyclopedia of Genes and Genomes) and reactome pathway analysis of host transcriptome data] in cogena based drug-repurposing studies. Based on GSEA (gene set enrichment analysis), leflunomide was found to target pathways enriched in COVID-19. In vitro, both leflunomide (EC50 41.49±8.8μmol/L) and teriflunomide (EC50 26μmol/L) showed SARS-CoV-2 inhibition. In clinical studies, leflunomide showed significant benefit in terms of decreasing the duration of viral shredding, duration of hospital stay and severity of infection. However, no advantage was seen while combining leflunomide and IFN alpha-2a among patients with prolonged post symptomatic viral shredding. Common adverse effects of leflunomide were hyperlipidemia, leucopenia, neutropenia and liver-function alteration. Leflunomide/teriflunomide may serve as an agent of importance to achieve faster virological clearance in COVID-19, however, findings needs to be validated in bigger sized placebo controlled studies.

Kaur Hardeep, Sarma Phulen, Bhattacharyya Anusuya, Sharma Saurabh, Chhimpa Neeraj, Prajapat Manisha, Prakash Ajay, Kumar Subodh, Singh Ashutosh, Singh Rahul, Avti Pramod, Thota Prasad, Medhi Bikash

2021-Jun-07

COVID-19, DHODH inhibitor, Leflunomide, SARS-CoV-2, drug repurposing, teriflunomide

Public Health Public Health

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics.

In Frontiers in research metrics and analytics

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers' decision-making on science policy and public health administration.

Wu Mengjia, Zhang Yi, Grosser Mark, Tipper Steven, Venter Deon, Lin Hua, Lu Jie

2021

COVID-19, bibliometrics, genetic research, knowledge discovery, network analytics

General General

COVID-19 pulmonary consolidations detection in chest X-ray using progressive resizing and transfer learning techniques.

In Heliyon

A viral outbreak with a lower respiratory tract febrile illness causes pulmonary syndrome named COVID-19. Pulmonary consolidations developed in the lungs of the patients are imperative factors during prognosis and diagnosis. Existing Deep Learning techniques demonstrate promising results in analyzing X-ray images when employed with Transfer Learning. However, Transfer Learning has its inherent limitations, which can be prevaricated by employing the Progressive Resizing technique. The Progressive Resizing technique reuses old computations while learning new ones in Convolution Neural Networks (CNN), enabling it to incorporate prior knowledge of the feature hierarchy. The proposed classification model can classify pulmonary consolidation into normal, pneumonia, and SARS-CoV-2 classes by analyzing X-rays images. The method exhibits substantial enhancement in classification results when the Transfer Learning technique is applied in consultation with the Progressive Resizing technique on EfficientNet CNN. The customized VGG-19 model attained benchmark scores in all evaluation criteria over the baseline VGG-19 model. GradCam based feature interpretation, coupled with X-ray visual analysis, facilitates improved assimilation of the scores. The model highlights its strength to assist medical experts in the COVID-19 identification during the prognosis and subsequently for diagnosis. Clinical implications exist in peripheral and remotely located health centers with the paucity of trained human resources to interpret radiological investigations' findings.

Bhatt Anant, Ganatra Amit, Kotecha Ketan

2021-Jun

COVID-19, Chest X-ray analysis, Progressive resizing, Pulmonary consolidations, Saliency maps, Transfer learning

General General

Use of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Analysis of Serum Peptidome to Classify and Predict Coronavirus Disease 2019 Severity.

In Open forum infectious diseases

Background : Classification and early detection of severe coronavirus disease 2019 (COVID-19) patients is required to establish an effective treatment. We tested the utility of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) to classify and predict the severity of COVID-19.

Methods : We used MALDI-TOF MS to analyze the serum peptidome from 72 patients with COVID-19 (training cohort), clinically classified as mild (28), severe (23), and critical (21), and 20 healthy controls. The resulting matrix of peak intensities was used for Machine Learning (ML) approaches to classify and predict COVID-19 severity of 22 independent patients (validation cohort). Finally, we analyzed all sera by liquid chromatography mass spectrometry (LC-MS/MS) to identify the most relevant proteins associated with disease severity.

Results : We found a clear variability of the serum peptidome profile depending on COVID-19 severity. Forty-two peaks exhibited a log fold change ≥1 and 17 were significantly different and at least 4-fold more intense in the set of critical patients than in the mild ones. The ML approach classified clinical stable patients according to their severity with 100% accuracy and correctly predicted the evolution of the nonstable patients in all cases. The LC-MS/MS identified 5 proteins that were significantly upregulated in the critical patients. They included the serum amyloid protein A2, which probably yielded the most intense peak detected by MALDI-TOF MS.

Conclusions : We demonstrate the potential of the MALDI-TOF MS as a bench to bedside technology to aid clinicians in their decision making regarding patients with COVID-19.

Gomila Rosa M, Martorell Gabriel, Fraile-Ribot Pablo A, Doménech-Sánchez Antonio, Albertí Miguel, Oliver Antonio, García-Gasalla Mercedes, Albertí Sebastián

2021-Jun

COVID-19, MALDI-TOF, machine learning, serum peptidome

General General

Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays.

In MethodsX

Deep learning and computer vision revolutionized a new method to automate medical image diagnosis. However, to achieve reliable and state-of-the-art performance, vision-based models require high computing costs and robust datasets. Moreover, even with the conventional training methods, large vision-based models still involve lengthy epochs and costly disk consumptions that can entail difficulty during deployment due to the absence of high-end infrastructures. Therefore, this method modified the training approach on a vision-based model through layer truncation, partial layer freezing, and feature fusion. The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method's effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results.•This novel method significantly reduced the model's parameter size without sacrificing much of its classification performance.•The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19.•The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices.

Montalbo Francis Jesmar P

2021

COVID-19, Deep convolutional neural networks, Feature fusion, Image classification, Medical image diagnosis

General General

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach.

In Biocybernetics and biomedical engineering

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98.

Kassania Sara Hosseinzadeh, Kassanib Peyman Hosseinzadeh, Wesolowskic Michal J, Schneidera Kevin A, Detersa Ralph

2021-Jun-05

Computer-Aided Diagnosis, Coronavirus Disease, Deep Learning, Feature Extraction, Lung Opacity, Transfer Learning

Public Health Public Health

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.

In BMJ health & care informatics

Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients.

Makridis Christos A, Strebel Tim, Marconi Vincent, Alterovitz Gil

2021-Jun

COVID-19, information science, medical informatics, patient care

General General

Screening of world approved drugs against highly dynamical spike glycoprotein of SARS-CoV-2 using CaverDock and machine learning.

In Computational and structural biotechnology journal

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulation. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer's three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate for movement inhibition. This new protocol for the rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) showed high robustness in the rate of finished calculations. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community.

Pinto Gaspar P, Vavra Ondrej, Marques Sergio M, Filipovic Jiri, Bednar David, Damborsky Jiri

2021

CaverDock, CaverWeb, Machine learning, Protein dynamics, Tunnel, Virtual screening

General General

Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models.

In Cognitive computation

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google's Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic's behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

Gomez-Cravioto Daniela A, Diaz-Ramos Ramon E, Cantu-Ortiz Francisco J, Ceballos Hector G

2021-Jun-03

Covid19, Data science, Recurrent neural networks., Time series forecasting

General General

Artificial Intelligence and technology in COVID Era: A narrative review.

In Journal of anaesthesiology, clinical pharmacology ; h5-index 25.0

Application of artificial intelligence (AI) in the medical field during the coronavirus disease 2019 (COVID-19) era is being explored further due to its beneficial aspects such as self-reported data analysis, X-ray interpretation, computed tomography (CT) image recognition, and patient management. This narrative review article included published articles from MEDLINE/PubMed, Google Scholar and National Informatics Center egov mobile apps. The database was searched for "Artificial intelligence" and "COVID-19" and "respiratory care unit" written in the English language during a period of one year 2019-2020. The relevance of AI for patients is in hands of people with digital health tools, Aarogya setu app and Smartphone technology. AI shows about 95% accuracy in detecting COVID-19-specific chest findings. Robots with AI are being used for patient assessment and drug delivery to patients to avoid the spread of infection. The pandemic outbreak has replaced the classroom method of teaching with the online execution of teaching practices and simulators. AI algorithms have been used to develop major organ tissue characterization and intelligent pain management techniques for patients. The Blue-dot AI-based algorithm helps in providing early warning signs. The AI model automatically identifies a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection sound pressure, and light level detection. There is now no looking back as AI and machine learning are to stay in the field of training, teaching, patient care, and research in the future.

Ahuja Vanita, Nair Lekshmi V

Artificial Intelligence, COVID-19, clinical research, diagnosis, disease management, teaching

General General

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions.

In Artificial intelligence review

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

Saraji Mahyar Kamali, Mardani Abbas, Köppen Mario, Mishra Arunodaya Raj, Rani Pratibha

2021-Jun-03

Adapted online education, Fuzzy sets, Hesitant fuzzy sets, Higher education institutions, Multi-criteria decision making (MCDM)

General General

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

In Annals of operations research

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.

Kumar Ajay, Choi Tsan-Ming, Wamba Samuel Fosso, Gupta Shivam, Tan Kim Hua

2021-Jun-04

Basic reproduction number, COVID-19 dynamics, Data analytics, Infection vulnerability stratification, Mathematical model, SEIR model

General General

COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network.

In Pattern recognition

Novel coronavirus 2019 (COVID-19) has spread rapidly around the world and is threatening the health and lives of people worldwide. Early detection of COVID-19 positive patients and timely isolation of the patients are essential to prevent its spread. Chest X-ray images of COVID-19 patients often show the characteristics of multifocality, bilateral hairy glass turbidity, patchy network turbidity, etc. It is crucial to design a method to automatically identify COVID-19 from chest X-ray images to help diagnosis and prognosis. Existing studies for the classification of COVID-19 rarely consider the role of attention mechanisms on the classification of chest X-ray images and fail to capture the cross-channel and cross-spatial interrelationships in multiple scopes. This paper proposes a multi-kernel-size spatial-channel attention method to detect COVID-19 from chest X-ray images. Our proposed method consists of three stages. The first stage is feature extraction. The second stage contains two parallel multi-kernel-size attention modules: multi-kernel-size spatial attention and multi-kernel-size channel attention. The two modules capture the cross-channel and cross-spatial interrelationships in multiple scopes using multiple 1D and 2D convolutional kernels of different sizes to obtain channel and spatial attention feature maps. The third stage is the classification module. We integrate the chest X-ray images from three public datasets: COVID-19 Chest X-ray Dataset Initiative, ActualMed COVID-19 Chest X-ray Dataset Initiative, and COVID-19 radiography database for evaluation. Experimental results demonstrate that the proposed method improves the performance of COVID-19 detection and achieves an accuracy of 98.2%.

Fan Yuqi, Liu Jiahao, Yao Ruixuan, Yuan Xiaohui

2021-Jun-04

Attention, Coronavirus, Deep learning, Multi-scale, X-ray images

Internal Medicine Internal Medicine

Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening.

In Medical image analysis

Chest computed tomography (CT) based analysis and diagnosis of the Coronavirus Disease 2019 (COVID-19) plays a key role in combating the outbreak of the pandemic that has rapidly spread worldwide. To date, the disease has infected more than 18 million people with over 690k deaths reported. Reverse transcription polymerase chain reaction (RT-PCR) is the current gold standard for clinical diagnosis but may produce false positives; thus, chest CT based diagnosis is considered more viable. However, accurate screening is challenging due to the difficulty in annotation of infected areas, curation of large datasets, and the slight discrepancies between COVID-19 and other viral pneumonia. In this study, we propose an attention-based end-to-end weakly supervised framework for the rapid diagnosis of COVID-19 and bacterial pneumonia based on multiple instance learning (MIL). We further incorporate unsupervised contrastive learning for improved accuracy with attention applied both in spatial and latent contexts, herein we propose Dual Attention Contrastive based MIL (DA-CMIL). DA-CMIL takes as input several patient CT slices (considered as bag of instances) and outputs a single label. Attention based pooling is applied to implicitly select key slices in the latent space, whereas spatial attention learns slice spatial context for interpretable diagnosis. A contrastive loss is applied at the instance level to encode similarity of features from the same patient against representative pooled patient features. Empirical results show that our algorithm achieves an overall accuracy of 98.6% and an AUC of 98.4%. Moreover, ablation studies show the benefit of contrastive learning with MIL.

Chikontwe Philip, Luna Miguel, Kang Myeongkyun, Hong Kyung Soo, Ahn June Hong, Park Sang Hyun

2021-May-24

COVID-19, CT images, Deep learning, Multiple instance learning, Unsupervised complementary loss

General General

COVID-19 detection using federated machine learning.

In PloS one ; h5-index 176.0

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient's private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models' loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

Abdul Salam Mustafa, Taha Sanaa, Ramadan Mohamed

2021

Public Health Public Health

Evaluating Community-Facing Virtual Modalities to Support Complex Neurological Populations during the COVID-19 Pandemic: A Protocol.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The COVID-19 pandemic and concomitant governmental responses created the need for innovative, collaborative approaches to deliver services, especially for populations that have been inequitably affected. In Alberta, two novel approaches were created in Spring 2020 to remotely support patients with complex neurological conditions and rehabilitation needs. The first approach is a telehealth service that provides wayfinding and self-management advice to Albertans with physical concerns related to existing neurological or musculoskeletal conditions or post-COVID-19 recovery needs. The second approach is a webinar series aimed at supporting self-management and social connectedness of individuals living with spinal cord injury.

OBJECTIVE : To evaluate the short- and long- term impacts and sustainability of two virtual modalities aimed at advancing self-management, connectedness and rehabilitation needs during the COVID-19 pandemic and beyond.

METHODS : We will use a mixed-methods evaluation approach. Evaluation of both approaches will include one-on-one semi-structured interviews and surveys. The evaluation of the telehealth initiative will include secondary data analyses as well as analysis of call data using artificial intelligence. The evaluation of the webinar series will include analysis of poll questions collected during the webinars as well as YouTube analytics data.

RESULTS : The proposed study describes unique pandemic virtual modalities and our approaches to evaluating them to ensure effectiveness and sustainability. Implementing and evaluating these virtual modalities synchronously allows for the building of knowledge on the complementarity of these methods. At the time of submission, we have completed qualitative and quantitative data collection for the telehealth evaluation. For the webinar series, so far we have distributed the evaluation survey following three webinars and have conducted five attendee interviews.

CONCLUSIONS : In conclusion, understanding the impact and sustainability of the proposed telehealth modalities is important. The results of the evaluation will provide data that can be actioned and serve to improve other telehealth modalities in the future since health systems need this information to make decisions on resource allocation, especially in an uncertain pandemic climate. Evaluating the RAL and AB-SCILS to ensure their effectiveness demonstrates that Alberta Health Services and the health system cares about ensuring best practice even after a shift to primarily virtual care.

CLINICALTRIAL :

Brehon Katelyn, Carriere Jay, Churchill Katie, Loyola-Sanchez Adalberto, O’Connell Petra, Papathanasoglou Elisavet, MacIsaac Rob, Tavakoli Mahdi, Ho Chester, Pohar Manhas Kiran

2021-Jun-04

General General

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

In Research square

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Roth Holger, Xu Ziyue, Diez Carlos Tor, Jacob Ramon Sanchez, Zember Jonathan, Molto Jose, Li Wenqi, Xu Sheng, Turkbey Baris, Turkbey Evrim, Yang Dong, Harouni Ahmed, Rieke Nicola, Hu Shishuai, Isensee Fabian, Tang Claire, Yu Qinji, Sölter Jan, Zheng Tong, Liauchuk Vitali, Zhou Ziqi, Moltz Jan, Oliveira Bruno, Xia Yong, Maier-Hein Klaus, Li Qikai, Husch Andreas, Zhang Luyang, Kovalev Vassili, Kang Li, Hering Alessa, Vilaça João, Flores Mona, Xu Daguang, Wood Bradford, Linguraru Marius

2021-Jun-04

General General

Assessment of protein-protein interfaces in cryo-EM derived assemblies.

In Nature communications ; h5-index 260.0

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

Malhotra Sony, Joseph Agnel Praveen, Thiyagalingam Jeyan, Topf Maya

2021-06-07

Internal Medicine Internal Medicine

SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care.

In Nature communications ; h5-index 260.0

Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia is associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22-2.77] adjusted for age and sex). RNAemia is comparable in performance to the best protein predictors. Mannose binding lectin 2 and pentraxin-3 (PTX3), two activators of the complement pathway of the innate immune system, are positively associated with mortality. Machine learning identified 'Age, RNAemia' and 'Age, PTX3' as the best binary signatures associated with 28-day ICU mortality. In longitudinal comparisons, COVID-19 ICU patients have a distinct proteomic trajectory associated with mortality, with recovery of many liver-derived proteins indicating survival. Finally, proteins of the complement system and galectin-3-binding protein (LGALS3BP) are identified as interaction partners of SARS-CoV-2 spike glycoprotein. LGALS3BP overexpression inhibits spike-pseudoparticle uptake and spike-induced cell-cell fusion in vitro.

Gutmann Clemens, Takov Kaloyan, Burnap Sean A, Singh Bhawana, Ali Hashim, Theofilatos Konstantinos, Reed Ella, Hasman Maria, Nabeebaccus Adam, Fish Matthew, McPhail Mark Jw, O’Gallagher Kevin, Schmidt Lukas E, Cassel Christian, Rienks Marieke, Yin Xiaoke, Auzinger Georg, Napoli Salvatore, Mujib Salma F, Trovato Francesca, Sanderson Barnaby, Merrick Blair, Niazi Umar, Saqi Mansoor, Dimitrakopoulou Konstantina, Fernández-Leiro Rafael, Braun Silke, Kronstein-Wiedemann Romy, Doores Katie J, Edgeworth Jonathan D, Shah Ajay M, Bornstein Stefan R, Tonn Torsten, Hayday Adrian C, Giacca Mauro, Shankar-Hari Manu, Mayr Manuel

2021-06-07

General General

A Pharmacophore Model for SARS-CoV-2 3CLpro Small Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors

bioRxiv Preprint

Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic and steric features that characterize small molecule inhibitors binding stably to 3CLpro, as well as by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of 3CLpro with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 mcM), and synthetic compounds previously not characterized (e.g. compound CID 46897844, IC50 = 31 mcM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts, to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, molecular dynamics simulations and machine learning can facilitate 3CLpro-targeted small molecule screening investigations. Different receptor-, ligand- and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

Glaab, E.; Manoharan, G. B.; Abankwa, D.

2021-06-09

Radiology Radiology

COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system.

In PloS one ; h5-index 176.0

Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss' kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.

Hwang Eui Jin, Kim Ki Beom, Kim Jin Young, Lim Jae-Kwang, Nam Ju Gang, Choi Hyewon, Kim Hyungjin, Yoon Soon Ho, Goo Jin Mo, Park Chang Min

2021

General General

Guest attendance data from 34 Swedish pre-schools and primary schools.

In Data in brief

This data article describes 34 datasets, compiled into one table, describing guest attendance at lunch meal servings in Swedish public schools and preschools. Fifteen of the schools and all 16 of the preschools covered belong to one municipality, while the remaining three schools belong to two other municipalities, all located in central Sweden. Data on number of plates was used as a proxy of the number of guests eating lunch. Number of used plates was recorded from late August 2010 to early June 2020, i.e. covering the period both before and during the initial phase of the Covid-19 pandemic, so that making possible to evaluate changes in guest attendance during the pandemic. Since these were real data, all data elements pertaining to exact canteens or staff identity have been removed. There is a scarcity of real business data for scientific and educational purposes, so these datasets can play an important role in research and education within catering management, consumption pattern analysis, machine learning, data mining and other fields.

Eriksson Mattias, Malefors Christopher, Secondi Luca, Marchetti Stefano

2021-Jun

Food waste, Forecasting, Meal planning, Public sector catering

General General

Virtual Screening of Phytochemicals by Targeting HR1 Domain of SARS-CoV-2 S Protein: Molecular Docking, Molecular Dynamics Simulations, and DFT Studies.

In BioMed research international ; h5-index 102.0

The recent COVID-19 pandemic has impacted nearly the whole world due to its high morbidity and mortality rate. Thus, scientists around the globe are working to find potent drugs and designing an effective vaccine against COVID-19. Phytochemicals from medicinal plants are known to have a long history for the treatment of various pathogens and infections; thus, keeping this in mind, this study was performed to explore the potential of different phytochemicals as candidate inhibitors of the HR1 domain in SARS-CoV-2 spike protein by using computer-aided drug discovery methods. Initially, the pharmacological assessment was performed to study the drug-likeness properties of the phytochemicals for their safe human administration. Suitable compounds were subjected to molecular docking to screen strongly binding phytochemicals with HR1 while the stability of ligand binding was analyzed using molecular dynamics simulations. Quantum computation-based density functional theory (DFT) analysis was constituted to analyze the reactivity of these compounds with the receptor. Through analysis, 108 phytochemicals passed the pharmacological assessment and upon docking of these 108 phytochemicals, 36 were screened passing a threshold of -8.5 kcal/mol. After analyzing stability and reactivity, 5 phytochemicals, i.e., SilybinC, Isopomiferin, Lycopene, SilydianinB, and Silydianin are identified as novel and potent candidates for the inhibition of HR1 domain in SARS-CoV-2 spike protein. Based on these results, it is concluded that these compounds can play an important role in the design and development of a drug against COVID-19, after an exhaustive in vitro and in vivo examination of these compounds, in future.

Majeed Arshia, Hussain Waqar, Yasmin Farkhanda, Akhtar Ammara, Rasool Nouman

2021

General General

iOntoBioethics: A Framework for the Agile Development of Bioethics Ontologies in Pandemics, Applied to COVID-19.

In Frontiers in medicine

Background: Few ontological attempts have been reported for conceptualizing the bioethics domain. In addition to limited scope representativeness and lack of robust methodological approaches in driving research design and evaluation of bioethics ontologies, no bioethics ontologies exist for pandemics and COVID-19. This research attempted to investigate whether studying the bioethics research literature, from the inception of bioethics research publications, facilitates developing highly agile, and representative computational bioethics ontology as a foundation for the automatic governance of bioethics processes in general and the COVID-19 pandemic in particular. Research Design: The iOntoBioethics agile research framework adopted the Design Science Research Methodology. Using systematic literature mapping, the search space resulted in 26,170 Scopus indexed bioethics articles, published since 1971. iOntoBioethics underwent two distinctive stages: (1) Manually Constructing Bioethics (MCB) ontology from selected bioethics sources, and (2) Automatically generating bioethics ontological topic models with all 26,170 sources and using special-purpose developed Text Mining and Machine-Learning (TM&ML) engine. Bioethics domain experts validated these ontologies, and further extended to construct and validate the Bioethics COVID-19 Pandemic Ontology. Results: Cross-validation of the MCB and TM&ML bioethics ontologies confirmed that the latter provided higher-level abstraction for bioethics entities with well-structured bioethics ontology class hierarchy compared to the MCB ontology. However, both bioethics ontologies were found to complement each other forming a highly comprehensive Bioethics Ontology with around 700 concepts and associations COVID-19 inclusive. Conclusion:The iOntoBioethics framework yielded the first agile, semi-automatically generated, literature-based, and domain experts validated General Bioethics and Bioethics Pandemic Ontologies Operable in COVID-19 context with readiness for automatic governance of bioethics processes. These ontologies will be regularly and semi-automatically enriched as iOntoBioethics is proposed as an open platform for scientific and healthcare communities, in their infancy COVID-19 learning stage. iOntoBioethics not only it contributes to better understanding of bioethics processes, but also serves as a bridge linking these processes to healthcare systems. Such big data analytics platform has the potential to automatically inform bioethics governance adherence given the plethora of developing bioethics and COVID-19 pandemic knowledge. Finally, iOntoBioethics contributes toward setting the first building block for forming the field of "Bioethics Informatics".

Odeh Mohammed, Kharbat Faten F, Yousef Rana, Odeh Yousra, Tbaishat Dina, Hakooz Nancy, Dajani Rana, Mansour Asem

2021

COVID-19, agile framework, bioethics, bioethics informatics, bioethics ontology, design science research methodology, iOntoBioethics, pandemic

Radiology Radiology

Deep Insight: Convolutional Neural Network and its Applications for COVID-19 Prognosis.

In Biomedical signal processing and control

Background and Objective : SARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the Convolutional Neural Network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.

Method : In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis.

Result : This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.

Conclusion : The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery.

Khanday Nadeem Yousuf, Sofi Shabir Ahmad

2021-May-28

And Challenges, Applications, COVID-19, Convolutional Neural Network, Pandemic

General General

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data.

In Frontiers in genetics ; h5-index 62.0

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient's survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning-based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.

Sardar Rahila, Sharma Arun, Gupta Dinesh

2021

COVID-19, biomarkers discovery, feature selection, machine learning, proteomics and bioinformatics

General General

Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis.

In An international journal on information fusion

The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor-patient dialogues and their 3706 images (347 X-ray + 2598 CT + 761 ultrasound) about COVID-19 patients and 607 non-COVID-19 patient dialogues and their 10754 images (9658 X-ray + 494 CT + 761 ultrasound), and the fine-grained labels of all. We hope this work can provide insights to the researchers working in this area to shift the attention from only medical images to the doctor-patient dialogue and its corresponding medical images.

Zheng Wenbo, Yan Lan, Gou Chao, Zhang Zhi-Cheng, Jason Zhang Jun, Hu Ming, Wang Fei-Yue

2021-Jun-01

COVID-19 diagnose, Knowledge attention mechanism, Knowledge embedding, Knowledge-based representation learning

General General

A novel self-learning semi-supervised deep learning network to detect fake news on social media.

In Multimedia tools and applications

Social media has become a popular means for people to consume and share news. However, it also enables the extensive spread of fake news, that is, news that deliberately provides false information, which has a significant negative impact on society. Especially recently, the false information about the new coronavirus disease 2019 (COVID-19) has spread like a virus around the world. The state of the Internet is forcing the world's tech giants to take unprecedented action to protect the "information health" of the public. Despite many existing fake news datasets, comprehensive and effective algorithms for detecting fake news have become one of the major obstacles. In order to address this issue, we designed a self-learning semi-supervised deep learning network by adding a confidence network layer, which made it possible to automatically return and add correct results to help the neural network to accumulate positive sample cases, thus improving the accuracy of the neural network. Experimental results indicate that our network is more accurate than the existing mainstream machine learning methods and deep learning methods.

Li Xin, Lu Peixin, Hu Lianting, Wang XiaoGuang, Lu Long

2021-Jun-02

Confidence values, Fake  news, Semi-supervised  deep  learning  network, Social  media

Public Health Public Health

Lung Segmentation and Automatic Detection of COVID-19 Using Radiomic Features from Chest CT Images.

In Pattern recognition

This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to restrict the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9500, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.

Zhao Chen, Xu Yan, He Zhuo, Tang Jinshan, Zhang Yijun, Han Jungang, Shi Yuxin, Zhou Weihua

2021-Jun-02

3D V-Net, COVID-19, chest CT, deep learning, pulmonary parenchyma segmentation

General General

Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences

bioRxiv Preprint

Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large-scale experiments are noisy, and small-scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins. We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus--Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.

Lanchantin, J.; Weingarten, T.; Sekhon, A.; Miller, C. L.; Qi, Y.

2021-06-08

General General

DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images.

In International journal of computer assisted radiology and surgery

PURPOSE : The global health crisis caused by coronavirus disease 2019 (COVID-19) is a common threat facing all humankind. In the process of diagnosing COVID-19 and treating patients, automatic COVID-19 lesion segmentation from computed tomography images helps doctors and patients intuitively understand lung infection. To effectively quantify lung infections, a convolutional neural network for automatic lung infection segmentation based on deep learning is proposed.

METHOD : This new type of COVID-19 lesion segmentation network is based on a U-Net backbone. First, a coarse segmentation network is constructed to extract the lung areas. Second, in the encoding and decoding process of the fine segmentation network, a new soft attention mechanism, namely the dilated convolutional attention (DCA) mechanism, is introduced to enable the network to focus on better quantitative information to strengthen the network's segmentation ability in the subtle areas of the lesions.

RESULTS : The experimental results show that the average Dice similarity coefficient (DSC), sensitivity (SEN), specificity (SPE) and area under the curve of DUDA-Net are 87.06%, 90.85%, 99.59% and 0.965, respectively. In addition, the introduction of a cascade U-shaped network scheme and DCA mechanism can improve the DSC by 24.46% and 14.33%, respectively.

CONCLUSION : The proposed DUDA-Net approach can automatically segment COVID-19 lesions with excellent performance, which indicates that the proposed method is of great clinical significance. In addition, the introduction of a coarse segmentation network and DCA mechanism can improve the COVID-19 segmentation performance.

Xie Feng, Huang Zheng, Shi Zhengjin, Wang Tianyu, Song Guoli, Wang Bolun, Liu Zihong

2021-Jun-05

Attention mechanism, Deep learning, Lesion segmentation, Medical image analysis, U-Net

General General

Unbiased identification of clinical characteristics predictive of COVID-19 severity.

In Clinical and experimental medicine

There is currently limited clinical ability to identify COVID-19 patients at risk for severe outcomes. To unbiasedly identify metrics associated with severe outcomes in COVID-19 patients, we conducted a retrospective study of 835 COVID-19 positive patients at a single academic medical center between March 10, 2020 and October 13, 2020. As of December 1, 2020, 656 (79%) patients required hospitalization and 149 (18%) died. Unbiased comparisons of all clinical characteristics and mortality revealed that abnormal pH (OR 8.54, 95% CI 5.34-13.6), abnormal creatinine (OR 6.94, 95% CI 4.22-11.4), and abnormal PTT (OR 4.78, 95% CI 3.11-7.33) were most significantly associated with mortality. Correlation with ordinal severity scores confirmed these associations, in addition to associations between respiratory rate (Spearman's rho  = -0.56), absolute neutrophil count (Spearman's rho  = -0.5), and C-reactive protein (Spearman's rho  =  0.59) with disease severity. Unsupervised principal component analysis and machine learning model classification of patient demographics, laboratory results, medications, comorbidities, signs and symptoms, and vitals are capable of separating patients on the basis of COVID-19 mortality (AUC 0.82). This retrospective analysis identifies laboratory and clinical metrics most relevant to predict COVID-19 severity.

Akama-Garren Elliot H, Li Jonathan X

2021-Jun-05

COVID-19, Laboratory results, Machine learning, Prediction

General General

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design.

In Briefings in functional genomics

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.

Cai Xinhui, Li Jiao Jiao, Liu Tao, Brian Oliver, Li Jinyan

2021-Jun-04

COVID-19, epitope prediction, mRNA vaccines, machine learning, other infectious diseases

Cardiology Cardiology

A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients.

In NPJ digital medicine

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

Yanamala Naveena, Krishna Nanda H, Hathaway Quincy A, Radhakrishnan Aditya, Sunkara Srinidhi, Patel Heenaben, Farjo Peter, Patel Brijesh, Sengupta Partho P

2021-Jun-04

Radiology Radiology

Imaging of COVID-19: An update of current evidences.

In Diagnostic and interventional imaging

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool to differentiate COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately depict myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.

Kato Shingo, Ishiwata Yoshinobu, Aoki Ryo, Iwasawa Tae, Hagiwara Eri, Ogura Takashi, Utsunomiya Daisuke

2021-May-25

Artificial intelligence, COVID-19, Cardiac magnetic resonance, Computed tomography, Pulmonary embolism

General General

Challenges and opportunities of digital health in a post-COVID19 world.

In Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences

Digital health as a rapidly growing medical field relies comprehensively on human health data. Conventionally, the collection of health data is mediated by officially diagnostic instruments, operated by health professionals in clinical environments and under strict regulatory conditions. Mobile health, telemedicine, and other smart devices with Internet connections are becoming the future choices for collecting patient information. Progress of technologies has facilitated smartphones, wearable devices, and miniaturized health-care devices. These devices allow the gathering of an individual's health-care information at the patient's home. The data from these devices will be huge, and by integrating such enormous data using Artificial Intelligence, more detailed phenotyping of disease and more personalized medicine will be realistic. The future of medicine will be progressively more digital, and recognizing the importance of digital technology in this field and pandemic preparedness planning has become urgent.

Manteghinejad Amirreza, Javanmard Shaghayegh Haghjooy

2021

COVID19, digital health, mobile health, telemedicine

Radiology Radiology

Patient-specific COVID-19 resource utilization prediction using fusion AI model.

In NPJ digital medicine

The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient's need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1-86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.

Tariq Amara, Celi Leo Anthony, Newsome Janice M, Purkayastha Saptarshi, Bhatia Neal Kumar, Trivedi Hari, Gichoya Judy Wawira, Banerjee Imon

2021-Jun-03

General General

Evaluation of biases in remote photoplethysmography methods.

In NPJ digital medicine

This work investigates the estimation biases of remote photoplethysmography (rPPG) methods for pulse rate measurement across diverse demographics. Advances in photoplethysmography (PPG) and rPPG methods have enabled the development of contact and noncontact approaches for continuous monitoring and collection of patient health data. The contagious nature of viruses such as COVID-19 warrants noncontact methods for physiological signal estimation. However, these approaches are subject to estimation biases due to variations in environmental conditions and subject demographics. The performance of contact-based wearable sensors has been evaluated, using off-the-shelf devices across demographics. However, the measurement uncertainty of rPPG methods that estimate pulse rate has not been sufficiently tested across diverse demographic populations or environments. Quantifying the efficacy of rPPG methods in real-world conditions is critical in determining their potential viability as health monitoring solutions. Currently, publicly available face datasets accompanied by physiological measurements are typically captured in controlled laboratory settings, lacking diversity in subject skin tones, age, and cultural artifacts (e.g, bindi worn by Indian women). In this study, we collect pulse rate and facial video data from human subjects in India and Sierra Leone, in order to quantify the uncertainty in noncontact pulse rate estimation methods. The video data are used to estimate pulse rate using state-of-the-art rPPG camera-based methods, and compared against ground truth measurements captured using an FDA-approved contact-based pulse rate measurement device. Our study reveals that rPPG methods exhibit similar biases when compared with a contact-based device across demographic groups and environmental conditions. The mean difference between pulse rates measured by rPPG methods and the ground truth is found to be ~2% (1 beats per minute (b.p.m.)), signifying agreement of rPPG methods with the ground truth. We also find that rPPG methods show pulse rate variability of ~15% (11 b.p.m.), as compared to the ground truth. We investigate factors impacting rPPG methods and discuss solutions aimed at mitigating variance.

Dasari Ananyananda, Prakash Sakthi Kumar Arul, Jeni László A, Tucker Conrad S

2021-Jun-03

General General

Ensemble machine learning of factors influencing COVID-19 across US counties.

In Scientific reports ; h5-index 158.0

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities.

McCoy David, Mgbara Whitney, Horvitz Nir, Getz Wayne M, Hubbard Alan

2021-Jun-03

Public Health Public Health

Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis.

In Journal of the Formosan Medical Association = Taiwan yi zhi

BACKGROUND : As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent large-scale repeated surges of epidemics worldwide since the end of 2019, data-driven analysis to look into the duration and case load of each episode of outbreak worldwide has been motivated.

METHODS : Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt < 1 and case load were first modelled by using the compound Poisson method. Machine learning analysis using the K-means clustering method was further adopted to classify patterns of community-acquired outbreaks worldwide.

RESULTS : The global estimated Rt declined after the first surge of COVID-19 pandemic but there were still two major surges of epidemics occurring in September 2020 and March 2021, respectively, and numerous episodes due to various extents of Nonpharmaceutical Interventions (NPIs). Unsupervised machine learning identified five patterns as "controlled epidemic", "mutant propagated epidemic", "propagated epidemic", "persistent epidemic" and "long persistent epidemic" with the corresponding duration and the logarithm of case load from the lowest (18.6 ± 11.7; 3.4 ± 1.8)) to the highest (258.2 ± 31.9; 11.9 ± 2.4). Countries like Taiwan outside five clusters were classified as no community-acquired outbreak.

CONCLUSION : Data-driven models for the new classification of community-acquired outbreaks are useful for global surveillance of uninterrupted COVID-19 pandemic and provide a timely decision support for the distribution of vaccine and the optimal NPIs from global to local community.

Wang Wei-Chun, Lin Ting-Yu, Chiu Sherry Yueh-Hsia, Chen Chiung-Nien, Sarakarn Pongdech, Ibrahim Mohd, Chen Sam Li-Sheng, Chen Hsiu-Hsi, Yeh Yen-Po

2021-May-16

COVID-19, Cluster analysis, Community-acquired outbreak, Machine learning

Ophthalmology Ophthalmology

Real-time prediction of the daily incidence of COVID-19 in all countries and territories individually using machine learning: an infodemiology study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Advanced prediction of the daily incidence of COVID-19 helps policy-making on prevention of spread which profoundly affects people's livelihood. Previous studies have investigated prediction in single or several countries and territories.

OBJECTIVE : We aim to develop models for real-time prediction of COVID-19 activity in all countries/territories individually worldwide.

METHODS : Data of the previous daily incidence and Google Trends from all the individual countries/territories were collected. Random Forest Regression algorithm was used to train models to predict the daily new confirmed cases seven days ahead. Several methods were used to optimize the models, including clustering the countries/territories, feature selection according to the importance scores, multiple-step forecasting, and upgrading models at regular intervals. The performance of the models was assessed using mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient and Spearman correlation coefficient.

RESULTS : Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries/territories. There were 198 (92.1%) countries/territories with MAE <10 and 187 (87.0%) and Pearson correlation coefficient >0.8. In totally 215 countries/territories, the mean MAE was 5.42 (range 0.26 - 15.32), the mean RMSE 9.27 (range 1.81 - 24.40), the mean Pearson correlation coefficient 0.89 (range 0.08 - 0.99), and the mean Spearman correlation coefficient 0.84 (range 0.21 - 1.00).

CONCLUSIONS : Integrating the previous incidence and Google Trends data, our machine learning algorithm is able to predict the incidence of COVID-19 in most individual countries/territories accurately seven days ahead.

CLINICALTRIAL :

Peng Yuanyuan, Li Cuilian, Rong Yibiao, Pang Chi Pui, Chen Xinjian, Chen Haoyu

2021-May-31

Public Health Public Health

The fast-motion research process about COVID-19 in children: a bibliometric review.

In JMIR pediatrics and parenting

BACKGROUND : Since the beginning of the COVID-19 pandemic, a great number of papers have been published in the pediatric field.

OBJECTIVE : We aimed to assess the worldwide research on COVID-19 in the pediatric field by bibliometric analysis, identifying publication trends and topic dissemination and showing the relevance of publishing authors, institutions and countries.

METHODS : Scopus database was comprehensively searched for all indexed documents published between January 1, 2020 and June 11, 2020, dealing with COVID-19 in pediatric age (0-18 years). A machine learning bibliometric methodology was applied to evaluate the total number of papers and citations, journal and publication types, the top productive institutions and countries and their scientific collaboration, the core keywords.

RESULTS : A total of 2301 papers were retrieved, with an average of 4.8 citations per article. Out of them, 1078 (46.9%) were research articles, 436 (18.9%) reviews, 363 (15.8%) letters, 186 (8.1%) editorials, 7 (0.3%) were conference papers, and 231 (10%) others. The studies were published in 969 different journals, headed by The Lancet. The retrieved papers were published by a total of 12657 authors from 114 countries. The most productive countries were the USA, China, and Italy. The four main clusters of keywords were: pathogenesis and clinical characteristics (keyword occurrences n=2240), public health issues (n=352), mental health (n=82), and therapeutic aspects (n=70).

CONCLUSIONS : In the pediatric field, a large number of articles were published in a limited period on COVID-19, testifying the rush to timely spread new findings on the topic. The leading authors, countries, and institutions evidently belong to the most seriously involved geographical areas. A focus on the pediatric population is often included in general articles, and the pediatric research about COVID-19 mainly focused on the clinical features, public health issues, and psychological impact of the disease.

CLINICALTRIAL : Not applicable.

Monzani Alice, Tagliaferri Francesco, Bellone Simonetta, Genoni Giulia, Rabbone Ivana

2021-May-11

General General

Covid-19 Imaging Tools: How Big Data is Big?

In Journal of medical systems ; h5-index 48.0

In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases.

Santosh K C, Ghosh Sourodip

2021-Jun-03

Big data, Chest Computed Tomography, Chest X-ray, Covid-19, Medical imaging tools

Public Health Public Health

A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers.

In Briefings in bioinformatics

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

Chen Xin, Gao Wei, Li Jie, You Dongfang, Yu Zhaolei, Zhang Mingzhi, Shao Fang, Wei Yongyue, Zhang Ruyang, Lange Theis, Wang Qianghu, Chen Feng, Lu Xiang, Zhao Yang

2021-Jun-03

COVID-19, dynamic risk prediction, longitudinal data, time-to-event

Public Health Public Health

Development of a Conceptual Framework for Severe Self-Neglect (SN) by Modifying the CREST Model for Self-Neglect.

In Frontiers in medicine

Self-neglect is an inability or refusal to meet one's own basic needs as accepted by societal norms and is the most common report received by state agencies charged with investigating abuse, neglect and exploitation of vulnerable adults. Self-neglect is often seen in addition to one or multiple conditions of frailty, mild to severe dementia, poor sleep and depression. While awareness of elder self-neglect as a public health condition and intervention has significantly risen in the past decade as evidenced by the increasing amount of literature available, research on self-neglect still lacks comprehensiveness and clarity since its inception to the medical literature in the late 1960s. With the burgeoning of the older adult population, commonness of self-neglect will most likely increase as the current incidence rate represents only the "tip of the iceberg" theory given that most cases are unreported. The COVID-19 pandemic has exacerbated the incidence of self-neglect in aged populations and the need for the use of intervention tools for aging adults and geriatric patients living alone, many of which may include in-home artificial intelligence systems. Despite this, little research has been conducted on aspects of self-neglect other than definition and identification. Substantial further study of this disorder's etiology, educating society on early detection, and conceivably preventing this syndrome altogether or at least halting progression and abating its severity is needed. The purpose of this research is to provide a definition of severe self-neglect, identify key concepts related to self-neglect, comprehensively describe this syndrome, present a conceptual framework and analyze the model for its usefulness, generalizability, parsimony, and testability.

Pickens Sabrina, Daniel Mary, Jones Erick C, Jefferson Felicia

2021

artificial intelligence, cognition, geriatrics, self-neglect, sleep

General General

Dental Care in the Arab Countries During the COVID-19 Pandemic: An Infodemiological Study.

In Risk management and healthcare policy

Background : Twitter is a powerful platform which could be used to reflect on the demand and supply of dental services during a pandemic. The aim of this study was to examine the nature and dissemination of COVID-19 information related to dentistry on Twitter platform Arabic database during the COVID-19 pandemic.

Methodology : One hundred and fifty independent searches with a combination of keywords for both COVID-19 and dentistry from a preselected Arabic keyword were carried out for the period from the 2nd of March (first confirmed cases of COVID-19) to the 6th of July 2020. Tweets were filtered to remove duplicate and unrelated tweets. The suitable tweets were 1,150. After calibration, two examiners coded the tweets following two main themes: COVID-19 and oral health-related information. Tweets were then compared with COVID-19 daily events in the Arab countries as reported by the World Health Organization (WHO). Descriptive analysis was performed to present the overview of the findings using Microsoft Excel.

Results : The most retweeted information was the help with urgent consultation or emergency dental treatment during COVID-19 tweeted by a dentist. There were 673 retweets and 1,116 likes of this tweet. The most common tweets related to oral health were needs of dental treatment (n=462, 39.5%) of which, toothaches or wisdom tooth problems constituted 48% of the related tweets.

Conclusion : Based on the results of this study, it is obvious that social media users reacted to the COVID-19 threat to dental practices. Twitter as one of the social media platforms served as a connection between dental health professionals and patients.

Al-Khalifa Khalifa S, AlSheikh Rasha, Alsahafi Yaser A, Alkhalifa Atheer, Sadaf Shazia, Al-Moumen Saud A, Muazen Yasmeen Y, Shetty Ashwin C

2021

Arab, COVID-19, Twitter, dentistry, oral health, pandemic, social media, tweet

Public Health Public Health

The imprinting effect of SARS experience on the fear of COVID-19: The role of AI and big data.

In Socio-economic planning sciences

The worldwide outbreak of the COVID-19 has significantly increased the fear of individuals, which brings severe psychosocial stress and adverse psychological consequences, and become a serious public health problem. Based on the imprinting theory, this study investigates whether childhood experiences of SARS have an imprinting effect that significantly influences the fear of COVID-19. Furthermore, we propose that this effect is contingent on the applications of AI and big data. We test our framework with a sample of 1871 questionnaires that covered students in universities across all provincial regions in China, and the results suggest that the imprinting of SARS increases the individuals' fear of COVID-19, and this effect is reduced with the applications of AI and big data. Overall, this study provides a novel insight of the fear caused by the childhood experience of the similar health crisis and the unique role of AI and big data applications into fighting against COVID-19.

Yao Haitang, Liu Wei, Wu Chia-Huei, Yuan Yu-Hsi

2021-May-27

Artificial intelligence, Big data, COVID-19, Imprinting theory, Public health, SARS

General General

How do you feel during the COVID-19 pandemic? A survey using psychological and linguistic self-report measures, and machine learning to investigate mental health, subjective experience, personality, and behaviour during the COVID-19 pandemic among university students.

In BMC psychology

BACKGROUND : The WHO has raised concerns about the psychological consequences of the current COVID-19 pandemic, negatively affecting health across societies, cultures and age-groups.

METHODS : This online survey study investigated mental health, subjective experience, and behaviour (health, learning/teaching) among university students studying in Egypt or Germany shortly after the first pandemic lockdown in May 2020. Psychological assessment included stable personality traits, self-concept and state-like psychological variables related to (a) mental health (depression, anxiety), (b) pandemic threat perception (feelings during the pandemic, perceived difficulties in describing, identifying, expressing emotions), (c) health (e.g., worries about health, bodily symptoms) and behaviour including perceived difficulties in learning. Assessment methods comprised self-report questions, standardized psychological scales, psychological questionnaires, and linguistic self-report measures. Data analysis comprised descriptive analysis of mental health, linguistic analysis of self-concept, personality and feelings, as well as correlational analysis and machine learning. N = 220 (107 women, 112 men, 1 = other) studying in Egypt or Germany provided answers to all psychological questionnaires and survey items.

RESULTS : Mean state and trait anxiety scores were significantly above the cut off scores that distinguish between high versus low anxious subjects. Depressive symptoms were reported by 51.82% of the student sample, the mean score was significantly above the screening cut off score for risk of depression. Worries about health (mental and physical health) and perceived difficulties in identifying feelings, and difficulties in learning behaviour relative to before the pandemic were also significant. No negative self-concept was found in the linguistic descriptions of the participants, whereas linguistic descriptions of feelings during the pandemic revealed a negativity bias in emotion perception. Machine learning (exploratory) predicted personality from the self-report data suggesting relations between personality and subjective experience that were not captured by descriptive or correlative data analytics alone.

CONCLUSION : Despite small sample sizes, this multimethod survey provides important insight into mental health of university students studying in Egypt or Germany and how they perceived the first COVID-19 pandemic lockdown in May 2020. The results should be continued with larger samples to help develop psychological interventions that support university students across countries and cultures to stay psychologically resilient during the pandemic.

Herbert Cornelia, El Bolock Alia, Abdennadher Slim

2021-Jun-02

Anxiety, COVID-19, Character computing, Corona virus, Depression, Emotion perception, Linguistic analysis, Machine learning, Mental health, Pandemic, Personality, Self-concept

General General

A multi-task CNN learning model for taxonomic assignment of human viruses.

In BMC bioinformatics

BACKGROUND : Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample.

RESULTS : In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data.

CONCLUSIONS : We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus .

Ma Haoran, Tan Tin Wee, Ban Kenneth Hon Kim

2021-Jun-02

Convolutional neural network, Deep learning, Genomic coverage, Naïve Bayesian network, Taxonomic assignment

General General

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.

Alhasan Mustafa, Hasaneen Mohamed

2021-May-15

Artificial intelligence, COVID-19, Digital technologies, Healthcare, Machine learning, Medical imaging

General General

Social Media and the Surge: Emergency physician Twitter use in the Covid-19 pandemic as a potential predictor of impending surge.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The early conversations on social media by emergency physicians offers a window into the ongoing response to the SARS-CoV-2 pandemic.

OBJECTIVE : This retrospective observational study of emergency physician Twitter use details how the healthcare crisis has influenced emergency physician discourse online, and may have use as a harbinger of ensuing surge.

METHODS : Followers of the three main emergency physician professional organizations were identified using Twitter's application programming interface. They and their followers were included in the study if identifying explicitly as United States-based emergency physicians. Statuses or 'tweets' were obtained between January 4th, 2020, when the new disease was first reported, and December 14th, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and sEntiment Reasoner (VADER) tool as well as topic modeling using Latent Dirichlet Allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization.

RESULTS : 3,463 emergency physicians produced 334,747 unique English tweets during the study period. 910 (26.3%) stated that they were in training, and 446 (51.7%) of those who provided a gender identified as a man. Overall tweet volume went from a pre-March mean of 481.9 ±72.7 daily tweets to 1,065.5 ±257.3 daily thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and four days in November, discourse was dominated by the healthcare system (45,570, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson's r = 0.41), as was the occurrence of 'covid', 'coronavirus', or 'pandemic' in tweet text (0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7 and 28-day moving averages, was found to have occurred 45.0 ±12.7 days before peak COVID-19 hospital bed utilization across the country and four most contributory states.

CONCLUSIONS : COVID-19 Twitter discussion among emergency physicians correlates with and may precede rising hospital burden. This study therefore begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online, and suggests a potential avenue for understanding predictors of surge.

Margus Colton, Brown Natasha, Hertelendy Attila, Safferman Michelle R, Hart Alexander, Ciottone Gregory R

2021-Apr-23

oncology Oncology

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal.

In Nucleic acids research ; h5-index 217.0

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

Zheng Shuyu, Aldahdooh Jehad, Shadbahr Tolou, Wang Yinyin, Aldahdooh Dalal, Bao Jie, Wang Wenyu, Tang Jing

2021-Jun-01

Public Health Public Health

Data-Driven and Machine-Learning Methods to Project Coronavirus Disease 2019 Pandemic Trend in Eastern Mediterranean.

In Frontiers in public health

Background: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health crisis worldwide, and the Eastern Mediterranean is one of the most affected areas. Materials and Methods: We use a data-driven approach to assess the characteristics, situation, prevalence, and current intervention actions of the COVID-19 pandemic. We establish a spatial model of the spread of the COVID-19 pandemic to project the trend and time distribution of the total confirmed cases and growth rate of daily confirmed cases based on the current intervention actions. Results: The results show that the number of daily confirmed cases, number of active cases, or growth rate of daily confirmed cases of COVID-19 are exhibiting a significant downward trend in Qatar, Egypt, Pakistan, and Saudi Arabia under the current interventions, although the total number of confirmed cases and deaths is still increasing. However, it is predicted that the number of total confirmed cases and active cases in Iran and Iraq may continue to increase. Conclusion: The COVID-19 pandemic in Qatar, Egypt, Pakistan, and Saudi Arabia will be largely contained if interventions are maintained or tightened. The future is not optimistic, and the intervention response must be further strengthened in Iran and Iraq. The aim of this study is to contribute to the prevention and control of the COVID-19 pandemic.

Huang Wenbo, Ao Shuang, Han Dan, Liu Yuming, Liu Shuang, Huang Yaojiang

2021

COVID-19, assessment, data-driven, machine learning, projection

General General

n-Gram Based Language Processing using Twitter Dataset to Identify COVID-19 Patients.

In Sustainable cities and society

Due to the rapid growth of electronic documents, e.g., tweets, blogs, Facebook posts, snaps in different languages that use the same writing script, language categorization, and processing have great importance. For instance, to identify COVID-19 positive patients or people's emotions on COVID-19 pandemic from tweets written in 35 different languages faster and accurate, language categorization and processing of tweets is significantly essential. Among many language categorization and processing techniques, character and word n-gram based techniques are very popular and simple but very efficient for categorizing and processing both short and large documents. One of the fundamental problems of language processing is the efficient use of memory space in implementing a technique so that a vast collection of documents can be easily categorized and processed. In this paper, we introduce a framework that categorizes the language of tweets using n-gram based language categorization technique and further processes the tweets using the machine-learning approach, Linear Support Vector Machine (LSVM), that may be able to identify COVID-19 positive patients. We evaluate and compare the performance of the proposed framework in terms of language categorization accuracy, precession, recall, and F-measure over n-gram length. The proposed framework is scalable as many other applications that involve extracting features and classifying languages collected from social media, and different types of networks may use this framework. This proposed framework, also being a part of health monitoring and improvement, tends to achieve the goal of having a sustainable society.

Nasser Nidal, Karim Lutful, El Ouadrhiri Ahmed, Ali Asmaa, Khan Nargis

2021-May-25

Character n-gram, LSVM, Language Categorization, Natural Language Processing, TFIDF, Word n-gram

General General

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning.

In Computational and structural biotechnology journal

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anti-corona activity and their inhibition efficiencies (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60-0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely, Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These computational methods would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

Rajput Akanksha, Thakur Anamika, Mukhopadhyay Adhip, Kamboj Sakshi, Rastogi Amber, Gautam Sakshi, Jassal Harvinder, Kumar Manoj

2021-May-24

AI, COVID-19, Chemical descriptors, Coronaviruses, Drug repurposing, Machine learning, SARS-CoV-2

General General

Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment.

In Journal of ambient intelligence and humanized computing

Different respiratory infections cause abnormal symptoms in lung parenchyma that show in chest computed tomography. Since December 2019, the SARS-COV-2 virus, which is the causative agent of COVID-19, has invaded the world causing high numbers of infections and deaths. The infection with SARS-COV-2 virus shows an abnormality in lung parenchyma that can be effectively detected using Computed Tomography (CT) imaging. In this paper, a novel computer aided framework (COV-CAF) is proposed for classifying the severity degree of the infection from 3D Chest Volumes. COV-CAF fuses traditional and deep learning approaches. The proposed COV-CAF consists of two phases: the preparatory phase and the feature analysis and classification phase. The preparatory phase handles 3D-CT volumes and presents an effective cut choice strategy for choosing informative CT slices. The feature analysis and classification phase incorporate fuzzy clustering for automatic Region of Interest (RoI) segmentation and feature fusion. In feature fusion, automatic features are extracted from a newly introduced Convolution Neural Network (Norm-VGG16) and are fused with spatial hand-crafted features extracted from segmented RoI. Experiments are conducted on MosMedData: Chest CT Scans with COVID-19 Related Findings with COVID-19 severity classes and SARS-COV-2 CT-Scan benchmark datasets. The proposed COV-CAF achieved remarkable results on both datasets. On MosMedData dataset, it achieved an overall accuracy of 97.76% and average sensitivity of 96.73%, while on SARS-COV-2 CT-Scan dataset it achieves an overall accuracy and sensitivity 97.59% and 98.41% respectively.

Ibrahim Mohamed Ramzy, Youssef Sherin M, Fathalla Karma M

2021-May-25

COVID-19, Computed tomography, Deep learning, Feature generation, RoI segmentation

Radiology Radiology

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

In Computational and mathematical methods in medicine

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.

Helwan Abdulkader, Ma’aitah Mohammad Khaleel Sallam, Hamdan Hani, Ozsahin Dilber Uzun, Tuncyurek Ozum

2021

General General

A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

In Applied soft computing

Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app's deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.

Sait Unais, K V Gokul Lal, Shivakumar Sanjana, Kumar Tarun, Bhaumik Rahul, Prajapati Sunny, Bhalla Kriti, Chakrapani Anaghaa

2021-Sep

Breathing sounds, CNN, Chest X-ray images, Covid-19, Deep-learning, MLP

General General

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic.

In Energy (Oxford, England)

COVID-19 made considerable changes in the lifestyle of people, which have led to a rise in energy use in homes. So, this study investigated the relationship between COVID-19 and domestic hot water demands. For this purpose, a nondimensional and principal component analysis were conducted to find out the influencing factors using demand data before and after COVID-19 from our study site. Analysis showed that the COVID-19 outbreak affected the daily peak time and the amount of domestic hot water usage, the active case number of COVID-19 was a good indicator for correlating the changes in hot water demand and patterns. Based on this, a machine learning model with an artificial neural network was developed to predict hot water demand depending on the severity of COVID-19 and the relevant correlation was also derived. The model analysis showed that the increase in the number of active cases in the region affected the hot water demand increased at a certain rate and the maximum demand peak in morning during weekdays and weekends decreased. Furthermore, if the number of active cases reached more than 4000, the peak in morning moved to afternoon so that the energy use patterns of weekdays and weekends are assimilated.

Kim Dongwoo, Yim Taesu, Lee Jae Yong

2021-Sep-15

Artificial neural network, COVID-19, Domestic hot water

Radiology Radiology

Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies.

In Pattern recognition

The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists and deep learning engineers dealing with model explanations. The final result is a proposed checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.

Hryniewska Weronika, Bombiski Przemysaw, Szatkowski Patryk, Tomaszewska Paulina, Przelaskowski Artur, Biecek Przemysaw

2021-May-21

COVID-19, X-ray, computed tomography, deep learning, explainable AI, lungs

Public Health Public Health

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection.

In Philosophical transactions of the Royal Society of London. Series B, Biological sciences

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Jombart Thibaut, Ghozzi Stéphane, Schumacher Dirk, Taylor Timothy J, Leclerc Quentin J, Jit Mark, Flasche Stefan, Greaves Felix, Ward Tom, Eggo Rosalind M, Nightingale Emily, Meakin Sophie, Brady Oliver J, Medley Graham F, Höhle Michael, Edmunds W John

2021-Jul-19

ASMODEE, algorithm, machine learning, outbreak, surveillance, trendbreaker

Radiology Radiology

Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging.

In Clinical imaging

PURPOSE : To evaluate whether the extent of COVID-19 pneumonia on CT scans using quantitative CT imaging obtained early in the illness can predict its future severity.

METHODS : We conducted a retrospective single-center study on confirmed COVID-19 patients between January 18, 2020 and March 5, 2020. A quantitative AI algorithm was used to evaluate each patient's CT scan to determine the proportion of the lungs with pneumonia (VR) and the rate of change (RAR) in VR from scan to scan. Patients were classified as being in the severe or non-severe group based on their final symptoms. Penalized B-splines regression modeling was used to examine the relationship between mean VR and days from onset of symptoms in the two groups, with 95% and 99% confidence intervals.

RESULTS : Median VR max was 18.6% (IQR 9.1-32.7%) in 21 patients in the severe group, significantly higher (P < 0.0001) than in the 53 patients in non-severe group (1.8% (IQR 0.4-5.7%)). RAR was increasing with a median RAR of 2.1% (IQR 0.4-5.5%) in severe and 0.4% (IQR 0.1-0.9%) in non-severe group, which was significantly different (P < 0.0001). Penalized B-spline analyses showed positive relationships between VR and days from onset of symptom. The 95% confidence limits of the predicted means for the two groups diverged 5 days after the onset of initial symptoms with a threshold of 11.9%.

CONCLUSION : Five days after the initial onset of symptoms, CT could predict the patients who later developed severe symptoms with 95% confidence.

Li Kunwei, Liu Xueguo, Yip Rowena, Yankelevitz David F, Henschke Claudia I, Geng Yayuan, Fang Yijie, Li Wenjuan, Pan Cunxue, Chen Xiaojun, Qin Peixin, Zhong Yinghua, Liu Kunfeng, Li Shaolin

2021-Feb-10

COVID-19, Deep learning, Prognosis, Quantitative evaluation, Tomography, X-ray computed

General General

Ethics of ICU triage during COVID-19.

In British medical bulletin

INTRODUCTION : The coronavirus disease 2019 pandemic has placed intensive care units (ICU) triage at the center of bioethical discussions. National and international triage guidelines emerged from professional and governmental bodies and have led to controversial discussions about which criteria-e.g. medical prognosis, age, life-expectancy or quality of life-are ethically acceptable. The paper presents the main points of agreement and disagreement in triage protocols and reviews the ethical debate surrounding them.

SOURCES OF DATA : Published articles, news articles, book chapters, ICU triage guidelines set out by professional societies and health authorities.

AREAS OF AGREEMENT : Points of agreement in the guidelines that are widely supported by ethical arguments are (i) to avoid using a first come, first served policy or quality-adjusted life-years and (ii) to rely on medical prognosis, maximizing lives saved, justice as fairness and non-discrimination.

AREAS OF CONTROVERSY : Points of disagreement in existing guidelines and the ethics literature more broadly regard the use of exclusion criteria, the role of life expectancy, the prioritization of healthcare workers and the reassessment of triage decisions.

GROWING POINTS : Improve outcome predictions, possibly aided by Artificial intelligence (AI); develop participatory approaches to drafting, assessing and revising triaging protocols; learn from experiences with implementation of guidelines with a view to continuously improve decision-making.

AREAS TIMELY FOR DEVELOPING RESEARCH : Examine the universality vs. context-dependence of triaging principles and criteria; empirically test the appropriateness of triaging guidelines, including impact on vulnerable groups and risk of discrimination; study the potential and challenges of AI for outcome and preference prediction and decision-support.

Vinay Rasita, Baumann Holger, Biller-Andorno Nikola

2021-May-31

COVID-19, ICU triage, ethics, guidelines, resource allocation

General General

Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach.

In SN computer science

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

Ahmed Sifat, Hossain Tonmoy, Hoque Oishee Bintey, Sarker Sujan, Rahman Sejuti, Shah Faisal Muhammad

2021

COVID-19, HRNet, Healthcare, Pandemic, UNet, X-Ray

General General

The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.

In Frontiers in artificial intelligence

COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.

Abdulkareem Musa, Petersen Steffen E

2021

COVID-19, artificial intelligence, contact tracing, detection, diagnosis, epidemiology, medical imaging, social control

General General

Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice.

In Digital biomarkers

Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.

Fagherazzi Guy, Fischer Aurélie, Ismael Muhannad, Despotovic Vladimir

Artificial intelligence, COVID-19, Signal decomposition, Smart home, Vocal biomarker, Voice

General General

Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19.

In ACS omega

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100's) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000's) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.

Srinivasan Srilok, Batra Rohit, Chan Henry, Kamath Ganesh, Cherukara Mathew J, Sankaranarayanan Subramanian K R S

2021-May-18

General General

Overview of digital health teaching courses in medical education in Germany in 2020.

In GMS journal for medical education

Objective: The digitalization of the healthcare system poses new challenges for physicians. Thus, the relevance of learning digital competencies (DiCo), such as dealing with data sets, apply telemedicine or using apps, is already growing in medical education. DiCo should be clearly separated from digitized teaching formats, which have been increasingly used since the COVID 19 pandemic. This article outlines the faculties in Germany where DiCo are already integrated into medical education. Methods: Courses with DiCo as teaching content were collected by a literature research on Pubmed and Google as well as by contacting all dean's offices and other persons responsible for teaching at German medical faculties. The courses were summarized in a table. Results: In a first survey, 16 universities were identified that offer courses on DiCo. In the elective area at the universities, 17 courses and in the compulsory area eight courses could be identified. The scope and content of the courses diverged between compulsory curricula, integrated courses of different lengths, and elective courses that are one-time or longitudinally integrated. The topics taught are heterogeneous and include fundamentals of medical informatics such as data management on the one hand and a collection of e.g. ethics, law, apps, artificial intelligence, telemedicine and robotics on the other hand. Conclusion: Currently, only some German medical faculties offer courses on DiCo. These courses vary in scope and design. They are frequently part of the elective curriculum and only reach some of the students. The possibility of embedding DiCo in the already existing cross-sectional area appears limited. In view of the ongoing digitalization of healthcare, it is necessary to make future courses on DiCo accessible to all medical students. In order to drive this expansion forward, the implementation of the new learning objectives catalogue, in which DiCo are integrated, a network formation, a teaching qualification as well as the involvement of students is recommended.

Aulenkamp Jana, Mikuteit Marie, Löffler Tobais, Schmidt Jeremy

2021

digital competencies, digital health, digital medicine, digital teaching, eLearning, education, medical informatics

General General

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction.

In Frontiers in public health

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.

Karthikeyan Akshaya, Garg Akshit, Vinod P K, Priyakumar U Deva

2021

biomarkers, coronavirus disease 2019, machine learning, mortality, prognosis

Radiology Radiology

Deep learning for predicting COVID-19 malignant progression.

In Medical image analysis

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.

Fang Cong, Bai Song, Chen Qianlan, Zhou Yu, Xia Liming, Qin Lixin, Gong Shi, Xie Xudong, Zhou Chunhua, Tu Dandan, Zhang Changzheng, Liu Xiaowu, Chen Weiwei, Bai Xiang, Torr Philip H S

2021-May-12

COVID-19, Domain adaptation, Feature fusion, Malignant progression

General General

Pretraining model for biological sequence data.

In Briefings in functional genomics

With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.

Song Bosheng, Li Zimeng, Lin Xuan, Wang Jianmin, Wang Tian, Fu Xiangzheng

2021-May-28

biological sequence, deep learning, pretraining model

Public Health Public Health

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race.

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

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What's more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

Hou Xiao, Gao Song, Li Qin, Kang Yuhao, Chen Nan, Chen Kaiping, Rao Jinmeng, Ellenberg Jordan S, Patz Jonathan A

2021-Jun-15

data assimilation, human mobility, neighborhood disparities, spatial epidemiology, stochastic COVID-19 spread modeling

General General

The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent.

In PloS one ; h5-index 176.0

In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of regression models' predictions are usually based on the notion of mean error, where by error we mean a deviation of a prediction from an observation. However, these mean based measures of models' performance have two drawbacks. Firstly, they ignore the length of the prediction, which is crucial when dealing with chaotic systems, where a small deviation at the beginning grows exponentially with time. Secondly, these measures are not suitable in situations where a prediction is made for a specific point in time (e.g. a date), since they average all errors from the start of the prediction to its end. Therefore, the aim of this paper is to propose a new measure of models' prediction precision, a divergence exponent, based on the notion of the Lyapunov exponent which overcomes the aforementioned drawbacks. The proposed approach enables the measuring and comparison of models' prediction precision for time series with unequal length and a given target date in the framework of chaotic phenomena. Application of the divergence exponent to the evaluation of models' accuracy is demonstrated by two examples and then a set of selected predictions of COVID-19 spread from other studies is evaluated to show its potential.

Mazurek Jiří

2021

General General

The 'humane in the loop': Inclusive research design and policy approaches to foster capacity building assistive technologies in the COVID-19 era.

In Assistive technology : the official journal of RESNA

The COVID-19 pandemic is emerging as a driver of greater reliance on wireless technology including intelligent assistive technologies such as robots and artificial intelligence. We must integrate the humane 'into the loop' of human-AT interactions to realize the full potential of wireless inclusion for people with disabilities and older adults. Embedding ethics into these new technologies is critical and requires a co-design approach, with end users participating throughout. Developing humane AT begins with a participatory, user-centered design embedded in an iterative co-creation process, and guided by an ethos prioritizing beneficence, user autonomy and agency. To gain insight into plausible AT development pathways ('futures'), we use scenario planning as a tool to articulate themes in the research literature. Four plausible scenarios are developed and compared to identify one as a desired 'humane' future for AT development. Policy and practice recommendations derived from this scenario, and their implications for the role of AT in the advancement of human potential are explored.

Bricout John, Greer Julienne, Fields Noelle, Xu Ling, Tamplain Priscila, Doelling Kris, Sharma Bonita

2021-May-28

Radiology Radiology

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease.

In Scientific reports ; h5-index 158.0

We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

Zandehshahvar Mohammadreza, van Assen Marly, Maleki Hossein, Kiarashi Yashar, De Cecco Carlo N, Adibi Ali

2021-May-27

General General

Maintaining proper health records improves machine learning predictions for novel 2019-nCoV.

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

BACKGROUND : An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of 2019-nCoV over 'early' datasets. A major consideration is the likelihood of death for patients with 2019-nCoV.

METHOD : Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. We included the patient's 'disease' history (only present in the second dataset) to predict the outcome for the second dataset.

RESULTS : The use of a patient's 'disease' history improves the prediction of 'death' by more than sevenfold. The models ignoring a patent's 'disease' history performed poorly in test predictions.

CONCLUSION : Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

Khan Koffka, Ramsahai Emilie

2021-May-27

2019-nCoV, AdaBoost, Bagging, Classifiers, Death, Disease, Machine learning, Pneumonia, Prediction

General General

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series.

In Environmental science and pollution research international

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

Said Ahmed Ben, Erradi Abdelkarim, Aly Hussein Ahmed, Mohamed Abdelmonem

2021-May-27

Bi-LSTM, COVID-19, Clustering, Cumulative cases

General General

Fuzzy Matching for Symptom Detection in Tweets: Application to Covid-19 During the First Wave of the Pandemic in France.

In Studies in health technology and informatics ; h5-index 23.0

The exhaustive automatic detection of symptoms in social media posts is made difficult by the presence of colloquial expressions, misspellings and inflected forms of words. The detection of self-reported symptoms is of major importance for emergent diseases like the Covid-19. In this study, we aimed to (1) develop an algorithm based on fuzzy matching to detect symptoms in tweets, (2) establish a comprehensive list of Covid-19-related symptoms and (3) evaluate the fuzzy matching for Covid-19-related symptom detection in French tweets. The Covid-19-related symptom list was built based on the aggregation of different data sources. French Covid-19-related tweets were automatically extracted using a dedicated data broker during the first wave of the pandemic in France. The fuzzy matching parameters were finetuned using all symptoms from MedDRA and then evaluated on a subset of 5000 Covid-19-related tweets in French for the detection of symptoms from our Covid-19-related list. The fuzzy matching improved the detection by the addition of 42% more correct matches with an 81% precision.

Faviez Carole, Foulquié Pierre, Chen Xiaoyi, Mebarki Adel, Quennelle Sophie, Texier Nathalie, Katsahian Sandrine, Schuck Stéphane, Burgun Anita

2021-May-27

Content analysis, Covid-19, fuzzy matching, social media, symptoms

General General

DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning.

In Studies in health technology and informatics ; h5-index 23.0

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

Hadj Bouzid Amel Imene, Yahiaoui Said, Lounis Anis, Berrani Sid-Ahmed, Belbachir Hacène, Naïli Qaïs, Abdi Mohamed El Hafedh, Bensalah Kawthar, Belazzougui Djamal

2021-May-27

CNN, CT-scan, Classification, Covid-19, Deep learning

General General

Machine Learning to Identify Fake News for COVID-19.

In Studies in health technology and informatics ; h5-index 23.0

International Organizations are seriously concerned about the fake news phenomenon. UNESCO has defined the term of misinformation/disinformation, which are the two faces of fake news. European Commission has conducted a survey about "Fake News" through EU citizens to estimate the awareness and people behaviour related to the appearance of fake news and disinformation on electronic. The findings are quite worrying, since about 40% come across fake news daily and 85% evaluate fake news as a problem. The aim of this work is to introduce an Artificial Intelligence approach, the Decision Trees algorithm to identify fake news on the COVID-19.

Isaakidou Marianna, Zoulias Emmanouil, Diomidous Marianna

2021-May-27

Artificial Intelligence, COVID-19, Fake News, Machine Learning, Natural Language Process

General General

Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review.

In Studies in health technology and informatics ; h5-index 23.0

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic's impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.

Syed Mahanazuddin, Syed Shorabuddin, Sexton Kevin, Greer Melody L, Zozus Meredith, Bhattacharyya Sudeepa, Syed Farhanuddin, Prior Fred

2021-May-27

COVID-19, Death, Deep Learning, Mortality, Pandemic, Scoping Review

General General

COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning.

In Studies in health technology and informatics ; h5-index 23.0

Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient.

Meyer Philip, Müller Dominik, Soto-Rey Iñaki, Kramer Frank

2021-May-27

COVID-19, artificial intelligence, computed tomography, deep learning, ensemble learning, segmentation

Public Health Public Health

Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation.

In JMIRx med

With over 117 million COVID-19-positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.

Prieto-Merino David, Bebiano Da Providencia E Costa Rui, Bacallao Gallestey Jorge, Sofat Reecha, Chung Sheng-Chia, Potts Henry

COVID-19, learning health systems

Cardiology Cardiology

Swarm Learning for decentralized and confidential clinical machine learning.

In Nature ; h5-index 368.0

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Warnat-Herresthal Stefanie, Schultze Hartmut, Shastry Krishnaprasad Lingadahalli, Manamohan Sathyanarayanan, Mukherjee Saikat, Garg Vishesh, Sarveswara Ravi, Händler Kristian, Pickkers Peter, Aziz N Ahmad, Ktena Sofia, Tran Florian, Bitzer Michael, Ossowski Stephan, Casadei Nicolas, Herr Christian, Petersheim Daniel, Behrends Uta, Kern Fabian, Fehlmann Tobias, Schommers Philipp, Lehmann Clara, Augustin Max, Rybniker Jan, Altmüller Janine, Mishra Neha, Bernardes Joana P, Krämer Benjamin, Bonaguro Lorenzo, Schulte-Schrepping Jonas, De Domenico Elena, Siever Christian, Kraut Michael, Desai Milind, Monnet Bruno, Saridaki Maria, Siegel Charles Martin, Drews Anna, Nuesch-Germano Melanie, Theis Heidi, Heyckendorf Jan, Schreiber Stefan, Kim-Hellmuth Sarah, Nattermann Jacob, Skowasch Dirk, Kurth Ingo, Keller Andreas, Bals Robert, Nürnberg Peter, Rieß Olaf, Rosenstiel Philip, Netea Mihai G, Theis Fabian, Mukherjee Sach, Backes Michael, Aschenbrenner Anna C, Ulas Thomas, Breteler Monique M B, Giamarellos-Bourboulis Evangelos J, Kox Matthijs, Becker Matthias, Cheran Sorin, Woodacre Michael S, Goh Eng Lim, Schultze Joachim L

2021-May-26

Public Health Public Health

Modeling transmission of pathogens in healthcare settings.

In Current opinion in infectious diseases

PURPOSE OF REVIEW : Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.

RECENT FINDINGS : The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.

SUMMARY : As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Stachel Anna, Keegan Lindsay T, Blumberg Seth

2021-May-25

General General

Systemness Taps the Power of Interdependence in Healthcare.

In Frontiers of health services management

While the term systemness has been used in the healthcare sector for decades, its definition varies from organization to organization. Still, the goals are consistent: to improve patient experience, lower costs, reduce risk, and provide insights into a wide range of care and management issues. Most health systems face similar challenges, such as margin enhancement, quality improvement, increased access, and fending off disruptive competition. Systemness is a way to address these challenges while improving the overall interdependence of the organization. Although embraced by and advantageous to healthcare organizations, systemness efforts often fail. The obstacles are surmountable when organizations thoroughly analyze the achievable scale of systemness, community resources, and current mindset regarding the good of the whole. Leaders must play a vital role in promoting systemness by providing education and a routine review of day-to-day organizational activities. Sometimes, systemness requires a change in leadership or an updating of leadership skills.Organizations must recognize and assess their culture as it relates to principles of independence versus interdependence, and refocus clinical standardization through best-practice protocols and policies as COVID-19 affects the already-fractured healthcare sector. Fortunately, current and developing artificial intelligence, wearables, at-home testing, and improved technologies promise to provide a needed break for a contracting physician field and fatigued front line, and they present an opportunity for those organizations poised to meet the systemness challenge.

Stokes Charles D, Brace Rod

2021-Jul-01

Radiology Radiology

Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic.

In Environmental science and pollution research international

The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.

Kaur Ishnoor, Behl Tapan, Aleya Lotfi, Rahman Habibur, Kumar Arun, Arora Sandeep, Bulbul Israt Jahan

2021-May-25

COVID-19, Computational tomography, Disease management, Industry 4.0, Radiology imaging, Techno-driven

General General

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

In Briefings in bioinformatics

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

Chen Tyrone, Philip Melcy, Lê Cao Kim-Anh, Tyagi Sonika

2021-May-24

COVID-19, SARS-CoV-2, data harmonisation, data integration, machine learning, multi-omics, multivariate analysis

Cardiology Cardiology

Digital Health: Implications for Heart Failure Management.

In Cardiac failure review

Digital health encompasses the use of information and communications technology and the use of advanced computing sciences in healthcare. This review covers the application of digital health in heart failure patients, focusing on teleconsultation, remote monitoring and apps and wearables, looking at how these technologies can be used to support care and improve outcomes. Interest in and use of these technologies, particularly teleconsultation, have been accelerated by the coronavirus disease 2019 pandemic. Remote monitoring of heart failure patients, to identify those patients at high risk of hospitalisation and to support clinical stability, has been studied with mixed results. Remote monitoring of pulmonary artery pressure has a consistent effect on reducing hospitalisation rates for patients with moderately severe symptoms and multiparameter monitoring shows promise for the future. Wearable devices and apps are increasingly used by patients for health and lifestyle support. Some wearable technologies have shown promise in AF detection, and others may be useful in supporting self-care and guiding prognosis, but more evidence is required to guide their optimal use. Support for patients and clinicians wishing to use these technologies is important, along with consideration of data validity and privacy and appropriate recording of decision-making.

Singhal Arvind, Cowie Martin R

2021-Mar

COVID-19, Digital health, apps, cardiology, e-health, heart failure, m-health, machine learning, telemedicine, wearables

General General

An investigation of the impacts of a successful COVID-19 response and meteorology on air quality in New Zealand.

In Atmospheric environment (Oxford, England : 1994)

The COVID-19 pandemic brought about national restrictions on people's movements, in effect commencing a socially engineered transport emission reduction experiment. In New Zealand during the most restrictive alert level (Level 4), roadside concentrations of nitrogen dioxide (NO2) were reduced 48-54% compared to Business-as-usual (BAU) values. NO2 concentrations rapidly returned to near mean levels as the alert levels decreased and restrictions eased. PM10 and PM2.5 responded differently to NO2 during the different alert levels. This is due to particulates having multiple sources, many of natural origin and therefore less influenced by human activity. PM10 and PM2.5 concentrations were reduced during alert level 4 but to a lesser extent than NO2 and with more variability across regions. Particulate concentrations increased notably during alert level 2 when many airsheds reported concentrations above the BAU means. To provide robust BAU reference concentrations, simple 5-year means were calculated along with predictions from machine learning modelling that, in effect, removed the influence of meteorology on observed concentrations. The results of this study show that latter method was found to be more closely aligned to observed values for NO2 as well as PM2.5 and PM10 away from coastal regions.

Talbot Nick, Takada Akika, Bingham Andrew H, Elder Dan, Lay Yee Samantha, Golubiewski Nancy E

2021-Jun-01

Atmospheric pollutants, COVID-19, Machine learning, On-road vehicles

General General

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning.

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

BACKGROUND : Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.

METHODS : A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19.

RESULTS : Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach.

CONCLUSION : Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals.

SOURCE CODE : All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification.

Ozdemir Mehmet Akif, Ozdemir Gizem Dilara, Guren Onan

2021-May-25

COVID-19, Convolutional neural network, Deep learning, Diagnosis, ECG, GLCM, Hexaxial mapping, Paper-based ECG

General General

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

In IEEE journal of biomedical and health informatics

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-19 infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). This network is integrated with a backbone segmentation network to refine the initial segmentation resulting from the backbone segmentation network. There are three contributions of this paper, as follows. First, we propose an interactive attention refinement network, which can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. Second, we propose a skip connection attention module to improve the important features in both segmentation and refinement networks for initial segmentation and refinement. Ultimately, we propose a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy. The model can even be applied to datasets from other centers that are collected in different hospitals and were not included in the training dataset.

Kitrungrotsakul Titinunt, Chen Qingqing, Wu Huitao, Iwamoto Yutaro, Hu Honjie, Zhu Wenchao, Chen Chao, Xu Fangyi, Zhou Yong, Lin Lanfen, Tong Ruo-Feng, Li Jingsong, Chen Yen Wei

2021-May-25

General General

Adhering to COVID-19 health guidelines: Examining demographic and psychological predictors of adherence.

In Applied psychology. Health and well-being

The effort to limit the spread of the coronavirus (COVID-19) has relied heavily on the general public's compliance with health guidelines limiting social contact and mitigating risk when contact occurs. The aim of this study was to identify latent variables underlying adherence to COVID-19 guidelines and to examine demographic and psychological predictors of adherence. A sample of US adults (N = 1,200) were surveyed in late April to mid-May 2020. The factor structure of adherence was examined using exploratory factor analysis. Machine learning regression models using elastic net regularization were used to examine predictors of adherence. Two factors characterized adherence: avoidance and cleaning. Elastic net models identified differential demographic and psychological predictors of these two forms of adherence. Religious affiliation, denial coping, full-time employment, substance use coping, and being 60 or older predicted lower avoidance adherence. Behavioral and mindfulness emotion regulation skills, agreeableness, and Democrat political affiliation predicted greater avoidance adherence. For cleaning adherence, interpersonal and behavioral emotion regulation skills and conscientiousness emerged as strong predictors of greater cleaning. Efforts to promote compliance with COVID-19 health guidelines may benefit from distinguishing avoidance and cleaning adherence and considering predictors of each of these aspects of adherence.

Bailey Brooklynn, Whelen Megan L, Strunk Daniel R

2021-May-25

COVID-19, health adherence, health behaviors, machine learning

General General

CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes.

In Research square

The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a potential role of Nsp2 in linking viral transcription within the viral replication-transcription complexes (RTC) to the translation initiation of the viral message. Collectively, the structure reported here, combined with mutant interaction mapping, provides a foundation for functional studies of this evolutionary conserved coronavirus protein and may assist future drug design.

Verba Kliment, Gupta Meghna, Azumaya Caleigh, Moritz Michelle, Pourmal Sergei, Diallo Amy, Merz Gregory, Jang Gwendolyn, Bouhaddou Mehdi, Fossati Andrea, Brilot Axel, Diwanji Devan, Hernandez Evelyn, Herrera Nadia, Kratochvil Huong, Lam Victor, Li Fei, Li Yang, Nguyen Henry, Nowotny Carlos, Owens Tristan, Peters Jessica, Rizo Alexandrea, Schulze-Gahmen Ursula, Smith Amber, Young Iris, Yu Zanlin, Asarnow Daniel, Billesbølle Christian, Campbell Melody, Chen Jen, Chen Kuei-Ho, Chio Un Seng, Dickinson Miles, Doan Loan, Jin Mingliang, Kim Kate, Li Junrui, Li Yen-Li, Linossi Edmond, Liu Yanxin, Lo Megan, Lopez Jocelyne, Lopez Kyle, Mancino Adamo, Iii Frank Moss, Paul Michael, Pawar Komal, Pelin Adrian, Pospiech Thomas, Puchades Cristina, Remesh Soumya, Safari Maliheh, Schaefer Kaitlin, Sun Ming, Tabios Mariano, Thwin Aye, Titus Erron, Trenker Raphael, Tse Eric, Tsui Tsz Kin Martin, Feng Feng, Zhang Kaihua, Zhang Yang, Zhao Jianhua, Zhou Fengbo, Zhou Yuan, Zuliani-Alvarez Lorena, Agard David, Cheng Yifan, Fraser James, Jura Natalia, Kortemme Tanja, Manglik Aashish, Southworth Daniel, Stroud Robert, Swaney Danielle, Krogan Nevan, Frost Adam, Rosenberg Oren

2021-May-19

Ophthalmology Ophthalmology

COVID-19 awareness, knowledge and perception towards digital health in an urban multi-ethnic Asian population.

In Scientific reports ; h5-index 158.0

This study aimed to determine COVID-19-related awareness, knowledge, impact and preparedness among elderly Asians; and to evaluate their acceptance towards digital health services amidst the pandemic. 523 participants (177 Malays, 171 Indians, 175 Chinese) were recruited and underwent standardised phone interview during Singapore's lockdown period (07 April till 01 June 2020). Multivariable logistic regression models were performed to evaluate the associations between demographic, socio-economic, lifestyle, and systemic factors, with COVID-19 awareness, knowledge, preparedness, well-being and digital health service acceptance. The average perception score on the seriousness of COVID-19 was 7.6 ± 2.4 (out of 10). 75.5% of participants were aware that COVID-19 carriers can be asymptomatic. Nearly all (≥ 90%) were aware of major prevention methods for COVID-19 (i.e. wearing of mask, social distancing). 66.2% felt prepared for the pandemic, and 86.8% felt confident with government's handling and measures. 78.4% felt their daily routine was impacted. 98.1% reported no prior experience in using digital health services, but 52.2% felt these services would be helpful to reduce non-essential contact. 77.8% were uncomfortable with artificial intelligence software interpreting their medical results. In multivariable analyses, Chinese participants felt less prepared, and more likely felt impacted by COVID-19. Older and lower income participants were less likely to use digital health services. In conclusion, we observed a high level of awareness and knowledge on COVID-19. However, acceptance towards digital health service was low. These findings are valuable for examining the effectiveness of COVID-19 communication in Singapore, and the remaining gaps in digital health adoption among elderly.

Teo Cong Ling, Chee Miao Li, Koh Kai Hui, Tseng Rachel Marjorie Wei Wen, Majithia Shivani, Thakur Sahil, Gunasekeran Dinesh Visva, Nusinovici Simon, Sabanayagam Charumathi, Wong Tien Yin, Tham Yih-Chung, Cheng Ching-Yu

2021-May-24

Pathology Pathology

Transfer transcriptomic signatures for infectious diseases.

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

The modulation of the transcriptome is among the earliest responses to infection. However, defining the transcriptomic signatures of disease is challenging because logistic, technical, and cost factors limit the size and representativeness of samples in clinical studies. These limitations lead to a poor performance of signatures when applied to new datasets. Although the study focuses on infection, the central hypothesis of the work is the generalization of sets of signatures across diseases. We use a machine learning approach to identify common elements in datasets and then test empirically whether they are informative about a second dataset from a disease or process distinct from the original dataset. We identify sets of genes, which we name transfer signatures, that are predictive across diverse datasets and/or species (e.g., rhesus to humans). We demonstrate the usefulness of transfer signatures in two use cases: the progression of latent to active tuberculosis and the severity of COVID-19 and influenza A H1N1 infection. This indicates that transfer signatures can be deployed in settings that lack disease-specific biomarkers. The broad significance of our work lies in the concept that a small set of archetypal human immunophenotypes, captured by transfer signatures, can explain a larger set of responses to diverse diseases.

di Iulio Julia, Bartha Istvan, Spreafico Roberto, Virgin Herbert W, Telenti Amalio

2021-Jun-01

immunophenotype, infection, transcriptomics, transfer learning, vaccination

Public Health Public Health

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19.

In Clinical journal of the American Society of Nephrology : CJASN

BACKGROUND : Acute Kidney Injury treated with dialysis initiation is a common complication of COVID-19 infection among hospitalized patients. However, dialysis supplies and personnel are often limited.

METHODS : Using data from adult hospitalized COVID-19 patients from five hospitals from the Mount Sinai Health System who were admitted from March 10th and December 26th, 2020, we developed and validated several models (logistic regression, LASSO, random forest, and XGBoost (with and without imputation)) for predicting treatment with dialysis or death at various time horizons (1, 3, 5 and 7 days) following hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation while the other hospitals were part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vitals signs within 12 hour of hospital admission.

RESULTS : 6093 patients (2,442 in training and 3,651 in external validation) were included in the final cohort. Of the different model approaches used, XGBoost without imputation had the highest area under the receiver curve on internal validation (range of 0.93-0.98) and area under the precision recall curve (range of 0.78-0.82 across) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range 0.85 to 0.87, and AUPRC range of 0.27 to 0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04 and mean difference in AUPRC of 0.15). Features of creatinine, Blood Urea Nitrogen, and Red cell distribution width were major drivers of model's prediction.

CONCLUSIONS : An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in COVID positive patients had the best performance compared to standard and other machine learning models.

Vaid Akhil, Chan Lili, Chaudhary Kumardeep, Jaladanki Suraj, Paranjpe Ishan, Russak Adam, Kia Arash, Timsina Prem, Levin Matthew, He John, Bottinger Erwin, Charney Alexander, Fayad Zahi, Coca Steven, Glicksberg Benjamin, Nadkarni Girish

2021-May-24

General General

Cough Recognition Based on Mel-Spectrogram and Convolutional Neural Network.

In Frontiers in robotics and AI

In daily life, there are a variety of complex sound sources. It is important to effectively detect certain sounds in some situations. With the outbreak of COVID-19, it is necessary to distinguish the sound of coughing, to estimate suspected patients in the population. In this paper, we propose a method for cough recognition based on a Mel-spectrogram and a Convolutional Neural Network called the Cough Recognition Network (CRN), which can effectively distinguish cough sounds.

Zhou Quan, Shan Jianhua, Ding Wenlong, Wang Chengyin, Yuan Shi, Sun Fuchun, Li Haiyuan, Fang Bin

2021

CNN, COVID-19, audio, cough recognition, deep learning, mel-spectrogram

Public Health Public Health

Review of Current COVID-19 Diagnostics and Opportunities for Further Development.

In Frontiers in medicine

Diagnostic testing plays a critical role in addressing the coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Rapid and accurate diagnostic tests are imperative for identifying and managing infected individuals, contact tracing, epidemiologic characterization, and public health decision making. Laboratory testing may be performed based on symptomatic presentation or for screening of asymptomatic people. Confirmation of SARS-CoV-2 infection is typically by nucleic acid amplification tests (NAAT), which requires specialized equipment and training and may be particularly challenging in resource-limited settings. NAAT may give false-negative results due to timing of sample collection relative to infection, improper sampling of respiratory specimens, inadequate preservation of samples, and technical limitations; false-positives may occur due to technical errors, particularly contamination during the manual real-time polymerase chain reaction (RT-PCR) process. Thus, clinical presentation, contact history and contemporary phyloepidemiology must be considered when interpreting results. Several sample-to-answer platforms, including high-throughput systems and Point of Care (PoC) assays, have been developed to increase testing capacity and decrease technical errors. Alternatives to RT-PCR assay, such as other RNA detection methods and antigen tests may be appropriate for certain situations, such as resource-limited settings. While sequencing is important to monitor on-going evolution of the SARS-CoV-2 genome, antibody assays are useful for epidemiologic purposes. The ever-expanding assortment of tests, with varying clinical utility, performance requirements, and limitations, merits comparative evaluation. We herein provide a comprehensive review of currently available COVID-19 diagnostics, exploring their pros and cons as well as appropriate indications. Strategies to further optimize safety, speed, and ease of SARS-CoV-2 testing without compromising accuracy are suggested. Access to scalable diagnostic tools and continued technologic advances, including machine learning and smartphone integration, will facilitate control of the current pandemic as well as preparedness for the next one.

Mardian Yan, Kosasih Herman, Karyana Muhammad, Neal Aaron, Lau Chuen-Yen

2021

COVID-19, antigen test, clinical, diagnostics, in-vitro assay, molecular test, serologic test

General General

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

In Computational and structural biotechnology journal

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Adamidi Eleni S, Mitsis Konstantinos, Nikita Konstantina S

2021

ABG, Arterial Blood Gas, ADA, Adenosine Deaminase, AI, Artificial Intelligence, ANN, Artificial Neural Networks, APTT, Activated Partial Thromboplastin Time, ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer, AUC, Area Under the Curve, Acc, Accuracy, Adaboost, Adaptive Boosting, Apol AI, Apolipoprotein AI, Apol B, Apolipoprotein B, Artificial intelligence, BNB, Bernoulli Naïve Bayes, BUN, Blood Urea Nitrogen, CI, Confidence Interval, CK-MB, Creatine Kinase isoenzyme, CNN, Convolutional Neural Networks, COVID-19, CPP, COVID-19 Positive Patients, CRP, C-Reactive Protein, CRT, Classification and Regression Decision Tree, CoxPH, Cox Proportional Hazards, DCNN, Deep Convolutional Neural Networks, DL, Deep Learning, DLC, Density Lipoprotein Cholesterol, DNN, Deep Neural Networks, DT, Decision Tree, Diagnosis, ED, Emergency Department, ESR, Erythrocyte Sedimentation Rate, ET, Extra Trees, FCV, Fold Cross Validation, FL, Federated Learning, FiO2, Fraction of Inspiration O2, GBDT, Gradient Boost Decision Tree, GBM light, Gradient Boosting Machine light, GDCNN, Genetic Deep Learning Convolutional Neural Network, GFR, Glomerular Filtration Rate, GFS, Gradient boosted feature selection, GGT, Glutamyl Transpeptidase, GNB, Gaussian Naïve Bayes, HDLC, High Density Lipoprotein Cholesterol, INR, International Normalized Ratio, Inception Resnet, Inception Residual Neural Network, L1LR, L1 Regularized Logistic Regression, LASSO, Least Absolute Shrinkage and Selection Operator, LDA, Linear Discriminant Analysis, LDH, Lactate Dehydrogenase, LDLC, Low Density Lipoprotein Cholesterol, LR, Logistic Regression, LSTM, Long-Short Term Memory, MCHC, Mean Corpuscular Hemoglobin Concentration, MCV, Mean corpuscular volume, ML, Machine Learning, MLP, MultiLayer Perceptron, MPV, Mean Platelet Volume, MRMR, Maximum Relevance Minimum Redundancy, Multimodal data, NB, Naïve Bayes, NLP, Natural Language Processing, NPV, Negative Predictive Values, Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer, OB, Occult Blood test, PCT, Thrombocytocrit, PPV, Positive Predictive Values, PWD, Platelet Distribution Width, PaO2, Arterial Oxygen Tension, Paco2, Arterial Carbondioxide Tension, Prognosis, RBC, Red Blood Cell, RBF, Radial Basis Function, RBP, Retinol Binding Protein, RDW, Red blood cell Distribution Width, RF, Random Forest, RFE, Recursive Feature Elimination, RSV, Respiratory Syncytial Virus, SEN, Sensitivity, SG, Specific Gravity, SMOTE, Synthetic Minority Oversampling Technique, SPE, Specificity, SRLSR, Sparse Rescaled Linear Square Regression, SVM, Support Vector Machine, SaO2, Arterial Oxygen saturation, Screening, TBA, Total Bile Acid, TTS, Training Test Split, WBC, White Blood Cell count, XGB, eXtreme Gradient Boost, k-NN, K-Nearest Neighbor

General General

Big data driven COVID-19 pandemic crisis management: potential approach for global health.

In Archives of medical science : AMS

Introduction : Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively.

Methods : A systematic review guided by PRISMA guidelines has been performed to obtain the key elements.

Results : This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data.

Conclusions : The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.

Lv Yang, Ma Chenwei, Li Xiaohan, Wu Min

2021

artificial intelligence, corona pandemic, digital health, digital technology, information access

General General

Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19.

In Procedia computer science

Artificial intelligence is based on algorithms that enable machines to make decisions instead of humans. This technology improves user experiences in a variety of areas. In this paper we discuss an intelligent solution to predict the performance of Moroccan students in the region of Guelmim Oued Noun through a recommendation system using artificial intelligence techniques during the COVID-19.

Tarik Ahajjam, Aissa Haidar, Yousef Farhaoui

2021

COVID-19, Data Analysis, Data Science, Machine Learning, Recommendation, artificial intelligent, high school, prediction

General General

Using Deep Learning Model for Adapting and Managing COVID-19 Pandemic Crisis.

In Procedia computer science

The purpose of current paper is to create a smart and effective tool for telemedicine to early detect and diagnose COVID-19 disease and therefore help to manage Pandemic Crisis (MCPC) in Sultanate of Oman, as a tool for future pandemic containment. In this paper, we used tools to create robust models in real-time to support Telemedicine, it is Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks using Tensorflow (CNN-TF), and CNN Deployment. These models will assist telemedicine, 1) developing Automated Medical Immediate Diagnosis service (AMID). 2) Analysis of Chest X-rays image (CXRs). 3) Simplifying Classification of confirmed cases according to its severity. 4) Overcoming the lack of experience, by improving the performance of medical diagnostics and providing recommendations to the medical staff. The results show that the best Regression among the five Regression models is Random Forest Regression. while the best classification among the eight classification models and Recurrent Neural Network using Tensorflow (RNNTF) is Random Forest classification, and the best Clustering model among two Clustering models is K-Means++. Furthermore, CNN-TF model was able to discriminate between those with positive cases Covid-19 and those with negative cases.

Alodat Mohammad

2021

COVID-19, Convolutional Neural Networks, Deep Learning, Machine Learning, Sultanate of Oman, Tensorflow

General General

The Efficiency of Learning Methodology for Privacy Protection in Context-aware Environment during the COVID-19 Pandemic.

In Procedia computer science

When the COVID-19 coronavirus hit, the context-aware application users were willing to relax their context privacy preferences during the lockdown to cope their lives while staying home. Such disturbance in the privacy behavior affected the performance of Machine Learning (ML) algorithm that is trained on normal behavior. In this paper, we present the impact of the pandemic on the efficiency of the learning algorithm implementation of a privacy protection system. The system is composed of three modules, in this work we focus on Privacy Preferences Manager (PPM) module which is implemented using hybrid methodology based on a Statistical Model (SM) and Logistic Regression (LR) learning algorithm. The efficiency of the hybrid methodology is assessed using two real-world datasets collected prior and during the COVID-19 pandemic. The results show that the pandemic significantly impacted the efficiency of the hybrid methodology by 13.05% and 15.22% for the accuracy and F1 score respectively.

Alawadhi Ranya, Hussain Tahani

2021

Behavior Recognition, COVID-19, Context-aware, Intelligent System, Logistic Regression, Machine Learning, Privacy, Protection

General General

Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network.

In Soft computing

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a "black-box" method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19.

Souza Alexandra A de, Almeida Danilo Candido de, Barcelos Thiago S, Bortoletto Rodrigo Campos, Munoz Roberto, Waldman Helio, Goes Miguel Angelo, Silva Leandro A

2021-May-17

Covid-19 diagnostic, SARS-CoV-2, Self-organizing maps

General General

NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms.

In Nonlinear dynamics

Nowadays, humanity is facing one of the most dangerous pandemics known as COVID-19. Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the world. Positive patients are often suffering from different symptoms that can vary from mild to severe including cough, fever, sore throat, and body aches. In more dire cases, infected patients can experience severe symptoms that can cause breathing difficulties which lead to stern organ failure and die. The medical corps all over the world are overloaded because of the exponentially myriad number of contagions. Therefore, screening for the disease becomes overwrought with the limited tools of test. Additionally, test results may take a long time to acquire, leaving behind a higher potential for the prevalence of the virus among other individuals by the patients. To reduce the chances of infection, we suggest a prediction model that distinguishes the infected COVID-19 cases based on clinical symptoms and features. This model can be helpful for citizens to catch their infection without the need for visiting the hospital. Also, it helps the medical staff in triaging patients in case of a deficiency of medical amenities. In this paper, we use the non-dominated sorting genetic algorithm (NSGA-II) to select the interesting features by finding the best trade-offs between two conflicting objectives: minimizing the number of features and maximizing the weights of selected features. Then, a classification phase is conducted using an AdaBoost classifier. The proposed model is evaluated using two different datasets. To maximize results, we performed a natural selection of hyper-parameters of the classifier using the genetic algorithm. The obtained results prove the efficiency of NSGA-II as a feature selection algorithm combined with AdaBoost classifier. It exhibits higher classification results that outperformed the existing methods.

Soui Makram, Mansouri Nesrine, Alhamad Raed, Kessentini Marouane, Ghedira Khaled

2021-May-18

AdaBoost, COVID-19 prediction, Feature selection, Hyper-parameters optimization, Machine learning, NSGA-II

General General

Convolutional neural network analysis of recurrence plots for high resolution melting classification.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : High resolution melting (HRM) analysis is a rapid and correct method for identification of species, such as, microorganism, bacteria, yeast, virus, etc. HRM data are produced using real-time polymerase chain reaction (PCR) and unique for each species. Analysis of the HRM data is important for several applications, such as, for detection of diseases (e.g., influenza, zika virus, SARS-Cov-2 and Covid-19 diseases) in health, for identification of spoiled foods in food industry, for analysis of crime scene evidence in forensic investigation, etc. However, the characteristics of the HRM data can change due to the experimental conditions or instrumental settings. In addition, it becomes laborious and time-consuming process as the number of samples increases. Because of these reasons, the analysis and classification of the HRM data become challenging for species which have similar characteristics.

METHODS : To improve the classification accuracy of HRM data, we propose to use image (visual) representation of HRM data, which we call HRM images, that are generated using recurrence plots, and propose convolutional neural network (CNN) based models for classifying HRM images. In this study, two different types of recurrence plots are generated, which are black-white recurrence plots (BW-RP) and gray scale recurrence plots (GS-RP) and four different CNN models are proposed for classifying HRM data.

RESULTS : The classification performance of the proposed methods are evaluated based on average classification accuracy and F1 score, specificity, recall, and precision values for each yeast species. When BW-RP representation of HRM data is used as input to the CNN models, the best classification accuracy of 95.2% is obtained. The classification accuracies of CNN models for melting curve and GS-RP data representations of HRM data are 90.13% and 86.13%, respectively. The classification accuracy of support vector machines (SVM) model that take melting curve representation of HRM data is 86.53%. Moreover, when BW-RP representation of HRM data is used as input to the CNN models, the F1 score, specificity, recall and precision values are the highest for almost all of species.

CONCLUSIONS : Experimental results show that using BW-RP representation of HRM data improved the classification accuracy of HRM data and CNN models that take these images as input outperformed CNN models that take melting curve and GS-RP representations of HRM data as inputs and SVM model that take melting curve representation of HRM data as input.

Ozkok Fatma Ozge, Celik Mete

2021-May-05

Classification, Convolutional neural network, Deep learning, HRM analysis, High resolution melting, Recurrence plot

Surgery Surgery

Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

In The American journal of the medical sciences

BACKGROUND : Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments.

RESEARCH QUESTION : Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death?

METHODS : This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%).

RESULTS : The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website.

INTERPRETATION : This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.

Woo Sang H, Rios-Diaz Arturo J, Kubey Alan A, Cheney-Peters Dianna R, Ackermann Lily L, Chalikonda Divya M, Venkataraman Chantel M, Riley Joshua M, Baram Michael

2021-May-21

COVID-19, SARS-CoV-2, model, risk

General General

Corrigendum: Deep Learning-Based Haptic Guidance for Surgical Skills Transfer.

In Frontiers in robotics and AI

[This corrects the article DOI: 10.3389/frobt.2020.586707.].

Fekri Pedram, Dargahi Javad, Zadeh Mehrdad

2021

COVID-19, LSTM, bone drilling, deep learning, force feedback, haptic, recurrent neural network, surgical skill transfer

oncology Oncology

RoboEthics in COVID-19: A Case Study in Dentistry.

In Frontiers in robotics and AI

The COVID-19 pandemic has caused dramatic effects on the healthcare system, businesses, and education. In many countries, businesses were shut down, universities and schools had to cancel in-person classes, and many workers had to work remotely and socially distance in order to prevent the spread of the virus. These measures opened the door for technologies such as robotics and artificial intelligence to play an important role in minimizing the negative effects of such closures. There have been many efforts in the design and development of robotic systems for applications such as disinfection and eldercare. Healthcare education has seen a lot of potential in simulation robots, which offer valuable opportunities for remote learning during the pandemic. However, there are ethical considerations that need to be deliberated in the design and development of such systems. In this paper, we discuss the principles of roboethics and how these can be applied in the new era of COVID-19. We focus on identifying the most relevant ethical principles and apply them to a case study in dentistry education. DenTeach was developed as a portable device that uses sensors and computer simulation to make dental education more efficient. DenTeach makes remote instruction possible by allowing students to learn and practice dental procedures from home. We evaluate DenTeach on the principles of data, common good, and safety, and highlight the importance of roboethics in Canada. The principles identified in this paper can inform researchers and educational institutions considering implementing robots in their curriculum.

Maddahi Yaser, Kalvandi Maryam, Langman Sofya, Capicotto Nicole, Zareinia Kourosh

2021

COVID-19, DenTeach, dentistry, education, roboethics

General General

Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy?

In Frontiers in robotics and AI

Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient's home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient's homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.

Lambercy Olivier, Lehner Rea, Chua Karen, Wee Seng Kwee, Rajeswaran Deshan Kumar, Kuah Christopher Wee Keong, Ang Wei Tech, Liang Phyllis, Campolo Domenico, Hussain Asif, Aguirre-Ollinger Gabriel, Guan Cuntai, Kanzler Christoph M, Wenderoth Nicole, Gassert Roger

2021

clinical intelligence, decentralized care, neurorehabilitation, robot-assisted therapy (RAT), stroke

General General

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

bioRxiv Preprint

Back and forth transmission of SARS-CoV-2 between humans and animals has the potential to create wild reservoirs of virus that can endanger both long-term control of COVID-19 in people, and vulnerable animal populations that are particularly susceptible to lethal disease. In the near term, SARS-CoV-2 virus variants arising in newly established animal hosts could escape immunity conferred by current human vaccines. In the long-term, animal reservoirs of SARS-CoV-2 increase the overall risk of disease resurgence, making global disease control unlikely. Predicting potential animal host species is key to targeting critical surveillance as well as lab experiments testing susceptibility of potential hosts. A major bottleneck to predicting animal hosts is a paucity of molecular information about the structure of ACE2 across species, a key cellular receptor required for viral cell entry that is highly conserved across thousands of animal species. We overcome this bottleneck by combining 3D modeling of virus and host cell protein interactions with machine learning analysis of species' ecological and biological traits, enabling predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals -- an order of magnitude more species than previously possible. High accuracy model predictions are strongly corroborated by available and emerging in vivo empirical studies. We also identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may facilitate spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. Predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.

Fischhoff, I. R.; Castellanos, A. A.; Rodrigues, J. P. G. L. M.; Varsani, A.; Han, B. A.

2021-05-24

General General

Unsupervised explainable AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome

bioRxiv Preprint

In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel knowledge from big data without prior knowledge or particular models is highly desirable for analyses of genome sequences, particularly for obtaining unexpected insights. We have developed a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions that can reveal various novel genome characteristics. Here, we explain the data mining by the BLSOM: unsupervised and explainable AI. As a specific target, we first selected SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) because a large number of the viral genome sequences have been accumulated via worldwide efforts. We analyzed more than 0.6 million sequences collected primarily in the first year of the pandemic. BLSOMs for short oligonucleotides (e.g., 4~6-mers) allowed separation into known clades, but longer oligonucleotides further increased the separation ability and revealed subgrouping within known clades. In the case of 15-mers, there is mostly one copy in the genome; thus, 15-mers appeared after the epidemic start could be connected to mutations. Because BLSOM is an explainable AI, BLSOM for 15-mers revealed the mutations that contributed to separation into known clades and their subgroups. After introducing the detailed methodological strategies, we explained BLSOMs for various topics. The tetranucleotide BLSOM for over 5 million 5-kb fragment sequences derived from almost all microorganisms currently available and its use in metagenome studies. We also explained BLSOMs for various eukaryotes, such as fishes, frogs and Drosophila species, and found a high separation ability among closely related species. When analyzing the human genome, we found evident enrichments in transcription factor-binding sequences (TFBSs) in centromeric and pericentromeric heterochromatin regions. The tDNAs (tRNA genes) were separated by the corresponding amino acid.

Ikemura, T.; Iwasaki, Y.; Wada, K.; Wada, Y.; Abe, T.

2021-05-24

Radiology Radiology

Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients

ArXiv Preprint

The main objective of this work is to utilize state-of-the-art deep learning approaches for the identification of pulmonary embolism in CTPA-Scans for COVID-19 patients, provide an initial assessment of their performance and, ultimately, provide a fast-track prototype solution (system). We adopted and assessed some of the most popular convolutional neural network architectures through transfer learning approaches, to strive to combine good model accuracy with fast training. Additionally, we exploited one of the most popular one-stage object detection models for the localization (through object detection) of the pulmonary embolism regions-of-interests. The models of both approaches are trained on an original CTPA-Scan dataset, where we annotated of 673 CTPA-Scan images with 1,465 bounding boxes in total, highlighting pulmonary embolism regions-of-interests. We provide a brief assessment of some state-of-the-art image classification models by achieving validation accuracies of 91% in pulmonary embolism classification. Additionally, we achieved a precision of about 68% on average in the object detection model for the pulmonary embolism localization under 50% IoU threshold. For both approaches, we provide the entire training pipelines for future studies (step by step processes through source code). In this study, we present some of the most accurate and fast deep learning models for pulmonary embolism identification in CTPA-Scans images, through classification and localization (object detection) approaches for patients infected by COVID-19. We provide a fast-track solution (system) for the research community of the area, which combines both classification and object detection models for improving the precision of identifying pulmonary embolisms.

Chairi Kiourt, Georgios Feretzakis, Konstantinos Dalamarinis, Dimitris Kalles, Georgios Pantos, Ioannis Papadopoulos, Spyros Kouris, George Ioannakis, Evangelos Loupelis, Petros Antonopoulos, Aikaterini Sakagianni

2021-05-24

General General

Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation.

In Journal of biomedical informatics ; h5-index 55.0

Deep learning based medical image segmentation is an important step within diagnosis, which relies strongly on capturing sufficient spatial context without requiring too complex models that are hard to train with limited labelled data. Training data is in particular scarce for segmenting infection regions of CT images of COVID-19 patients. Attention models help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent criss-cross-attention module aims to approximate global self-attention while remaining memory and time efficient by separating horizontal and vertical self-similarity computations. However, capturing attention from all non-local locations can adversely impact the accuracy of semantic segmentation networks. We propose a new Dynamic Deformable Attention Network (DDANet) that enables a more accurate contextual information computation in a similarly efficient way. Our novel technique is based on a deformable criss-cross attention block that learns both attention coefficients and attention offsets in a continuous way. A deep U-Net [1] segmentation network that employs this attention mechanism is able to capture attention from pertinent non-local locations and also improves the performance on semantic segmentation tasks compared to criss-cross attention within a U-Net on a challenging COVID-19 lesion segmentation task. Our validation experiments show that the performance gain of the recursively applied dynamic deformable attention blocks comes from their ability to capture dynamic and precise attention context. Our DDANet achieves Dice scores of 73.4% and 61.3% for Ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.9% points compared to a baseline U-Net and 24.4% points compared to current state of art methods [2].

Rajamani Kumar T, Siebert Hanna, Heinrich Mattias P

2021-May-19

Attention Mechanism, CCNet, COVID-19, Computed Tomography (CT), Criss-Cross Attention, Deformable Attention, Differentiable Attention Sampling, Ground-glass opacity, Infection, RT-PCR, Segmentation, Semantic Segmentation, U-Net, consolidation

oncology Oncology

Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19.

In NPJ digital medicine

As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.

Subudhi Sonu, Verma Ashish, Patel Ankit B, Hardin C Corey, Khandekar Melin J, Lee Hang, McEvoy Dustin, Stylianopoulos Triantafyllos, Munn Lance L, Dutta Sayon, Jain Rakesh K

2021-May-21

General General

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice.

In AIMS public health

** : The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.

Results : As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds.

Conclusion : A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

Lella Kranthi Kumar, Pja Alphonse

2021

1D CNN, COVID-19, augmentation, data de-noising auto encoder, respiratory sounds

Surgery Surgery

Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling.

In Frontiers in medicine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence-based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives.

Mahboub Bassam, Bataineh Mohammad T Al, Alshraideh Hussam, Hamoudi Rifat, Salameh Laila, Shamayleh Abdulrahim

2021

COVID-19, artificial intelligence, length of stay, predictive analytics, risk of death

General General

Digital health: A panacea in COVID-19 crisis.

In Journal of family medicine and primary care

The whole world is in the grip of the coronavirus disease (COVID-19) outbreak. This pandemic brought visible changes in the life of humans around the globe. Likewise, the medical health sector is forced to use digital technology to continue to provide medical health services by preventing themselves. COVID-19 pandemic highlighted the significance of digitalization in every sphere of life. By focusing on virtual care at a large scale, health care delivery becomes possible and convenient even for remote places. The use of artificial intelligence concepts in this pandemic, like robots replaced human movements and function automatically to guide the patients in the reception area and found helpful to prevent and manage the crowd in a few countries. Similarly, the use of e-earning platform has emerged as a digital solution to impart medical education to medical students in this corona outbreak.

Rani Ruchika, Kumar Rajesh, Mishra Rakhi, Sharma Suresh K

2021-Jan

COVID-19, E-learning, digital health, education, medical, patient

General General

Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment.

In Frontiers in psychology ; h5-index 92.0

The COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people (73% women) from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences (version 24) was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic (before and after the implementation of isolation measures), with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community.

Morales-Rodríguez Francisco Manuel, Martínez-Ramón Juan Pedro, Méndez Inmaculada, Ruiz-Esteban Cecilia

2021

COVID-19, artificial neural networks, coping strategies, educational psychology, evaluation, health emergency, resilience, stress

General General

Analyzing the vast coronavirus literature with CoronaCentral.

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

The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as "long COVID" and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.

Lever Jake, Altman Russ B

2021-Jun-08

coronavirus, literature analysis, literature categorization, machine learning

General General

Converging global crises are forcing the rapid adoption of disruptive changes in drug discovery.

In Drug discovery today ; h5-index 68.0

Spiralling research costs combined with urgent pressures from the Coronavirus 2019 (COVID-19) pandemic and the consequences of climate disruption are forcing changes in drug discovery. Increasing the predictive power of in vitro human assays and using them earlier in discovery would refocus resources on more successful research strategies and reduce animal studies. Increasing laboratory automation enables effective social distancing for researchers, while allowing integrated data capture from remote laboratory networks. Such disruptive changes would not only enable more cost-effective drug discovery, but could also reduce the overall carbon footprint of discovering new drugs.

Mark Treherne J, Langley Gillian R

2021-May-17

AOPs, Covid-19, advanced human cell and tissue models, artificial intelligence, climate disruption, decision theory, drug pipeline attrition, laboratory automation, machine learning

General General

COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning.

In PloS one ; h5-index 176.0

INTRODUCTION : There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often associated with kidney failure.

METHODS : We assessed whether proteinuria can be predictive of kidney failure, the development of chronic kidney disease, and mortality in 37 critically ill COVID-19 patients. We used machine learning (ML) methods as decision trees and cut-off points created by the OneR package to add new aspects, even in smaller cohorts.

RESULTS : Among a total of 37 patients, 24 suffered higher-grade renal failure, 20 of whom required kidney replacement therapy. More than 40% of patients remained on hemodialysis after intensive care unit discharge or died (27%). Due to frequent anuria proteinuria measured in two-thirds of the patients, it was not predictive for the investigated endpoints; albuminuria was higher in patients with AKI 3, but the difference was not significant. ML found cut-off points of >31.4 kg/m2 for BMI and >69 years for age, constructed decision trees with great accuracy, and identified highly predictive variables for outcome and remaining chronic kidney disease.

CONCLUSIONS : Different ML methods and their clinical application, especially decision trees, can provide valuable support for clinical decisions. Presence of proteinuria was not predictive of CKD or AKI and should be confirmed in a larger cohort.

Herzog Anna Laura, von Jouanne-Diedrich Holger K, Wanner Christoph, Weismann Dirk, Schlesinger Tobias, Meybohm Patrick, Stumpner Jan

2021

General General

Forecasting COVID-19 cases: A comparative analysis between Recurrent and Convolutional Neural Networks.

In medRxiv : the preprint server for health sciences

When the entire world is waiting restlessly for a safe and effective COVID-19 vaccine that could soon become a reality, numerous countries around the globe are grappling with unprecedented surges of new COVID-19 cases. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies are posing new challenges to the government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly in order to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It has been unearthed in our study that CNN can provide robust long term forecasting results in time series analysis due to its capability of essential features learning, distortion invariance and temporal dependence learning. However, the prediction accuracy of LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when it comes to forecasting with very few features and less amount of historical data.

Nabi Khondoker Nazmoon, Tahmid Md Toki, Rafi Abdur, Kader Muhammad Ehsanul, Haider Md Asif

2021-Feb-20

General General

CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes.

In bioRxiv : the preprint server for biology

The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a potential role of Nsp2 in linking viral transcription within the viral replication-transcription complexes (RTC) to the translation initiation of the viral message. Collectively, the structure reported here, combined with mutant interaction mapping, provides a foundation for functional studies of this evolutionary conserved coronavirus protein and may assist future drug design.

Gupta Meghna, Azumaya Caleigh M, Moritz Michelle, Pourmal Sergei, Diallo Amy, Merz Gregory E, Jang Gwendolyn, Bouhaddou Mehdi, Fossati Andrea, Brilot Axel F, Diwanji Devan, Hernandez Evelyn, Herrera Nadia, Kratochvil Huong T, Lam Victor L, Li Fei, Li Yang, Nguyen Henry C, Nowotny Carlos, Owens Tristan W, Peters Jessica K, Rizo Alexandrea N, Schulze-Gahmen Ursula, Smith Amber M, Young Iris D, Yu Zanlin, Asarnow Daniel, Billesbølle Christian, Campbell Melody G, Chen Jen, Chen Kuei-Ho, Chio Un Seng, Dickinson Miles Sasha, Doan Loan, Jin Mingliang, Kim Kate, Li Junrui, Li Yen-Li, Linossi Edmond, Liu Yanxin, Lo Megan, Lopez Jocelyne, Lopez Kyle E, Mancino Adamo, Moss Frank R, Paul Michael D, Pawar Komal Ishwar, Pelin Adrian, Pospiech Thomas H, Puchades Cristina, Remesh Soumya Govinda, Safari Maliheh, Schaefer Kaitlin, Sun Ming, Tabios Mariano C, Thwin Aye C, Titus Erron W, Trenker Raphael, Tse Eric, Tsui Tsz Kin Martin, Wang Feng, Zhang Kaihua, Zhang Yang, Zhao Jianhua, Zhou Fengbo, Zhou Yuan, Zuliani-Alvarez Lorena, Agard David A, Cheng Yifan, Fraser James S, Jura Natalia, Kortemme Tanja, Manglik Aashish, Southworth Daniel R, Stroud Robert M, Swaney Danielle L, Krogan Nevan J, Frost Adam, Rosenberg Oren S, Verba Kliment A

2021-May-11

Public Health Public Health

Retro Drug Design: From Target Properties to Molecular Structures.

In bioRxiv : the preprint server for biology

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate μ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

Wang Yuhong, Michael Sam, Huang Ruili, Zhao Jinghua, Recabo Katlin, Bougie Danielle, Shu Qiang, Shinn Paul, Sun Hongmao

2021-May-12

General General

Design of Specific Primer Sets for the Detection of B.1.1.7, B.1.351, P.1, B.1.617.2 and B.1.1.519 Variants of SARS-CoV-2 using Artificial Intelligence

bioRxiv Preprint

As the COVID-19 pandemic continues, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Variant B.1.1.7 lineage clade GR from Global Initiative on Sharing All Influenza Data (GISAID) was first detected in the UK, and it appears to possess an increased transmissibility. At the same time, South African authorities reported variant B.1.351, that shares several mutations with B.1.1.7, and might also present high transmissibility. Earlier this year, a variant labeled P.1 with 17 non-synonymous mutations was detected in Brazil. Recently the World Health Organization has raised concern for the variants B.1.617.2 mainly detected in India but now exported worldwide. It is paramount to rapidly develop specific molecular tests to uniquely identify new variants. Using a completely automated pipeline built around deep learning and evolutionary algorithms techniques, we designed primer sets specific to variants B.1.1.7, B.1.351, P.1, B.1.617.2 and B.1.1.519 respectively. We wish to raise awareness to the variant of interest, B.1.1.519, which contain several mutations of important threat to public health. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for each variant. In-silico tests show that the sequences in the primer sets present high accuracy and are based on 2 mutations or more. In addition, we present an analysis of key mutations for SARS-CoV-2 variants. The presented methodology can be exploited to swiftly obtain primer sets for each new variant, that can later be a part of a multiplexed approach for the initial diagnosis of COVID-19 patients.

Lopez-Rincon, A.; Perez-Romero, C.; Tonda, A.; Mendoza-Maldonado, L.; Claassen, E.; Garssen, J.; Kraneveld, A. D.

2021-05-21

General General

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

In Research square

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R . For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

Zhang-James Yanli, Hess Jonathan, Salkin Asif, Wang Dongliang, Chen Samuel, Winkelstein Peter, Morley Christopher P, Faraone Stephen V

2021-Apr-26

General General

Water-triggered, irreversible conformational change of SARS-CoV-2 main protease on passing from the solid state to aqueous solution

bioRxiv Preprint

The main protease from SARS-CoV-2 is a homodimer. Yet, a recent 0.1 ms long molecular dynamics simulation shows that it readily undergoes a symmetry breaking event on passing from the solid state to the aqueous solution. As a result, the subunits present distinct conformations of the binding pocket. By analysing this long time simulation, here we uncover a previously unrecognised role of water molecules in triggering the transition. Interestingly, each subunit presents a different collection of long-lived water molecules. Enhanced sampling methods performed here, along with machine learning approaches, further establish that the transition to the asymmetric state is essentially irreversible.

Ansari, N.; Rizzi, V.; Carloni, P.; Parrinello, M.

2021-05-21

General General

Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.

In Nucleic acids research ; h5-index 217.0

Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.

Ong Edison, Cooke Michael F, Huffman Anthony, Xiang Zuoshuang, Wong Mei U, Wang Haihe, Seetharaman Meenakshi, Valdez Ninotchka, He Yongqun

2021-May-01

General General

DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.

In Briefings in bioinformatics

Cytolytic T-cells play an essential role in the adaptive immune system by seeking out, binding and killing cells that present foreign antigens on their surface. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life-threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native peptides to elicit a T-cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen alleles, for both synthetic biological applications, and to augment real training datasets. Here, we propose a beta-binomial distribution approach to derive peptide immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, K-nearest neighbors, support vector machine, Random Forest and AdaBoost) and three deep learning models (convolutional neural network (CNN), Residual Net and graph neural network) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-CoV-2). We chose the CNN as the best prediction model, based on its adaptivity for small and large datasets and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepImmuno-CNN correctly predicts which residues are most important for T-cell antigen recognition and predicts novel impacts of SARS-CoV-2 variants. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physicochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.

Li Guangyuan, Iyer Balaji, Prasath V B Surya, Ni Yizhao, Salomonis Nathan

2021-May-03

COVID-19, convolutional neural network, deep learning, generative adversarial network, immunogenicity, neoantigen

oncology Oncology

Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The emergence of artificial intelligence (AI) in health care has impacted health care systems, including employment, training, education, and professional regulation. It is incumbent on health professional associations to assist their membership in defining and preparing for AI-related change. Health professional associations, or the national groups convened to represent the interests of the members of a profession, play a unique role in establishing the sociocultural, normative, and regulative elements of health care professions.

OBJECTIVE : The aim of this paper is to present a protocol for a proposed study of how, when faced with AI as a disruptive technology, health professional associations engage in sensemaking and legitimization of change to support their membership in preparing for future practice.

METHODS : An exploratory multi-case study approach will be used. This study will be informed by the normalization process theory (NPT), which suggests behavioral constructs required for complex change, providing a novel lens through which to consider the agency of macrolevel actors in practice change. A total of 4 health professional associations will be studied, each representing an instrumental case and related fields selected for their early consideration of AI technologies. Data collection will consist of key informant interviews, observation of relevant meetings, and document review. Individual and collective sensemaking activities and action toward change will be identified using stakeholder network mapping. A hybrid inductive and deductive model will be used for a concurrent thematic analysis, mapping emergent themes against the NPT framework to assess fit and identify areas of discordance.

RESULTS : As of January 2021, we have conducted 17 interviews, with representation across the 4 health professional associations. Of these 17 interviews, 15 (88%) have been transcribed. Document review is underway and complete for one health professional association and nearly complete for another. Observation opportunities have been challenged by competing priorities during COVID-19 and may require revisiting. A linear cross-case analytic approach will be taken to present the data, highlighting both guidance for the implementation of AI and implications for the application of NPT at the macro level. The ability to inform consideration of AI will depend on the degree to which the engaged health professional associations have considered this topic at the time of the study and, hence, what priority it has been assigned within the health professional association and what actions have been taken to consider or prepare for it. The fact that this may differ between health professional associations and practice environments will require consideration throughout the analysis.

CONCLUSIONS : Ultimately, this protocol outlines a case study approach to understand how, when faced with AI as a disruptive technology, health professional associations engage in sensemaking and legitimization of change to support their membership in preparing for future practice.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : DERR1-10.2196/27340.

Gillan Caitlin, Hodges Brian, Wiljer David, Dobrow Mark

2021-May-19

artificial intelligence, case study, health professions, normalization process theory

General General

Detection of COVID-19 in X-Ray Images by Classification of Bag of Visual Words Using Neural Networks.

In Biomedical signal processing and control

Coronavirus disease 2019 (COVID-19) was classified as a pandemic by the World Health Organization in March 2020. Given that this novel virus most notably affects the human respiratory system, early detection may help prevent severe lung damage, save lives, and help prevent further disease spread. Given the constraints on the healthcare facilities and staff, the role of artificial intelligence for automatic diagnosis is critical. The automatic diagnosis of COVID-19 based on medical images is, however, not straightforward. Due to the novelty of the disease, available X-ray datasets are very limited. Furthermore, there is a significant similarity between COVID-19 X-rays and other lung infections. In this paper, these challenges are addressed by proposing an approach consisting of a bag of visual words and a neural network classifier. The proposed method can classify X-ray chest images into non-COVID-19 and COVID-19 with high performance. Three public datasets are used to evaluate the proposed approach. Our best accuracy on the first, second, and third datasets is 96.1, 99.84, and 98 percent. Since detection of COVID-19 is important, sensitivity is used as a criterion. The proposed method's best sensitivities are 90.32, 99.65, and 91 percent on these datasets, respectively. The experimental results show that extracting features with the bag of visual words results in better classification accuracy than the state-of-the-art techniques.

Nabizadeh-Shahre-Babak Zahra, Karimi Nader, Khadivi Pejman, Roshandel Roshanak, Emami Ali, Samavi Shadrokh

2021-May-14

Bag of Visual Words, COVID-19, Classifier, Coronavirus

General General

An Intelligent Centrality Measures for Influential Node Detection in COVID-19 Environment.

In Wireless personal communications

With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure's (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.

Jeyasudha J, Usha G

2021-May-13

Influential nodes, Intelligent centrality measures, Machine learning, Support vector machines

General General

Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems

ArXiv Preprint

With the rapid development in artificial intelligence, social computing has evolved beyond social informatics toward the birth of social intelligence systems. This paper, therefore, takes initiatives to propose a social behaviour understanding framework with the use of deep neural networks for social and behavioural analysis. The integration of information fusion, person and object detection, social signal understanding, behaviour understanding, and context understanding plays a harmonious role to elicit social behaviours. Three systems, including depression detection, activity recognition and cognitive impairment screening, are developed to evidently demonstrate the importance of social intelligence. The study considerably contributes to the cumulative development of social computing and health informatics. It also provides a number of implications for academic bodies, healthcare practitioners, and developers of socially intelligent agents.

Ethan Lim Ding Feng, Zhi-Wei Neo, Aaron William De Silva, Kellie Sim, Hong-Ray Tan, Thi-Thanh Nguyen, Karen Wei Ling Koh, Wenru Wang, Hoang D. Nguyen

2021-05-20

General General

Vaccine-escape and fast-growing mutations in the United Kingdom, the United States, Singapore, Spain, India, and other COVID-19-devastated countries.

In Genomics

Recently, the SARS-CoV-2 variants from the United Kingdom (UK), South Africa, and Brazil have received much attention for their increased infectivity, potentially high virulence, and possible threats to existing vaccines and antibody therapies. The question remains if there are other more infectious variants transmitted around the world. We carry out a large-scale study of 506,768 SARS-CoV-2 genome isolates from patients to identify many other rapidly growing mutations on the spike (S) protein receptor-binding domain (RBD). We reveal that essentially all 100 most observed mutations strengthen the binding between the RBD and the host angiotensin-converting enzyme 2 (ACE2), indicating the virus evolves toward more infectious variants. In particular, we discover new fast-growing RBD mutations N439K, S477N, S477R, and N501T that also enhance the RBD and ACE2 binding. We further unveil that mutation N501Y involved in United Kingdom (UK), South Africa, and Brazil variants may moderately weaken the binding between the RBD and many known antibodies, while mutations E484K and K417N found in South Africa and Brazilian variants, L452R and E484Q found in India variants, can potentially disrupt the binding between the RBD and many known antibodies. Among these RBD mutations, L452R is also now known as part of the California variant B.1.427. Finally, we hypothesize that RBD mutations that can simultaneously make SARS-CoV-2 more infectious and disrupt the existing antibodies, called vaccine escape mutations, will pose an imminent threat to the current crop of vaccines. A list of most likely vaccine escape mutations is given, including S494P, Q493L, K417N, F490S, F486L, R403K, E484K, L452R, K417T, F490L, E484Q, and A475S. Mutation T478K appears to make the Mexico variant B.1.1.222 the most infectious one. Our comprehensive genetic analysis and protein-protein binding study show that the genetic evolution of SARS-CoV-2 on the RBD, which may be regulated by host gene editing, viral proofreading, random genetic drift, and natural selection, gives rise to more infectious variants that will potentially compromise existing vaccines and antibody therapies.

Wang Rui, Chen Jiahui, Gao Kaifu, Wei Guo-Wei

2021-May-15

Antibody, Binding affinity, COVID-19, Deep learning, Mutation, Persistent homology, SARS-CoV-2, Vaccine escape

General General

Primary Care Provider-Reported Prevalence of Vaccine and Polyethylene-Glycol Allergy in Canada.

In Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology

BACKGROUND : The COVID-19 pandemic has highlighted the importance of accurate capture of vaccine, and vaccine component, allergy. There remains a gap in the prevalence literature from the perspective of direct primary care provider (PCP) reporting at a population level.

OBJECTIVE : To determine the prevalence of PCP-documented vaccine and polyethylene glycol (PEG) allergy using Electronic Medical Record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).

METHODS : Retrospective cohort study using the CPCSSN repository. Machine learning algorithms were applied to assess for vaccine allergy documentation, and ATC codes used for PEG allergy or allergy to common injectable medications containing PEG (CIMCP).

RESULTS : The prevalence of PCP-documented vaccine allergy in Canada was 0.037%, (395/1,055,677) and of PEG allergy was 0.0009% (10/1,055,677). In total, 0.01% of patients had a documented allergy to either PEG or CIMCP (135/1,055,677). None of the patients with PEG allergy had a documented allergy to a CIMCP. Patients with vaccine allergy and PEG allergy were significantly more likely to have other atopic comorbidities including asthma (P<.0001 for both), eczema (P<.0001 and P=.001 respectively), rhinitis (P=.002 and P<.0001 respectively) and food allergy (P<.0001 for both). Significantly higher rates of depression (P=.0005 and P<.0001 respectively) and anxiety (P=.003 and P<.0001) were seen in those with vaccine allergy, or PEG allergy, compared to those without.

CONCLUSION : This is the first study to estimate the prevalence of vaccine and PEG allergy in a national cohort that utilizes PCP documentation, demonstrating a low reported rate of vaccine allergy and PEG allergy.

Abrams Elissa M, Greenhawt Matthew, Shaker Marcus, Kosowan Leanne, Singer Alexander G

2021-May-15

PEG allergy, vaccine allergy

Radiology Radiology

Differential impact of COVID-19 on cancer diagnostic services based on body regions - A public facility-based study in Hong Kong.

In International journal of radiation oncology, biology, physics

PURPOSE : A reduction in cancer services during the COVID-19 pandemic has impacted cancer diagnosis. The purpose of this study is to quantitatively determine the impact on cancer diagnostic service in public facilities across Hong Kong. Quantifying the temporal changes in the number of cancer diagnosis before, during and after the outbreak, is useful to establish the scale of the problem and to assess if there has been an adequate level of response.

METHODS AND MATERIALS : Retrospective cohort study using a territory-wide database in Hong Kong from 2017 to 2020 using consecutive specimens received for pathological diagnosis in public laboratories in 41 hospitals were retrieved.

RESULTS : In 2020, a total of 455,453 pathological specimens were received, which amounted to a 15.5% reduction compared to prior 3-year average (p-value < 0.001). Analysis on confirmed malignant pathological diagnosis revealed a statistically significant reduction in colorectal (-10.0%, p-value < 0.001), prostate (-19.7%, p-value < 0.001), non-significant reduction for lung (-3.0%, p-value = 0.0526), and a marginal but non-significant increase for breast (0.7%, p-value = 0.7592) regions. Based on time series projection data, the estimated missed cancers for the 3 regions with reduced investigations were colorectal (10.0%), lung (3.0%) and prostate (19.7%).

CONCLUSIONS : Variable impact on actual malignant pathological diagnoses based on 4 body regions was observed, with a statistically significant reduction in colorectal, lung and prostate regions, marginal but insignificant increase in breast regions. The findings could help public health policy future planning and intervention.

Vardhanabhuti Varut, Ng Kei Shing

2021-May-15

General General

Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19.

In Risk analysis : an official publication of the Society for Risk Analysis

This Special Issue is dedicated to issues and challenges related to pandemic risk and resilience, with a focus on policy and operations of global systems in the COVID-19 pandemic. The cascading effects of emerging and reemerging infectious diseases to the global economy are a critical interest. Measures to confront the ongoing pandemic are an urgent need. Data analysis at regional and global scales is helping to prioritize response and resilience across locations of high risks. The risk sciences are available for addressing human health and infection risks; the evaluation of risk management strategies and tradeoffs; risk perception as it relates to information processing and receiving risk communication; and tracking system resilience as it relates to various imposed measures.

Wu Desheng Dash, Mitchell Jade, Lambert James H

2021-May

Data analytics, artificial intelligence, cascading effects, engineering systems, pandemic, resilience, risk analysis

oncology Oncology

Personalized Prediction of Hospital Mortality in COVID-19 positive patients.

In Mayo Clinic proceedings. Innovations, quality & outcomes

Objective : To develop predictive models for in-hospital mortality and length of stay (LOS) for COVID-19 positive patients.

Patients and Methods : We performed a multicenter retrospective cohort study of hospitalized COVID-19 positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from 03/09/2020 to 05/20/2020 who had reverse transcriptase-polymerase chain reaction proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7 days, 14 days, and 30 days of hospitalization) and in-hospital LOS.

Results : Among 764 patients, 116 (15%) either died (n = 87) or were transitioned to hospice care (n = 29) during their hospitalization. The median LOS was 5 days (range 1 - 44 days) for patients admitted to the regular nursing floor and 10 days (range 1-38 days) for patients admitted to the intensive care unit (ICU). Patients who died during hospitalization were older, initially admitted to the ICU, more likely to be white and to have worse organ dysfunction compared to patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the Receiver Operating Characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort.

Conclusions : We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19 positive patients. The model can aid healthcare systems in bed allocation and distribution of vital resources.

Rozenbaum Daniel, Shreve Jacob, Radakovich Nathan, Douggal Abhijit, Jehi Lara, Nazha Aziz

2021-May-12

COVID-19, COVID-19, Coronavirus disease 2019, ICU, Intensive care unit, LOS, Length of stay, Personalized prediction, ROC AUC, Area under the Receiver Operating Characteristics curve, mortality

General General

Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

ArXiv Preprint

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anatomy with the likelihood maps (i.e., heatmaps). However, most current solutions overlook another essence of heatmap regression, the objective metric for regressing target heatmaps and rely on hand-crafted heuristics to set the target precision, thus being usually cumbersome and task-specific. In this paper, we propose a novel learning-to-learn framework for landmark detection to optimize the neural network and the target precision simultaneously. The pivot of this work is to leverage the reinforcement learning (RL) framework to search objective metrics for regressing multiple heatmaps dynamically during the training process, thus avoiding setting problem-specific target precision. We also introduce an early-stop strategy for active termination of the RL agent's interaction that adapts the optimal precision for separate targets considering exploration-exploitation tradeoffs. This approach shows better stability in training and improved localization accuracy in inference. Extensive experimental results on two different applications of landmark localization: 1) our in-house prenatal ultrasound (US) dataset and 2) the publicly available dataset of cephalometric X-Ray landmark detection, demonstrate the effectiveness of our proposed method. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.

Guang-Quan Zhou, Juzheng Miao, Xin Yang, Rui Li, En-Ze Huo, Wenlong Shi, Yuhao Huang, Jikuan Qian, Chaoyu Chen, Dong Ni

2021-05-19

Radiology Radiology

Joint Segmentation and Detection of COVID-19 via a Sequential Region Generation Network.

In Pattern recognition

The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.

Wu Jipeng, Zhang Shengchuan, Li Xi, Chen Jie, Xu Haibo, Zheng Jiawen, Gao Yue, Tian Yonghong, Liang Yongsheng, Ji Rongrong

2021-May-13

COVID-19, Context Enhancement, Detection, Edge Loss, Segmentation

General General

Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care

ArXiv Preprint

Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. Basically, we modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, a reinforcement learning based oxygen control policy is learned and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1,372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021. The mean mortality rate under the RL algorithm is lower than standard of care by 2.57% (95% CI: 2.08- 3.06) reduction (P<0.001) from 7.94% under the standard of care to 5.37 % under our algorithm and the averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14-1.42) lower than the rate actually delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic.

Hua Zheng, Jiahao Zhu, Wei Xie, Judy Zhong

2021-05-19

General General

Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena.

In Scientific reports ; h5-index 158.0

The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-19 misinformation statistics are well fitted by a log-normal distribution. Secondly, we form misinformation networks by taking individual misinformation as a node and similarity between misinformation nodes as links, and we decipher the laws of COVID-19 misinformation network evolution: (1) We discover that misinformation evolves to optimize the network information transfer over time with the sacrifice of robustness. (2) We demonstrate the co-existence of fit get richer and rich get richer phenomena in misinformation networks. (3) We show that a misinformation network evolution with node deletion mechanism captures well the public attention shift on social media. Lastly, we present a network science inspired deep learning framework to accurately predict which Twitter posts are likely to become central nodes (i.e., high centrality) in a misinformation network from only one sentence without the need to know the whole network topology. With the network analysis and the central node prediction, we propose that if we correctly suppress certain central nodes in the misinformation network, the information transfer of network would be severely impacted.

Cheng Mingxi, Yin Chenzhong, Nazarian Shahin, Bogdan Paul

2021-May-17

General General

Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies

ArXiv Preprint

In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeking to resort to NLP to monitor these outlets. We propose for the first time the use of the SpanBERT architecture for the task of ADE extraction: this new version of the popular BERT transformer showed improved capabilities with multi-token text spans. We validate our hypothesis with experiments on two datasets (SMM4H and CADEC) with different text typologies (tweets and blog posts), finding that SpanBERT combined with a CRF outperforms all the competitors on both of them.

Beatrice Portelli, Daniele Passabì, Edoardo Lenzi, Giuseppe Serra, Enrico Santus, Emmanuele Chersoni

2021-05-19

Internal Medicine Internal Medicine

Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".

In Journal of medical Internet research ; h5-index 88.0

NA.

Martos Pérez Francisco, Gomez Huelgas Ricardo, Martín Escalante María Dolores, Casas Rojo José Manuel

2021-May-13

General General

Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19.

In AJOB neuroscience

Well before COVID-19, there was growing excitement about the potential of various digital technologies such as tele-health, smartphone apps, or AI chatbots to revolutionize mental healthcare. As the SARS-CoV-2 virus spread across the globe, clinicians warned of the mental illness epidemic within the coronavirus pandemic. Now, funding for digital mental health technologies is surging and many researchers are calling for widespread adoption to address the mental health sequelae of COVID-19. Reckoning with the ethical implications of these technologies is urgent because decisions made today will shape the future of mental health research and care for the foreseeable future. We contend that the most pressing ethical issues concern (1) the extent to which these technologies demonstrably improve mental health outcomes and (2) the likelihood that wide-scale adoption will exacerbate the existing health inequalities laid bare by the pandemic. We argue that the evidence for efficacy is weak and that the likelihood of increasing inequalities is high. First, we review recent trends in digital mental health. Next, we turn to the clinical literature to show that many technologies proposed as a response to COVID-19 are unlikely to improve outcomes. Then, we argue that even evidence-based technologies run the risk of increasing health disparities. We conclude by suggesting that policymakers should not allocate limited resources to the development of many digital mental health tools and should focus instead on evidence-based solutions to address mental health inequalities.

Skorburg Joshua August, Yam Josephine

2021-May-14

Bioethics, artificial intelligence, mental health, psychiatry

Radiology Radiology

Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.

In Japanese journal of radiology

PURPOSE : To evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)-based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia.

MATERIALS AND METHODS : This retrospective study included 100 SARS-CoV-2-positive patients with mild (n = 23), moderate (n = 37) or severe (n = 40) pneumonia classified according to the Japanese guidelines. Univariate Kruskal-Wallis and multivariate ordinal logistic analyses were used to examine whether CT parameters (opacity score, volume of opacity, % opacity, volume of high opacity, % high opacity and mean HU total on CT) as well as blood test parameters [procalcitonin, estimated glomerular filtration rate (eGFR), C-reactive protein, % lymphocyte, ferritin, aspartate aminotransferase, lactate dehydrogenase, alanine aminotransferase, creatine kinase, hemoglobin A1c, prothrombin time, activated partial prothrombin time (APTT), white blood cell count and creatinine] differed by disease severity.

RESULTS : All CT parameters and all blood test parameters except procalcitonin and APPT were significantly different among mild, moderate and severe groups. By multivariate analysis, mean HU total and eGFR were two independent factors associated with severity (p < 0.0001). Cutoff values for mean HU total and eGFR were, respectively, - 801 HU and 77 ml/min/1.73 m2 between mild and moderate pneumonia and - 704 HU and 53 ml/min/1.73 m2 between moderate and severe pneumonia.

CONCLUSION : The mean HU total of the whole lung, determined by the AI algorithm, and eGFR reflect the severity of COVID-19 pneumonia.

Okuma Tomohisa, Hamamoto Shinichi, Maebayashi Tetsunori, Taniguchi Akishige, Hirakawa Kyoko, Matsushita Shu, Matsushita Kazuki, Murata Katsuko, Manabe Takao, Miki Yukio

2021-May-14

COVID-19, Chest CT, Deep learning, Quantitative analysis

General General

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

In Optics letters

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are segmented for feature extraction, then a bi-directional long short-term memory network is used to classify between healthy and COVID positive red blood cells based on their spatiotemporal behavior. Individuals are then classified based on the simple majority of their cells' classifications. The proposed system may be beneficial for under-resourced healthcare systems. To the best of our knowledge, this is the first report of digital holographic microscopy for rapid screening of COVID-19.

O’Connor Timothy, Shen Jian-Bing, Liang Bruce T, Javidi Bahram

2021-May-15

Surgery Surgery

COVID-19 … What are drugs and strategies now?

In Acta bio-medica : Atenei Parmensis

From February 2019 the World faces the Covid19 pandemic. The data in our possession are still insufficient to effectively combat this pathology. The gold standard for diagnosis remains molecular testing, while clinical and instrumental and serological diagnostics are highly nonspecific leading to a slowdown in the battle against covid19.[3] Can Artificial Intelligence (AI) and Machine Learning (ML) help us? The use of large databases to cross-reference data to stratify the diagnostic scores, to quickly differentiate a critical Covid-19 patient from a non-critical one is the challenge of the future. All to achieve better management of resources in the field and a more effective therapeutic approach.[2].

Craca Michelangelo, Bignami Elena, Bellini Valentina, Saturno Francesco, Potì Francesco, Cortegiani Andrea, Vetrugno Luigi, Vetrugno Luigi

2021-May-12

General General

Fast prototyping of a local fuzzy search system for decision support and retraining of hospital staff during pandemic.

In Health information science and systems

Purpose : The COVID-19 pandemic showed an urgent need for decision support systems to help doctors at a time of stress and uncertainty. However, significant differences in hospital conditions, as well as skepticism of doctors about machine learning algorithms, limit their introduction into clinical practice. Our goal was to test and apply the principle of "patient-like-mine" decision support in rapidly changing conditions of a pandemic.

Methods : In the developed system we implemented a fuzzy search that allows a doctor to compare their medical case with similar cases recorded in their medical center since the beginning of the pandemic. Various distance metrics were tried for obtaining clinically relevant search results. With the use of R programming language, we designed the first version of the system in approximately a week. A set of features for the comparison of the cases was selected with the use of random forest algorithm implemented in Caret. Shiny package was chosen for the design of GUI.

Results : The deployed tool allowed doctors to quickly estimate the current conditions of their patients by means of studying the most similar previous cases stored in the local health information system. The extensive testing of the system during the first wave of COVID-19 showed that this approach helps not only to draw a conclusion about the optimal treatment tactics and to train medical staff in real-time but also to optimize patients' individual testing plans.

Conclusions : This project points to the possibility of rapid prototyping and effective usage of "patient-like-mine" search systems at the time of a pandemic caused by a poorly known pathogen.

Bakin Evgeny A, Stanevich Oksana V, Danilenko Daria M, Lioznov Dmitry A, Kulikov Alexander N

2021-Dec

COVID-19, Decision support algorithm, Fuzzy search, Patient-like-mine, Prototyping

Radiology Radiology

Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients.

In Clinical imaging

BACKGROUND : The aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome.

METHODS : Consecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge.

RESULTS : Seventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = -0.234, p = 0.04) and increased semi-consolidation ratio (rho = -0.303, p = 0.008). Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge.

CONCLUSION : Quantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.

Esposito Antonio, Palmisano Anna, Cao Roberta, Rancoita Paola, Landoni Giovanni, Grippaldi Daniele, Boccia Edda, Cosenza Michele, Messina Antonio, La Marca Salvatore, Palumbo Diego, Di Serio Clelia, Spessot Marzia, Tresoldi Moreno, Scarpellini Paolo, Ciceri Fabio, Zangrillo Alberto, De Cobelli Francesco

2021-Apr-29

Artificial intelligence, Covid-19, Outcome, Pneumonia, Quantitative CT

General General

Joint learning of 3D lesion segmentation and classification for explainable COVID-19 diagnosis.

In IEEE transactions on medical imaging ; h5-index 74.0

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.

Wang Xiaofei, Jiang Lai, Li Liu, Xu Mai, Deng Xin, Dai Lisong, Xu Xiangyang, Li Tianyi, Guo Yichen, Wang Zulin, Dragotti Pier Luigi

2021-May-13

Radiology Radiology

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.

In NPJ digital medicine

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Shamout Farah E, Shen Yiqiu, Wu Nan, Kaku Aakash, Park Jungkyu, Makino Taro, Jastrzębski Stanisław, Witowski Jan, Wang Duo, Zhang Ben, Dogra Siddhant, Cao Meng, Razavian Narges, Kudlowitz David, Azour Lea, Moore William, Lui Yvonne W, Aphinyanaphongs Yindalon, Fernandez-Granda Carlos, Geras Krzysztof J

2021-May-12

General General

Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model.

In Scientific reports ; h5-index 158.0

In this paper, we have implemented a large-scale agent-based model to study the outbreak of coronavirus infectious diseases (COVID-19) in Singapore, taking into account complex human interaction pattern. In particular, the concept of multiplex network is utilized to differentiate between social interactions that happen in households and workplaces. In addition, weak interactions among crowds, transient interactions within social gatherings, and dense human contact between foreign workers in dormitories are also taken into consideration. Such a categorization in terms of a multiplex of social network connections together with the Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model have enabled a more precise study of the feasibility and efficacy of control measures such as social distancing, work from home, and lockdown, at different moments and stages of the pandemics. Using this model, we study an epidemic outbreak that occurs within densely populated residential areas in Singapore. Our simulations show that residents in densely populated areas could be infected easily, even though they constitute a very small fraction of the whole population. Once infection begins in these areas, disease spreading is uncontrollable if appropriate control measures are not implemented.

Chung N N, Chew L Y

2021-May-12

Radiology Radiology

COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis.

In BMC research notes

OBJECTIVES : The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand.

DATA DESCRIPTION : In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 × 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.

Shakouri Shokouh, Bakhshali Mohammad Amin, Layegh Parvaneh, Kiani Behzad, Masoumi Farid, Ataei Nakhaei Saeedeh, Mostafavi Sayyed Mostafa

2021-May-12

Artificial intelligence, COVID-19, Chest CT image, Clinical imaging, Computed tomography, Coronavirus, Deep learning, Diagnosis, Lung infection, Radiology

General General

Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet.

In Biomedical physics & engineering express

Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection lesions and around normal tissues, and blurred boundaries of infections. Moreover, a shortage of available CT dataset hinders deep learning techniques applying to tackling COVID-19. To address these issues, we propose a deep learning-based approach known as PPM-Unet to segmenting COVID-19 lesions from CT images. Our method improves an Unet by adopting pyramid pooling modules instead of the conventional skip connection and then enhances the representation of the neural network by aiding the global attention mechanism. We first pre-train PPM-Unet on COVID-19 dataset of pseudo labels containing1600 samples producing a coarse model. Then we fine-tune the coarse PPM-Unet on the standard COVID-19 dataset consisting of 100 pairs of samples to achieve a fine PPM-Unet. Qualitative and quantitative results demonstrate that our method can accurately segment COVID-19 infection regions from CT images, and achieve higher performance than other state-of-the-art segmentation models in this study. It offers a promising tool to lay a foundation for quantitatively detecting COVID-19 lesions.

Ma Yinjin, Feng Peng, He Peng, Ren Yong, Guo Xiaodong, Wei Biao

2021-May-12

COVID-19, computed tomography, deep learning, image segmentation, medical image

General General

The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.

In Informatics in medicine unlocked

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90-0.91), specificity was 0.91 (95% CI, 0.90-0.92) and the AUC was 0.96 (95% CI, 0.91-0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.88 (95% CI, 0.87-0.88) and the AUC was 0.96 (95% CI, 0.93-0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.95 (95% CI, 0.94-0.95) and the AUC was 0.97 (95% CI, 0.96-0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.

Moezzi Meisam, Shirbandi Kiarash, Shahvandi Hassan Kiani, Arjmand Babak, Rahim Fakher

2021

Artificial intelligence, COVID-19, CT-Scan, Computed tomography, Coronavirus infections, Deep learning, Machine learning, Respiratory tract infections

Radiology Radiology

Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study.

In PeerJ

This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (N = 1203) vs. those directly admitted to ICU (N = 104), and (ii) patients not upgraded to ICU (N = 979) vs. those upgraded to the ICU (N = 224) from the general floor. A NN algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer and lymphocyte count showed temporal divergence between COVID-19 patients hospitalized in the general floor that were upgraded to ICU compared to those that were not. The NN predictive model essentially ranked the same laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782 ± 0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.822 ± 0.018). This work could help frontline physicians to anticipate downstream ICU need to more effectively allocate healthcare resources.

Lu Joyce Q, Musheyev Benjamin, Peng Qi, Duong Tim Q

2021

Coronavirus, Machine learning, Pneumonia, Predictive model, SARS-CoV-2

General General

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly.

In Multimedia systems

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Singh Hari, Bawa Seema

2021-May-06

Accuracy, COVID-19, Linear regression, Machine learning, Polynomial regression, t-Test

General General

COVID-19 Lesion Discrimination and Localization Network Based on Multi-Receptive Field Attention Module on CT Images.

In Optik

Since discovered in Hubei, China in December 2019, Corona Virus Disease 2019 named COVID-19 has lasted mor than one year, and the number of new confirmed cases and confirmed deaths is still at a high level. COVID-19 is an infectious disease caused by SARS-CoV-2. Although RT-PCR is considered the gold standard for detection of COVID-19, CT plays an important role in the diagnosis and evaluation of the therapeutic effect of COVID-19. Diagnosis and localization of COVID-19 on CT images using deep learning can provide quantitative auxiliary information for doctors. This article proposes a novel network with multi-receptive field attention module to diagnose COVID-19 on CT images. This attention module includes three parts, a pyramid convolution module (PCM), a multi-receptive field spatial attention block (SAB), and a multi-receptive field channel attention block (CAB). The PCM can improve the diagnostic ability of the network for lesions of different sizes and shapes. The role of SAB and CAB is to focus the features extracted from the network on the lesion area to improve the ability of COVID-19 discrimination and localization. We verify the effectiveness of the proposed method on two datasets. The accuracy rate of 97.12%, specificity of 96.89%, and sensitivity of 97.21% are achieved by the proposed network on DTDB dataset provided by the Beijing Ditan Hospital Capital Medical University. Compared with other state-of-the-art attention modules, the proposed method achieves better result. As for the public COVID-19 SARS-CoV-2 dataset, 95.16% for accuracy, 95.6% for F1-score and 99.01% for AUC are obtained. The proposed network can effectively assist doctors in the diagnosis of COVID-19 CT images.

Ma Xia, Zheng Bingbing, Zhu Yu, Yu Fuli, Zhang Rixin, Chen Budong

2021-May-07

COVID-19, attention, auxiliary diagnosis, deep learning

General General

SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes.

In Nature communications ; h5-index 260.0

Despite its clinical importance, the SARS-CoV-2 gene set remains unresolved, hindering dissection of COVID-19 biology. We use comparative genomics to provide a high-confidence protein-coding gene set, characterize evolutionary constraint, and prioritize functional mutations. We select 44 Sarbecovirus genomes at ideally-suited evolutionary distances, and quantify protein-coding evolutionary signatures and overlapping constraint. We find strong protein-coding signatures for ORFs 3a, 6, 7a, 7b, 8, 9b, and a novel alternate-frame gene, ORF3c, whereas ORFs 2b, 3d/3d-2, 3b, 9c, and 10 lack protein-coding signatures or convincing experimental evidence of protein-coding function. Furthermore, we show no other conserved protein-coding genes remain to be discovered. Mutation analysis suggests ORF8 contributes to within-individual fitness but not person-to-person transmission. Cross-strain and within-strain evolutionary pressures agree, except for fewer-than-expected within-strain mutations in nsp3 and S1, and more-than-expected in nucleocapsid, which shows a cluster of mutations in a predicted B-cell epitope, suggesting immune-avoidance selection. Evolutionary histories of residues disrupted by spike-protein substitutions D614G, N501Y, E484K, and K417N/T provide clues about their biology, and we catalog likely-functional co-inherited mutations. Previously reported RNA-modification sites show no enrichment for conservation. Here we report a high-confidence gene set and evolutionary-history annotations providing valuable resources and insights on SARS-CoV-2 biology, mutations, and evolution.

Jungreis Irwin, Sealfon Rachel, Kellis Manolis

2021-May-11

Ophthalmology Ophthalmology

Indian contribution toward biomedical research and development in COVID-19: A systematic review.

In Indian journal of pharmacology

COVID-19 pandemic led to an unprecedented collaborative effort among industry, academia, regulatory bodies, and governments with huge financial investments. Scientists and researchers from India also left no stone unturned to find therapeutic and preventive measures against COVID-19. Indian pharmaceutical companies are one of the leading manufacturers of vaccine in the world, are utilizing its capacity to its maximum, and are one among the forerunners in vaccine research against COVID-19 across the globe. In this systematic review, the information regarding contribution of Indian scientists toward COVID-19 research has been gathered from various news articles across Google platform apart from searching PubMed, WHO site, COVID-19 vaccine tracker, CTRI, clinicaltrials.gov, and websites of pharmaceutical companies. The article summarizes and highlights the various therapeutic and vaccine candidates, diagnostic kits, treatment agents, and technology being developed and tested by Indian researcher community against COVID-19.

Kaur Hardeep, Kaur Manpreet, Bhattacharyya Anusuya, Prajapat Manisha, Thota Prasad, Sarma Phulen, Kumar Subodh, Kaur Gurjeet, Sharma Saurabh, Prakash Ajay, Saifuddin P K, Medhi Bikash

AYUSH, Artificial intelligence, COVID-19, India, SARS-CoV2, clinical trials, drug designing, vaccine

Cardiology Cardiology

Prospective Case-Control Study of Cardiovascular Abnormalities 6 Months Following Mild COVID-19 in Healthcare Workers.

In JACC. Cardiovascular imaging

OBJECTIVES : The purpose of this study was to detect cardiovascular changes after mild severe acute respiratory syndrome coronavirus 2 infection.

BACKGROUND : Concern exists that mild coronavirus disease 2019 may cause myocardial and vascular disease.

METHODS : Participants were recruited from COVIDsortium, a 3-hospital prospective study of 731 health care workers who underwent first-wave weekly symptom, polymerase chain reaction, and serology assessment over 4 months, with seroconversion in 21.5% (n = 157). At 6 months post-infection, 74 seropositive and 75 age-, sex-, and ethnicity-matched seronegative control subjects were recruited for cardiovascular phenotyping (comprehensive phantom-calibrated cardiovascular magnetic resonance and blood biomarkers). Analysis was blinded, using objective artificial intelligence analytics where available.

RESULTS : A total of 149 subjects (mean age 37 years, range 18 to 63 years, 58% women) were recruited. Seropositive infections had been mild with case definition, noncase definition, and asymptomatic disease in 45 (61%), 18 (24%), and 11 (15%), respectively, with 1 person hospitalized (for 2 days). Between seropositive and seronegative groups, there were no differences in cardiac structure (left ventricular volumes, mass, atrial area), function (ejection fraction, global longitudinal shortening, aortic distensibility), tissue characterization (T1, T2, extracellular volume fraction mapping, late gadolinium enhancement) or biomarkers (troponin, N-terminal pro-B-type natriuretic peptide). With abnormal defined by the 75 seronegatives (2 SDs from mean, e.g., ejection fraction <54%, septal T1 >1,072 ms, septal T2 >52.4 ms), individuals had abnormalities including reduced ejection fraction (n = 2, minimum 50%), T1 elevation (n = 6), T2 elevation (n = 9), late gadolinium enhancement (n = 13, median 1%, max 5% of myocardium), biomarker elevation (borderline troponin elevation in 4; all N-terminal pro-B-type natriuretic peptide normal). These were distributed equally between seropositive and seronegative individuals.

CONCLUSIONS : Cardiovascular abnormalities are no more common in seropositive versus seronegative otherwise healthy, workforce representative individuals 6 months post-mild severe acute respiratory syndrome coronavirus 2 infection.

Joy George, Artico Jessica, Kurdi Hibba, Seraphim Andreas, Lau Clement, Thornton George D, Oliveira Marta Fontes, Adam Robert Daniel, Aziminia Nikoo, Menacho Katia, Chacko Liza, Brown James T, Patel Rishi K, Shiwani Hunain, Bhuva Anish, Augusto Joao B, Andiapen Mervyn, McKnight Aine, Noursadeghi Mahdad, Pierce Iain, Evain Timothée, Captur Gabriella, Davies Rhodri H, Greenwood John P, Fontana Marianna, Kellman Peter, Schelbert Erik B, Treibel Thomas A, Manisty Charlotte, Moon James C

2021-May-05

COVID-19, SARS-CoV-2, cardiovascular magnetic resonance, late gadolinium enhancement, myocardial edema, myocarditis, troponin

Surgery Surgery

Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom.

In Intensive care medicine ; h5-index 86.0

PURPOSE : The trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration of the dynamic course of the disease in the context of applied therapeutic interventions.

METHODS : We included adult patients undergoing invasive mechanical ventilation (IMV) within 48 h of intensive care unit (ICU) admission with complete clinical data until ICU death or discharge. We examined the importance of factors associated with disease progression over the first week, implementation and responsiveness to interventions used in acute respiratory distress syndrome (ARDS), and ICU outcome. We used machine learning (ML) and Explainable Artificial Intelligence (XAI) methods to characterise the evolution of clinical parameters and our ICU data visualisation tool is available as a web-based widget ( https://www.CovidUK.ICU ).

RESULTS : Data for 633 adults with COVID-19 who underwent IMV between 01 March 2020 and 31 August 2020 were analysed. Overall mortality was 43.3% and highest with non-resolution of hypoxaemia [60.4% vs17.6%; P < 0.001; median PaO2/FiO2 on the day of death was 12.3(8.9-18.4) kPa] and non-response to proning (69.5% vs.31.1%; P < 0.001). Two ML models using weeklong data demonstrated an increased predictive accuracy for mortality compared to admission data (74.5% and 76.3% vs 60%, respectively). XAI models highlighted the increasing importance, over the first week, of PaO2/FiO2 in predicting mortality. Prone positioning improved oxygenation only in 45% of patients. A higher peak pressure (OR 1.42[1.06-1.91]; P < 0.05), raised respiratory component (OR 1.71[ 1.17-2.5]; P < 0.01) and cardiovascular component (OR 1.36 [1.04-1.75]; P < 0.05) of the sequential organ failure assessment (SOFA) score and raised lactate (OR 1.33 [0.99-1.79]; P = 0.057) immediately prior to application of prone positioning were associated with lack of oxygenation response. Prone positioning was not applied to 76% of patients with moderate hypoxemia and 45% of those with severe hypoxemia and patients who died without receiving proning interventions had more missed opportunities for prone intervention [7 (3-15.5) versus 2 (0-6); P < 0.001]. Despite the severity of gas exchange deficit, most patients received lung-protective ventilation with tidal volumes less than 8 mL/kg and plateau pressures less than 30cmH2O. This was despite systematic errors in measurement of height and derived ideal body weight.

CONCLUSIONS : Refractory hypoxaemia remains a major association with mortality, yet evidence based ARDS interventions, in particular prone positioning, were not implemented and had delayed application with an associated reduced responsiveness. Real-time service evaluation techniques offer opportunities to assess the delivery of care and improve protocolised implementation of evidence-based ARDS interventions, which might be associated with improvements in survival.

Patel Brijesh V, Haar Shlomi, Handslip Rhodri, Auepanwiriyakul Chaiyawan, Lee Teresa Mei-Ling, Patel Sunil, Harston J Alex, Hosking-Jervis Feargus, Kelly Donna, Sanderson Barnaby, Borgatta Barbara, Tatham Kate, Welters Ingeborg, Camporota Luigi, Gordon Anthony C, Komorowski Matthieu, Antcliffe David, Prowle John R, Puthucheary Zudin, Faisal Aldo A

2021-May-11

ARDS, Artificial intelligence, COVID-19, Mechanical ventilation, Mortality risk, Prone position

Surgery Surgery

Answering the Challenge of COVID-19 Pandemic Through Innovation and Ingenuity.

In Advances in experimental medicine and biology

The novel coronavirus disease 2019 (COVID-19) pandemic has created a maelstrom of challenges affecting virtually every aspect of global healthcare system. Critical hospital capacity issues, depleted ventilator and personal protective equipment stockpiles, severely strained supply chains, profound economic slowdown, and the tremendous human cost all culminated in what is questionably one of the most profound challenges that humanity faced in decades, if not centuries. Effective global response to the current pandemic will require innovation and ingenuity. This chapter discusses various creative approaches and ideas that arose in response to COVID-19, as well as some of the most impactful future trends that emerged as a result. Among the many topics discussed herein are telemedicine, blockchain technology, artificial intelligence, stereolithography, and distance learning.

Kelley Kathryn Clare, Kamler Jonathan, Garg Manish, Stawicki Stanislaw P

2021

Artificial intelligence, Blockchain technology, COVID-19, Healthcare innovation, Pandemic, SARS-CoV-2, Technology, Telemedicine

Cardiology Cardiology

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA for decision support for a Crisis Standards of Care team.

MATERIALS AND METHODS : We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.

RESULTS : The prospective cohort included 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) required intensive care unit care, 1,480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.

DISCUSSION : Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.

CONCLUSION : We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

Sottile Peter D, Albers David, DeWitt Peter E, Russell Seth, Stroh J N, Kao David P, Adrian Bonnie, Levine Matthew E, Mooney Ryan, Larchick Lenny, Kutner Jean S, Wynia Matthew K, Glasheen Jeffrey J, Bennett Tellen D

2021-May-10

COVID-19, Crisis Triage, Machine Learning, Mortality Prediction

oncology Oncology

Contact Tracing in Healthcare Settings During the COVID-19 Pandemic Using Bluetooth Low Energy and Artificial Intelligence-A Viewpoint.

In Frontiers in artificial intelligence

The COVID-19 pandemic has inflicted great damage with effects that will likely linger for a long time. This crisis has highlighted the importance of contact tracing in healthcare settings because hospitalized patients are among the high risk for complications and death. Moreover, effective contact tracing schemes are not yet available in healthcare settings. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. One potential solution that addresses all of these elements leverages an indoor real-time location system based on Bluetooth Low Energy and artificial intelligence.

Tang Guanglin, Westover Kenneth, Jiang Steve

2021

COVID-19, artificial intelligence, bluetooth, contact tracing, deep learning, healthcare, real-time location system

General General

Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries.

In Information systems frontiers : a journal of research and innovation

This research examines the considerations of responsible Artificial Intelligence in the deployment of AI-based COVID-19 digital proximity tracking and tracing applications in two countries; the State of Qatar and the United Kingdom. Based on the alignment level analysis with the Good AI Society's framework and sentiment analysis of official tweets, the diagnostic analysis resulted in contrastive findings for the two applications. While the application EHTERAZ (Arabic for precaution) in Qatar has fallen short in adhering to the responsible AI requirements, it has contributed significantly to controlling the pandemic. On the other hand, the UK's NHS COVID-19 application has exhibited limited success in fighting the virus despite relatively abiding by these requirements. This underlines the need for obtaining a practical and contextual view for a comprehensive discourse on responsible AI in healthcare. Thereby offering necessary guidance for striking a balance between responsible AI requirements and managing pressures towards fighting the pandemic.

El-Haddadeh Ramzi, Fadlalla Adam, Hindi Nitham M

2021-May-06

COVID-19 pandemic, Ethics, Responsible Artificial Intelligence, Sentiment analysis

Public Health Public Health

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

In Knowledge-based systems

COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.

Satu Md Shahriare, Khan Md Imran, Mahmud Mufti, Uddin Shahadat, Summers Matthew A, Quinn Julian M W, Moni Mohammad Ali

2021-May-06

COVID-19, Classification, Machine learning, TClustVID, Topics modelling, Twitter data

General General

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

In Scientific reports ; h5-index 158.0

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

Zargari Khuzani Abolfazl, Heidari Morteza, Shariati S Ali

2021-May-10

Public Health Public Health

Carceral-community epidemiology, structural racism, and COVID-19 disparities.

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

Black and Hispanic communities are disproportionately affected by both incarceration and COVID-19. The epidemiological relationship between carceral facilities and community health during the COVID-19 pandemic, however, remains largely unexamined. Using data from Cook County Jail, we examine temporal patterns in the relationship between jail cycling (i.e., arrest and processing of individuals through jails before release) and community cases of COVID-19 in Chicago ZIP codes. We use multivariate regression analyses and a machine-learning tool, elastic regression, with 1,706 demographic control variables. We find that for each arrested individual cycled through Cook County Jail in March 2020, five additional cases of COVID-19 in their ZIP code of residence are independently attributable to the jail as of August. A total 86% of this additional disease burden is borne by majority-Black and/or -Hispanic ZIPs, accounting for 17% of cumulative COVID-19 cases in these ZIPs, 6% in majority-White ZIPs, and 13% across all ZIPs. Jail cycling in March alone can independently account for 21% of racial COVID-19 disparities in Chicago as of August 2020. Relative to all demographic variables in our analysis, jail cycling is the strongest predictor of COVID-19 rates, considerably exceeding poverty, race, and population density, for example. Arrest and incarceration policies appear to be increasing COVID-19 incidence in communities. Our data suggest that jails function as infectious disease multipliers and epidemiological pumps that are especially affecting marginalized communities. Given disproportionate policing and incarceration of racialized residents nationally, the criminal punishment system may explain a large proportion of racial COVID-19 disparities noted across the United States.

Reinhart Eric, Chen Daniel L

2021-May-25

carceral-community epidemiology, inequality, mass incarceration, public health, racial disparities

Ophthalmology Ophthalmology

Variations in volume of emergency surgeries and emergency department access at a third level hospital in Milan, Lombardy, during the COVID-19 outbreak.

In BMC emergency medicine

BACKGROUND : During the recent outbreak of COVID-19 (coronavirus disease 2019), Lombardy was the most affected region in Italy, with 87,000 patients and 15,876 deaths up to May 26, 2020. Since February 22, 2020, well before the Government declared a state of emergency, there was a huge reduction in the number of emergency surgeries performed at hospitals in Lombardy. A general decrease in attendance at emergency departments (EDs) was also observed. The aim of our study is to report the experience of the ED of a third-level hospital in downtown Milan, Lombardy, and provide possible explanations for the observed phenomena.

METHODS : This retrospective, observational study assessed the volume of emergency surgeries and attendance at an ED during the course of the pandemic, i.e. immediately before, during and after a progressive community lockdown in response to the COVID-19 pandemic. These data were compared with data from the same time periods in 2019. The results are presented as means, standard error (SE), and 95% studentized confidence intervals (CI). The Wilcoxon rank signed test at a 0.05 significance level was used to assess differences in per-day ED access distributions.

RESULTS : Compared to 2019, a significant overall drop in emergency surgeries (60%, p < 0.002) and in ED admittance (66%, p ≅ 0) was observed in 2020. In particular, there were significant decreases in medical (40%), surgical (74%), specialist (ophthalmology, otolaryngology, traumatology, and urology) (92%), and psychiatric (60%) cases. ED admittance due to domestic violence (59%) and individuals who left the ED without being seen (76%) also decreased. Conversely, the number of deaths increased by 196%.

CONCLUSIONS : During the COVID-19 outbreak the volume of urgent surgeries and patients accessing our ED dropped. Currently, it is not known if mortality of people who did not seek care increased during the pandemic. Further studies are needed to understand if such reductions during the COVID-19 pandemic will result in a rebound of patients left untreated or in unwanted consequences for population health.

Castoldi Laura, Solbiati Monica, Costantino Giorgio, Casiraghi Elena

2021-May-10

COVID-19, Coronavirus, Emergency department attendance, Emergency department overcrowding, Emergency surgery

General General

Retro Drug Design: From Target Properties to Molecular Structures

bioRxiv Preprint

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

Wang, Y.; Michael, S.; Huang, R.; Zhao, J.; Recabo, K.; Bougie, D.; Shu, Q.; Shinn, P.; Sun, H.

2021-05-12

General General

Trade-offs between short-term mortality attributable to NO2 and O3 changes during the COVID-19 lockdown across major Spanish cities.

In Environmental pollution (Barking, Essex : 1987)

The emergence of the COVID-19 pandemic forced most countries to put in place lockdown measures to slow down the transmission of the virus. These lockdowns have led to temporal improvements in air quality. Here, we evaluate the changes in NO2 and O3 levels along with the associated impact upon premature mortality during the COVID-19 lockdown and deconfinement periods along the first epidemic wave across the provincial capital cities of Spain. We first quantify the change in pollutants solely due to the lockdown as the difference between business-as-usual (BAU) pollution levels, estimated with a machine learning-based meteorological normalization technique, and observed concentrations. Second, instead of using exposure-response functions between the pollutants and mortality reported in the literature, we fit conditional quasi-Poisson regression models to estimate city-specific associations between daily pollutant levels and non-accidental mortality during the period 2010-2018. Significant relative risk values are observed at lag 1 for NO2 (1.0047 [95% CI: 1.0014 to 1.0081]) and at lag 0 for O3 (1.0039 [1.0013 to 1.0065]). On average NO2 changed by -51% (intercity range -65.7 to -30.9%) and -36.4% (-53.7 to -11.6%), and O3 by -1.1% (-20.2 to 23.8%) and 0.6% (-12.4 to 23.0%), during the lockdown (57 days) and deconfinement (42 days) periods, respectively. We obtain a reduction in attributable mortality associated with NO2 changes of -119 (95% CI: -273 to -24) deaths over the lockdown, and of -53 (-114 to -10) deaths over the deconfinement. This was partially compensated by an increase in the attributable number of deaths, 14 (-72 to 99) during the lockdown, and 8 (-27 to 50) during the deconfinement, associated with the rise in O3 levels in the most populous cities during the analysed period, despite the overall small average reductions. Our study shows that the potential trade-offs between multiple air pollutants should be taken into account when evaluating the health impacts of environmental exposures.

Achebak Hicham, Petetin Hervé, Quijal-Zamorano Marcos, Bowdalo Dene, Pérez García-Pando Carlos, Ballester Joan

2021-May-04

COVID-19 lockdown, Mortality, NO(2), O(3), Spain

General General

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi.

In PloS one ; h5-index 176.0

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

Hosny Khalid M, Darwish Mohamed M, Li Kenli, Salah Ahmad

2021

Radiology Radiology

Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature.

In World journal of radiology

The current gold standard for the diagnosis of coronavirus disease-19 (COVID-19) is a positive reverse transcriptase polymerase chain reaction (RT-PCR) test, on the background of clinical suspicion. However, RT-PCR has its limitations; this includes issues of low sensitivity, sampling errors and appropriate timing of specimen collection. As pulmonary involvement is the most common manifestation of severe COVID-19, early and appropriate lung imaging is important to aid diagnosis. However, gross discrepancies can occur between the clinical and imaging findings in patients with COVID-19, which can mislead clinicians in their decision making. Although chest X-ray (CXR) has a low sensitivity for the diagnosis of COVID-19 associated lung disease, especially in the earlier stages, a positive CXR increases the pre-test probability of COVID-19. CXR scoring systems have shown to be useful, such as the COVID-19 opacification rating score which helps to predict the need of tracheal intubation. Furthermore, artificial intelligence-based algorithms have also shown promise in differentiating COVID-19 pneumonia on CXR from other lung diseases. Although costlier than CXR, unenhanced computed tomographic (CT) chest scans have a higher sensitivity, but lesser specificity compared to RT-PCR for the diagnosis of COVID-19 pneumonia. A semi-quantitative CT scoring system has been shown to predict short-term mortality. The routine use of CT pulmonary angiography as a first-line imaging modality in patients with suspected COVID-19 is not justifiable due to the risk of contrast nephropathy. Scoring systems similar to those pioneered in CXR and CT can be used to effectively plan and manage hospital resources such as ventilators. Lung ultrasound is useful in the assessment of critically ill COVID-19 patients in the hands of an experienced operator. Moreover, it is a convenient tool to monitor disease progression, as it is cheap, non-invasive, easily accessible and easy to sterilise. Newer lung imaging modalities such as magnetic resonance imaging (MRI) for safe imaging among children, adolescents and pregnant women are rapidly evolving. Imaging modalities are also essential for evaluating the extra-pulmonary manifestations of COVID-19: these include cranial imaging with CT or MRI; cardiac imaging with ultrasonography (US), CT and MRI; and abdominal imaging with US or CT. This review critically analyses the utility of each imaging modality to empower clinicians to use them appropriately in the management of patients with COVID-19 infection.

Kumar Hemant, Fernandez Cornelius James, Kolpattil Sangeetha, Munavvar Mohamed, Pappachan Joseph M

2021-Apr-28

COVID-19, Chest X-ray, Computed tomography, Lung imaging, Lung ultrasound, Pneumonia

General General

Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.

In Disease markers

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.

Perumal Varalakshmi, Narayanan Vasumathi, Rajasekar Sakthi Jaya Sundar

2021

Public Health Public Health

Automatic prediction of COVID- 19 from chest images using modified ResNet50.

In Multimedia tools and applications

Recently coronavirus 2019 (COVID-2019), discovered in Wuhan city of China in December 2019 announced as world pandemic by the World Health Organization (WHO). It has catastrophic impacts on daily lives, public health, and the global economy. The detection of coronavirus (COVID- 19) is now a critical task for medical specialists. Laboratory methods for detecting the virus such as Polymerase Chain Reaction, antigens, and antibodies have pros and cons represented in time required to obtain results, accuracy, cost and suitability of the test to phase of infection. The need for accurate, fast, and cheap auxiliary diagnostic tools has become a necessity as there are no accurate automated toolkits available. Other medical investigations such as chest X-ray and Computerized Tomography scans are imaging techniques that play an important role in the diagnosis of COVID- 19 virus. Application of advanced artificial intelligence techniques for processing radiological imaging can be helpful for the accurate detection of this virus. However, Due to the small dataset available for COVID- 19, transfer learning from pre-trained convolution neural networks, CNNs can be used as a promising solution for diagnosis of coronavirus. Transfer learning becomes an effective mechanism by transferring knowledge from generic object recognition tasks to domain-specific tasks. Hence, the main contribution of this paper is to exploit the pre-trained deep learning CNN architectures as a cornerstone to enhance and build up an automated tool for detection and diagnosis of COVID- 19 in chest X-Ray and Computerized Tomography images. The main idea is to make use of their convolutional neural network structure and its learned weights on large datasets such as ImageNet. Moreover, a modification to ResNet50 is proposed to classify the patients as COVID infected or not. This modification includes adding three new layers, named, 'Conv', 'Batch_Normaliz' and 'Activation_Relu' layers. These layers are injected in the ResNet50 architecture for accurate discrimination and robust feature extraction. Extensive experiments are carried out to assess the performance of the proposed model on COVID- 19 chest X-Ray and Computerized Tomography scan images. Experimental results approve that the proposed modification, injected layers, increases the diagnosis accuracy to 97.7% for Computerized Tomography dataset and 97.1% for X-Ray dataset which is superior compared to other approaches.

Elpeltagy Marwa, Sallam Hany

2021-May-04

COVID-19, ResNet50, Transfer learning

General General

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review.

In Archives of computational methods in engineering : state of the art reviews

The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

Bhatt Aditya Narayan, Shrivastava Nitin

2021-May-03

Radiology Radiology

An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray.

In Expert systems with applications

The objective of the research article is to propose and validate a combination of machine learning and radiomics features to detect COVID-19 early and rapidly from chest X-ray (CXR) in presence of other viral/bacterial pneumonia and at different severity levels of diseases. It is vital to assess the performance of any diagnosis method on an independent data set and at very early stage of the disease when the disease severity of is very low. In such cases, most of the diagnosis methods fail. A total of 378 CXR images containing both normal lung and pneumonia (both COVID-19 and others lung conditions) were collected from publically available data set. 71 radiomics features for each lung segment were chosen from 100 extracted features based on Z-score heatmap and one way ANOVA test that can detect COVID-19. Three best performing classical machine learning algorithms during the training phase - 1) fine Gaussian support vector machine (SVM), 2) fine k-nearest neighbor (KNN) and 3) ensemble bagged model (EBM) trees were chosen for further evaluation on an independent test data set. The independent test data set consists of 115 COVID-19 CXR images collected from a local hospital and 100 CXR images collected from publically available data set containing normal lung and viral/bacterial pneumonia. Severity was scored between 0 to 4 by two experienced radiologists for each lung with pneumonia (both COVID-19 and non COVID-19) for the test data set. Ensemble Bagging Model Trees (EBM) with the selected radiomics features is the most suitable to distinguish between COVID-19 and other lung infections with an overall sensitivity of 87.8% and specificity of 97% (95.2% accuracy and 0.9228 area under curve) and is robust across severity levels. The method also can detect COVID-19 from CXR when two experienced radiologists were unable to detect any abnormality in the lung CXR (represented by severity score of 0). Once the CXR is acquired and lung is segmented, it takes less than two minutes for extracting radiomics features and providing diagnosis result. Since the proposed method does not require any manual intervention (e.g., sample collection etc.), it can be straightway integrated with standard X-ray reporting system to be used as an efficient, cost-effective and rapid early diagnosis device.

Tamal Mahbubunnabi, Alshammari Maha, Alabdullah Meernah, Hourani Rana, Alola Hossain Abu, Hegazi Tarek M

2021-Oct-15

COVID-19, Chest X-ray, Early diagnosis, Machine learning, Radiomics

General General

Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images.

In Expert systems with applications

X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.

Alhudhaif Adi, Polat Kemal, Karaman Onur

2021-Oct-15

Chest X-ray images, Convolutional Neural Network (CNN), Corona Virus (COVID-19), Deep learning

General General

Automated detection of COVID-19 from CT scan using convolutional neural network.

In Biocybernetics and biomedical engineering

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

Mishra Narendra Kumar, Singh Pushpendra, Joshi Shiv Dutt

2021-Apr-30

COVID-19, CT scan images, Convolutional Neural Network, ResNet50, Transfer learning, VGG16

Public Health Public Health

Health information technology and digital innovation for national learning health and care systems.

In The Lancet. Digital health

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Sheikh Aziz, Anderson Michael, Albala Sarah, Casadei Barbara, Franklin Bryony Dean, Richards Mike, Taylor David, Tibble Holly, Mossialos Elias

2021-May-06

Internal Medicine Internal Medicine

A novel approach utilizing laser acupuncture teletherapy for management of elderly-onset rheumatoid arthritis: A randomized clinical trial.

In Journal of telemedicine and telecare ; h5-index 28.0

INTRODUCTION : Rheumatoid arthritis (RA) disease is a systemic progressive inflammatory autoimmune disorder. Elderly-onset RA can be assumed as a benign form of RA. Until recently, face-to-face therapeutic sessions between health professionals and patients are usually the method of its treatment. However, during pandemics, including coronavirus disease 2019 (COVID-19), teletherapeutic sessions can extensively increase the patient safety especially in elderly patients who are more vulnerable to these infections. Thus, the aim of this study was to evaluate a novel teletherapy approach for management of elderly patients suffering from RA by utilizing laser acupuncture.

METHODS : A teletherapy system was used for management of elderly patients suffering from RA. Sixty participants were allocated randomly into two groups and the ratio was 1:1. Patients in the first group were treated with laser acupuncture and telerehabilitation sessions, which consisted of aerobic exercise and virtual reality training. Patients in the second group received telerehabilitation sessions, which consisted of aerobic exercise and virtual reality training. Evaluation of patients was done by using the Health Assessment questionnaire (HAQ), the Rheumatoid Arthritis Quality of Life (RAQoL) questionnaire, and the analysis of interleukin-6 (IL-6), serum C-reactive protein (CRP), plasma adenosine triphosphate (ATP) concentration and plasma malondialdehyde (MDA).

RESULTS : A statistically significant difference was found in CRP, RAQoL, IL-6 and MDA between the pre- and post-treatments in the first group (p < 0.05) favouring the post-treatment group, while the HAQ showed a statistically significant difference between pre- and post-treatments (p < 0.05) in both groups. Statistically significant post-treatment differences were also observed between the two groups (p < 0.05) in RAQoL, CRP, ATP and MDA, favouring the first group.

DISCUSSION : Laser acupuncture teletherapy could be suggested as a reliable treatment method for elderly patients suffering from RA, as it can provide a safe and effective therapeutic approach. Teletherapy provided safer access to health professionals and patients while giving a high patient satisfaction value with a relatively lower cost (ClinicalTrials.gov Identifier: NCT04684693).

Adly Aya Sedky, Adly Afnan Sedky, Adly Mahmoud Sedky, Ali Mohammad F

2021-May-09

Teletherapy, elderly-onset rheumatoid arthritis, gerontechnology, laser acupuncture, randomized clinical trial, telerehabilitation

General General

Intelligent interactive technologies for mental health and well-being

ArXiv Preprint

Mental healthcare has seen numerous benefits from interactive technologies and artificial intelligence. Various interventions have successfully used intelligent technologies to automate the assessment and evaluation of psychological treatments and mental well-being and functioning. These technologies include different types of robots, video games, and conversational agents. The paper critically analyzes existing solutions with the outlooks for their future. In particular, we: i)give an overview of the technology for mental health, ii) critically analyze the technology against the proposed criteria, and iii) provide the design outlooks for these technologies.

Mladjan Jovanovic, Aleksandar Jevremovic, Milica Pejovic-Milovancevic

2021-05-11

General General

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method.

In Computers in biology and medicine

Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.

Jia Guangyu, Lam Hak-Keung, Xu Yujia

2021-Apr-29

COVID-19 detection, Chest X-Ray and CT images, Deep learning, Modified CNN

Public Health Public Health

The barriers and enablers to downloading the COVIDSafe app - a topic modelling analysis.

In Australian and New Zealand journal of public health

OBJECTIVE : We report a survey in regional Queensland to understand the reasons for suboptimal uptake of the COVIDSafe app.

METHODS : A short five-minute electronic survey disseminated to healthcare professionals, mining groups and school communities in the Central Queensland region. Free text responses and their topics were modelled using natural language processing and a latent Dirichlet model.

RESULTS : We received a total of 723 responses; of these, 69% had downloaded the app and 31% had not. The respondents' reasons for not downloading the app were grouped under four topics: lack of perceived risk of COVID-19/lack of perceived need and privacy issues; phone-related issues; tracking and misuse of data; and trust, security and credibility. Among the 472 people who downloaded the app and provided text amenable to text mining, the two topics most commonly listed were: to assist with contact tracing; and to return to normal.

CONCLUSIONS : This survey of a regional population found that lack of perceived need, concerns around privacy and technical difficulties were the major barriers to users downloading the application. Implications for public health: Health promotion campaigns aimed at increasing the uptake of the COVIDSafe app should focus on promoting how the app will assist with contact tracing to help return to 'normal'. Additionally, health promotors should address the app's impacts on privacy, people's lack of perceived need for the app and technical barriers.

Smoll Nicolas R, Walker Jacina, Khandaker Gulam

2021-May-10

COVIDSafe, contact tracing, machine learning, natural language processing, survey

Radiology Radiology

On the Role of Artificial Intelligence in Medical Imaging of COVID-19.

In Patterns (New York, N.Y.)

Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, both in the focus on imaging modalities (AI experts neglected CT and Ultrasound, favoring X-Ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found deficient regarding potential use in clinical practice, but 2.7% (N=12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.

Born Jannis, Beymer David, Rajan Deepta, Coy Adam, Mukherjee Vandana V, Manica Matteo, Prasanna Prasanth, Ballah Deddeh, Guindy Michal, Shaham Dorith, Shah Pallav L, Karteris Emmanouil, Robertus Jan L, Gabrani Maria, Rosen-Zvi Michal

2021-Apr-30

ACR, (American College of Radiology), AI, (Artificial Intelligence), Artificial Intelligence, COVID-19, CT, (Computed Tomography), CXR, (Chest Radiographs), Chest CT, Chest Ultrasound, Chest X-ray, Coronavirus, DL, (Deep Learning), Deep Learning, Digital Healthcare, LUS, (Lung Ultrasound), Lung imaging, MI, (Medical Imaging), Machine Learning, Medical Imaging, Meta Review, PRISMA, PRISMA, (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), RT-PCR, (Reverse Transcriptase Polymerase Chain reaction), SARS-CoV-2, US, (Ultrasound)

Radiology Radiology

Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study.

In Frontiers in public health

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.

Yan Chengxi, Chang Ying, Yu Huan, Xu Jingxu, Huang Chencui, Yang Minglei, Wang Yiqiao, Wang Di, Yu Tian, Wei Shuqin, Li Zhenyu, Gong Feifei, Kou Mingqing, Gou Wenjing, Zhao Qili, Sun Penghui, Jia Xiuqin, Fan Zhaoyang, Xu Jiali, Li Sijie, Yang Qi

2021

COVID-19, computed tomography, deep learning, intensive care unit, pneumonia

Public Health Public Health

Alignment free sequence comparison methods and reservoir host prediction.

In Bioinformatics (Oxford, England)

MOTIVATION : The emergence and subsequent pandemic of the SARS-CoV-2 virus raised urgent questions about its origin and, particularly, its reservoir host. These types of questions are long-standing problems in the management of emerging infectious diseases and are linked to virus discovery programs and the prediction of viruses that are likely to become zoonotic. Conventional means to identify reservoir hosts have relied on surveillance, experimental studies and phylogenetics. More recently, machine learning approaches have been applied to generate tools to swiftly predict reservoir hosts from sequence data.

RESULTS : Here, we extend a recent work that combined sequence alignment and a mixture of alignment-free approaches using a gradient boosting machines (GBMs) machine learning model, which integrates genomic traits (GT) and phylogenetic neighbourhood (PN) signatures to predict reservoir hosts. We add a more uniform approach by applying Machine Learning with Digital Signal Processing (MLDSP)-based structural patterns (M-SP). The extended model was applied to an existing virus/reservoir host dataset and to the SARS-CoV-2 and related viruses and generated an improvement in prediction accuracy.

AVAILABILITY AND IMPLEMENTATION : The source code used in this work is freely available at https://github.com/bill1167/hostgbms.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Lee Bill, Smith David K, Guan Yi

2021-May-08

Public Health Public Health

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.

In Microscopy research and technique

Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand-crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U-Net deep learning model. The hand-crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand-crafted features (ii) classification using fusion of hand-crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand-crafted & deep microscopic feature's fusion provide better results compared to only hand-crafted fused features.

Amin Javaria, Anjum Muhammad Almas, Sharif Muhammad, Saba Tanzila, Tariq Usman

2021-May-08

U-Net, ensemble methods, entropy, fusion, hand crafted features, healthcare, public health

General General

ModFOLD8: accurate global and local quality estimates for 3D protein models.

In Nucleic acids research ; h5-index 217.0

Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which has maintained its position as a leading server for the prediction of global and local quality of 3D protein models, over the past decade (>20 000 unique external users). ModFOLD8 is the latest version of the server, which combines the strengths of multiple pure-single and quasi-single model methods. Improvements have been made to the web server interface and there has been successive increases in prediction accuracy, which were achieved through integration of newly developed scoring methods and advanced deep learning-based residue contact predictions. Each version of the ModFOLD server has been independently blind tested in the biennial CASP experiments, as well as being continuously evaluated via the CAMEO project. In CASP13 and CASP14, the ModFOLD7 and ModFOLD8 variants ranked among the top 10 quality estimation methods according to almost every official analysis. Prior to CASP14, ModFOLD8 was also applied for the evaluation of SARS-CoV-2 protein models as part of CASP Commons 2020 initiative. The ModFOLD8 server is freely available at: https://www.reading.ac.uk/bioinf/ModFOLD/.

McGuffin Liam J, Aldowsari Fahd M F, Alharbi Shuaa M A, Adiyaman Recep

2021-May-08

General General

COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.

In Database : the journal of biological databases and curation

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.

Nagy Ádám, Ligeti Balázs, Szebeni János, Pongor Sándor, Gyrffy Balázs

2021-May-08

General General

Development and validation of a machine learning model to predict mortality risk in patients with COVID-19.

In BMJ health & care informatics

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in patients during the COVID-19 pandemic as healthcare providers incurred an exponential increase in workload,which created a strain on the staff and limited resources. Further, methods to better understand and characterise the predictors of morbidity and mortality was needed. METHODS: We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3395 hospital admissions with COVID-19. Multiple methods were applied, and final model was selected based on performance. A variable importance algorithm was used for interpretability, and understanding of performance and predictors was applied to the best model. We built a model with an area under the receiver operating characteristic curve of 83-97 to identify predictors and patients with high risk of mortality due to COVID-19. Oximetry, respirations, blood urea nitrogen, lymphocyte per cent, calcium, troponin and neutrophil percentage were important features, and key ranges were identified that contributed to a 50% increase in patients' mortality prediction score. With an increasing negative predictive value starting 0.90 after the second day of admission suggests we might be able to more confidently identify likely survivors DISCUSSION: This study serves as a use case of a machine learning methods with visualisations to aide clinicians with a better understanding of the model and predictors of mortality. CONCLUSION: As we continue to understand COVID-19, computer assisted algorithms might be able to improve the care of patients.

Stachel Anna, Daniel Kwesi, Ding Dan, Francois Fritz, Phillips Michael, Lighter Jennifer

2021-May

information science, medical informatics, patient care

Surgery Surgery

Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning.

In Surgical innovation

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

Mohamed Ali Abdel-Moneim, El-Alali Emran, Weltz Adam S, Rehrig Scott T

2021-May-07

biomedical engineering, radiologist, surgical education

Internal Medicine Internal Medicine

Prediction of Disease Progression of COVID-19 Based upon Machine Learning.

In International journal of general medicine

Background : Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression.

Methods : In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models.

Results : A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k-nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector-machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed.

Conclusion : The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

Xu Fumin, Chen Xiao, Yin Xinru, Qiu Qiu, Xiao Jingjing, Qiao Liang, He Mi, Tang Liang, Li Xiawei, Zhang Qiao, Lv Yanling, Xiao Shili, Zhao Rong, Guo Yan, Chen Mingsheng, Chen Dongfeng, Wen Liangzhi, Wang Bin, Nian Yongjian, Liu Kaijun

2021

COVID-19, disease progression, machine-learning models

General General

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic.

In Visual computing for industry, biomedicine, and art

A neural network is one of the current trends in deep learning, which is increasingly gaining attention owing to its contribution in transforming the different facets of human life. It also paves a way to approach the current crisis caused by the coronavirus disease (COVID-19) from all scientific directions. Convolutional neural network (CNN), a type of neural network, is extensively applied in the medical field, and is particularly useful in the current COVID-19 pandemic. In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients. The CNN models discussed in this review were mainly developed for the detection, classification, and segmentation of COVID-19 images. The base models used for detection and classification were AlexNet, Visual Geometry Group Network with 16 layers, residual network, DensNet, GoogLeNet, MobileNet, Inception, and extreme Inception. U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19. To further validate these observations, we conducted an experimental study using a simple CNN framework for the binary classification of COVID-19 CT images. We achieved an accuracy of 93% with an F1-score of 0.93. Thus, with the availability of improved medical image datasets, it is evident that CNNs are very useful for the efficient diagnosis and prognosis of COVID-19.

Kugunavar Sneha, Prabhakar C J

2021-May-05

COVID-19, Convolutional neural network, Deep learning, Medical image analysis, Neural network

General General

Personalized analytics and wearable biosensor platform for early detection of COVID-19 decompensation (DeCODe: Detection of COVID-19 Decompensation): protocol for development of COVID-19 Decompensation Index.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS Co-V2/ COVID-19, improve care delivery and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support the work of researchers to make discoveries not possible through small, limited data sets.

OBJECTIVE : To this end, we seek to develop a COVID-19 digital biomarker that could provide early detection of a patient's physiologic worsening or decompensation. We propose developing and validating a COVID-19 Decompensation Index (CDI) in a two-phased project that builds off existing wearable biosensor-derived analytics generated by physIQ's end-to-end cloud platform for continuous monitoring of physiology with wearable biosensors. This effort will achieve two primary objectives: 1) collect adequate data to enable the development of the CDI; and 2) collect rich deidentified clinical data correlative with outcomes and symptomology related to COVID-19 disease progression. Secondary objectives include evaluation of feasibility and usability of pinpointIQ™, the digital platform through which data is gathered, analyzed, and displayed.

METHODS : This study is a prospective, non-randomized, open-label, two-phase design. Phase I will involve data collection for the NIH digital data hub as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study.

RESULTS : Our target COVID-19 Decompensation Index (CDI) will be a binary classifier trained to distinguish between participants decompensating and not decompensating. The primary performance metric for CDI will be ROC AUC with a minimum performance criterion of AUC ≥ 0.75 (significance α = 0.05 and power 1 - β = 0.80). Determination of sex/gender, race or ethnic characteristics that impact differences in the CDI performance, as well as lead time-time to predict decompensation and the relationship to ultimate severity of disease based on the World Health Organization COVID-19 Ordinal Scale will be explored.

CONCLUSIONS : Using machine learning techniques on a large data set of COVID-19 positive patients could produce valuable insights into the physiology of COVID-19 as well as a digital biomarker for COVID-19 decompensation. We plan, with this study, to develop a tool that can uniquely reflect the physiologic data of a diverse population and contribute to a trove of high-quality data that will help researchers better understand COVID-19.

CLINICALTRIAL : Trial Registration: ClinicalTrials.gov NCT NCT04575532.

Larimer Karen, Wegerich Stephan, Splan Joel, Chestek David, Prendergast Heather, Vanden Hoek Terry

2021-May-04

Surgery Surgery

"P3": an adaptive modeling tool for post-COVID-19 restart of surgical services.

In JAMIA open

Objective : To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic.

Materials and Methods : Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs.

Results : The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios.

Conclusions : Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.

Joshi Divya, Jalali Ali, Whipple Todd, Rehman Mohamed, Ahumada Luis M

2021-Apr

COVID-19, decision support, optimization, predictive analytics, surgical backlog

General General

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays.

In Neural computing & applications

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification.

Altaf Fouzia, Islam Syed M S, Janjua Naeem Khalid

2021-Apr-29

COVID-19, Chest radiography, Computer-aided diagnosis, Deep learning, Dictionary learning, Thoracic disease classification, Transfer learning

Cardiology Cardiology

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.

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

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Chee Marcel Lucas, Ong Marcus Eng Hock, Siddiqui Fahad Javaid, Zhang Zhongheng, Lim Shir Lynn, Ho Andrew Fu Wah, Liu Nan

2021-Apr-29

COVID-19, artificial intelligence, critical care, emergency department, intensive care, machine learning

General General

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets.

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

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients' feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

Shah Adnan Muhammad, Naqvi Rizwan Ali, Jeong Ok-Ran

2021-Apr-29

COVID-19, discrete emotions, online reviews, sentiment analysis, text mining, topic modeling

General General

In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

ArXiv Preprint

Since the outbreak of COVID-19 policy makers have been relying upon non-pharmacological interventions to control the outbreak. With air pollution as a potential transmission vector there is need to include it in intervention strategies. We propose a U-net driven quantile regression model to predict $PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on ground-truth data and predict reasonable $PM_{2.5}$ values with their spatial distribution, even for locations where pollution data is unavailable. Such predictions of $PM_{2.5}$ characteristics could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.

Jacquelyn Shelton, Przemyslaw Polewski, Wei Yao

2021-05-06

Radiology Radiology

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.

In Healthcare (Basel, Switzerland)

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

Almalki Yassir Edrees, Qayyum Abdul, Irfan Muhammad, Haider Noman, Glowacz Adam, Alshehri Fahad Mohammed, Alduraibi Sharifa K, Alshamrani Khalaf, Alkhalik Basha Mohammad Abd, Alduraibi Alaa, Saeed M K, Rahman Saifur

2021-Apr-29

chest X-ray images, data analytics, feature extraction, healthcare, image processing, pandemic

Radiology Radiology

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria

ArXiv Preprint

The automatic assignment of a severity score to the CT scans of patients affected by COVID-19 pneumonia could reduce the workload in radiology departments. This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net_1) is devoted to the identification of the lung parenchyma, the second one (U-net_2) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice index. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. Both Dice and accuracy showed a dependency on the quality of annotations of the available data samples. On an independent and publicly available benchmark dataset, the Dice values measured between the masks predicted by LungQuant system and the reference ones were 0.95$\pm$0.01 and 0.66$\pm$0.13 for the segmentation of lungs and COVID-19 lesions, respectively. The accuracy of 90% in the identification of the CT-SS on this benchmark dataset was achieved. We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of the Dice index, the U-net segmentation quality strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent validation sets, demonstrating the satisfactory generalization ability of the LungQuant.

Francesca Lizzi, Abramo Agosti, Francesca Brero, Raffaella Fiamma Cabini, Maria Evelina Fantacci, Silvia Figini, Alessandro Lascialfari, Francesco Laruina, Piernicola Oliva, Stefano Piffer, Ian Postuma, Lisa Rinaldi, Cinzia Talamonti, Alessandra Retico

2021-05-06

General General

Correction: A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

In JMIR medical informatics ; h5-index 23.0

[This corrects the article DOI: 10.2196/23238.].

He Qian, Du Fei, Simonse Lianne W L

2021-May-04

General General

News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston.

In Expert systems with applications

Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level is needed as opposed to the state or country level. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the number of tests normalizes the latter. Positivity gives a better picture of the spread of this pandemic as, with time, more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD (Susceptible, Exposed, Infectious, Recovered, and Deceased) lacks information about the sentiment of people concerning coronavirus. Thirdly, models that make use of social media posts might have too much noise and misinformation. On the other hand, news sentiment can capture long-term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new artificial intelligence (i.e., AI) model, viz., Sentiment Informed Time-series Analyzing AI (SITALA), trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76, and that for the test dataset of 22 consecutive days is 9.6. A cone of uncertainty is provided within which future COVID-19 test positivity has been shown to fall with high accuracy. The model predictions fare better than a published Bayesian-based SEIRD model. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment (e.g., death count for COVID-19 will grow at an alarming rate in Houston if mask orders are not followed) will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.

Desai Prathamesh S

2021-Apr-29

Artificial Intelligence, COVID-19 Model, Deep Learning, News Sentiment, Pandemic Forecast, Public Policy

Radiology Radiology

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear.

METHODS : We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support).

RESULTS : Our algorithm classified patients into these classes with an area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables - including basic patient information, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type - suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors.

CONCLUSIONS : Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.

Navlakha Saket, Morjaria Sejal, Perez-Johnston Rocio, Zhang Allen, Taur Ying

2021-May-04

COVID-19, Cancer, Clinical machine learning, Infectious diseases, Predictive modeling

General General

The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function.

In BMC bioinformatics

BACKGROUND : Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition.

RESULTS : We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19.

CONCLUSIONS : The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer .

Krämer Andreas, Billaud Jean-Noël, Tugendreich Stuart, Shiffman Dan, Jones Martin, Green Jeff

2021-May-03

COVID-19, Drug repurposing, Knowledge graph, Network biology

Public Health Public Health

Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).

In The Science of the total environment

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.

Tiwari Anuj, Dadhania Arya V, Ragunathrao Vijay Avin Balaji, Oliveira Edson R A

2021-Jun-15

COVID-19, Disproportionate COVID-19, Machine learning, Racial minority, Vulnerability modeling

General General

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19.

In Frontiers in robotics and AI

The COVID-19 pandemic has highly impacted the communities globally by reprioritizing the means through which various societal sectors operate. Among these sectors, healthcare providers and medical workers have been impacted prominently due to the massive increase in demand for medical services under unprecedented circumstances. Hence, any tool that can help the compliance with social guidelines for COVID-19 spread prevention will have a positive impact on managing and controlling the virus outbreak and reducing the excessive burden on the healthcare system. This perspective article disseminates the perspectives of the authors regarding the use of novel biosensors and intelligent algorithms embodied in wearable IoMT frameworks for tackling this issue. We discuss how with the use of smart IoMT wearables certain biomarkers can be tracked for detection of COVID-19 in exposed individuals. We enumerate several machine learning algorithms which can be used to process a wide range of collected biomarkers for detecting (a) multiple symptoms of SARS-CoV-2 infection and (b) the dynamical likelihood of contracting the virus through interpersonal interaction. Eventually, we enunciate how a systematic use of smart wearable IoMT devices in various social sectors can intelligently help controlling the spread of COVID-19 in communities as they enter the reopening phase. We explain how this framework can benefit individuals and their medical correspondents by introducing Systems for Symptom Decoding (SSD), and how the use of this technology can be generalized on a societal level for the control of spread by introducing Systems for Spread Tracing (SST).

Mehrdad Sarmad, Wang Yao, Atashzar S Farokh

2021

AI for health, COVID-19, IoMT, smart connected health, smart wearables, spread control, symptom tracking, telemedicine

General General

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.

In Journal of healthcare engineering

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.

Li Xiaoshuo, Tan Wenjun, Liu Pan, Zhou Qinghua, Yang Jinzhu

2021

Public Health Public Health

Leveraging Informatics and Technology to Support Public Health Response: Framework and Illustrations using COVID-19.

In Online journal of public health informatics

Objective : To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic.

Method : The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response.

Results : Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction.

Discussion : Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders.

Conclusion : The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.

Snowdon Jane L, Kassler William, Karunakaram Hema, Dixon Brian E, Rhee Kyu

2021

artificial intelligence, clinical informatics, coronavirus, information technology, pandemics, public health informatics

General General

A COVID-19 time series forecasting model based on MLP ANN.

In Procedia computer science

With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model's predictions available online, collaborating with the fight against the pandemic.

Borghi Pedro Henrique, Zakordonets Oleksandr, Teixeira João Paulo

2021

COVID-19 Brazil forecast, COVID-19 Italy forecast, COVID-19 worldwide forecast

General General

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.

In Information systems frontiers : a journal of research and innovation

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Piccialli Francesco, di Cola Vincenzo Schiano, Giampaolo Fabio, Cuomo Salvatore

2021-Apr-26

Artificial intelligence, COVID-19, Deep learning, Healthcare, Machine learning, Review, SARS-CoV-2, Survey

General General

A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.

In Information systems frontiers : a journal of research and innovation

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.

Singh Prabh Deep, Kaur Rajbir, Singh Kiran Deep, Dhiman Gaurav

2021-Apr-25

Artificial intelligence, COVID-19, Corona virus, Ensemble classifier, Machine learning, Quality of service

General General

Optimization of Thermal and Structural Design in Lithium-Ion Batteries to Obtain Energy Efficient Battery Thermal Management System (BTMS): A Critical Review.

In Archives of computational methods in engineering : state of the art reviews

Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.

Fayaz H, Afzal Asif, Samee A D Mohammed, Soudagar Manzoore Elahi M, Akram Naveed, Mujtaba M A, Jilte R D, Islam Md Tariqul, Ağbulut Ümit, Saleel C Ahamed

2021-Apr-26

General General

Predicting compassion fatigue among psychological hotline counselors using machine learning techniques.

In Current psychology (New Brunswick, N.J.)

** : During the outbreak of coronavirus disease 2019, psychological hotline counselors frequently address help-seekers' traumatic experiences from time to time, which possibly causes counselors' compassion fatigue. The present study aimed to explore the predictors of compassion fatigue among a high-risk population of psychological hotline counselors. Seven hundred and twelve psychological hotline counselors were recruited from the Mental Health Service Platform at Central China Normal University, Ministry of Education, then were asked to complete the questionnaires measuring compassion fatigue, trait empathy, social support, trait mindfulness, counselor's self-efficacy, humor, life meaning, and post-traumatic growth. A chi-square test was utilized to filter for the top-20 predictive variables. Machine learning techniques, including logistic regression, decision tree, random forest, k-nearest neighbor, support vector machine, and naïve Bayes were employed to predict compassion fatigue. The results showed that the most important predictors of compassion fatigue were meaning in life, counselors' self-efficacy, mindfulness, and empathy. Except for the decision tree, the rest machine learning techniques obtained good performance. Naïve Bayes presented the highest area under the receiver operating characteristic curve of 0.803. Random forest achieved the least classification error of 23.64, followed by Naïve Bayes with a classification error of 23.85. These findings support the potential application of machine learning techniques in the prediction of compassion fatigue.

Supplementary Information : The online version contains supplementary material available at 10.1007/s12144-021-01776-7.

Zhang Lin, Zhang Tao, Ren Zhihong, Jiang Guangrong

2021-Apr-26

COVID-19, Compassion fatigue, Hotline psychological counselor, Machine learning

General General

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.

In Multimedia systems

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

Gaur Loveleen, Bhatia Ujwal, Jhanjhi N Z, Muhammad Ghulam, Masud Mehedi

2021-Apr-28

COVID-19, Chest X-rays, Computer vision, Deep CNN, Deep learning, Transfer learning

General General

Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna's first COVID-19 lockdown: Significance for air quality management.

In Environmental pollution (Barking, Essex : 1987)

BACKGROUND : Lockdowns amid the COVID-19 pandemic have offered a real-world opportunity to better understand air quality responses to previously unseen anthropogenic emission reductions.

METHODS AND MAIN OBJECTIVE : This work examines the impact of Vienna's first lockdown on ground-level concentrations of nitrogen dioxide (NO2), ozone (O3) and total oxidant (Ox). The analysis runs over January to September 2020 and considers business as usual scenarios created with machine learning models to provide a baseline for robustly diagnosing lockdown-related air quality changes. Models were also developed to normalise the air pollutant time series, enabling facilitated intervention assessment.

CORE FINDINGS : NO2 concentrations were on average -20.1% [13.7-30.4%] lower during the lockdown. However, this benefit was offset by amplified O3 pollution of +8.5% [3.7-11.0%] in the same period. The consistency in the direction of change indicates that the NO2 reductions and O3 increases were ubiquitous over Vienna. Ox concentrations increased slightly by +4.3% [1.8-6.4%], suggesting that a significant part of the drops in NO2 was compensated by gains in O3. Accordingly, 82% of lockdown days with lowered NO2 were accompanied by 81% of days with amplified O3. The recovery shapes of the pollutant concentrations were depicted and discussed. The business as usual-related outcomes were broadly consistent with the patterns outlined by the normalised time series. These findings allowed to argue further that the detected changes in air quality were of anthropogenic and not of meteorological reason. Pollutant changes on the machine learning baseline revealed that the impact of the lockdown on urban air quality were lower than the raw measurements show. Besides, measured traffic drops in major Austrian roads were more significant for light-duty than for heavy-duty vehicles. It was also noted that the use of mobility reports based on cell phone movement as activity data can overestimate the reduction of emissions for the road transport sector, particularly for heavy-duty vehicles. As heavy-duty vehicles can make up a large fraction of the fleet emissions of nitrogen oxides, the change in the volume of these vehicles on the roads may be the main driver to explain the change in NO2 concentrations.

INTERPRETATION AND IMPLICATIONS : A probable future with emissions of volatile organic compounds (VOCs) dropping slower than emissions of nitrogen oxides could risk worsened urban O3 pollution under a VOC-limited photochemical regime. More holistic policies will be needed to achieve improved air quality levels across different regions and criteria pollutants.

Brancher Marlon

2021-Apr-15

Air quality data, Atmospheric composition, COVID-19 lockdown, Machine learning, Meteorology

Public Health Public Health

Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The emergence of SARS-CoV-2 in late 2019 and its subsequent global spread continues to be a global health crisis. Many governments see contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-Cov-2.

OBJECTIVE : We here report on an analysis of the suitability of Artificial Intelligence (AI)-enabled social media analysis of Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.

METHODS : We extracted over 10,000 relevant social media posts and analysed these, over an eight month period, from 1st of March to 31st of October 2020. We used an initial filter with COVID-19 related keywords, which were pre-defined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app keywords and a geographical filter. A hybrid rule-based ensemble model was developed and utilised for the study, combining state-of-the-art lexicon rule-based and Deep Learning-based approaches.

RESULTS : Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments being in the North of England. These sentiments varied over time, likely being influenced by ongoing public debates around implementing app-based contact tracing using a centralised model where data would be shared with the health service, versus de-centralised contact-tracing technology.

CONCLUSIONS : Variations in sentiments corroborate with ongoing debates surrounding the information governance of health related information. AI-enabled social media analysis of public attitudes in healthcare can help to facilitate the implementation of effective public health campaigns.

Cresswell Kathrin, Tahir Ahsen, Sheikh Zakariya, Hussain Zain, Domínguez Hernández Andrés, Harrison Ewen, Williams Robin, Sheikh Aziz, Hussain Amir

2021-Apr-17

Public Health Public Health

Experimental and natural evidence of SARS-CoV-2 infection-induced activation of type I interferon responses.

In iScience

Type I interferons (IFNs) are our first line of defence against virus infection. Recent studies have suggested the ability of SARS-CoV-2 proteins to inhibit IFN responses. Emerging data also suggest that timing and extent of IFN production is associated with manifestation of COVID-19 severity. In spite of progress in understanding how SARS-CoV-2 activates antiviral responses, mechanistic studies into wildtype SARS-CoV-2-mediated induction and inhibition of human type I IFN responses are scarce. Here we demonstrate that SARS-CoV-2 infection induces a type I IFN response in vitro and in moderate cases of COVID-19. In vitro stimulation of type I IFN expression and signaling in human airway epithelial cells is associated with activation of canonical transcriptions factors, and SARS-CoV-2 is unable to inhibit exogenous induction of these responses. Furthermore, we show that physiological levels of IFNα detected in patients with moderate COVID-19 is sufficient to suppress SARS-CoV-2 replication in human airway cells.

Banerjee Arinjay, El-Sayes Nader, Budylowski Patrick, Jacob Rajesh Abraham, Richard Daniel, Maan Hassaan, Aguiar Jennifer A, Demian Wael L, Baid Kaushal, D’Agostino Michael R, Ang Jann Catherine, Murdza Tetyana, Tremblay Benjamin J-M, Afkhami Sam, Karimzadeh Mehran, Irving Aaron T, Yip Lily, Ostrowski Mario, Hirota Jeremy A, Kozak Robert, Capellini Terence D, Miller Matthew S, Wang Bo, Mubareka Samira, McGeer Allison J, McArthur Andrew G, Doxey Andrew C, Mossman Karen

2021-Apr-26

Radiology Radiology

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

In Medical image analysis

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.

Goncharov Mikhail, Pisov Maxim, Shevtsov Alexey, Shirokikh Boris, Kurmukov Anvar, Blokhin Ivan, Chernina Valeria, Solovev Alexander, Gombolevskiy Victor, Morozov Sergey, Belyaev Mikhail

2021-Apr-01

COVID-19, Chest computed tomography, Convolutional neural network, Triage

Radiology Radiology

A comprehensive review of imaging findings in COVID-19 - status in early 2021.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.

Afshar-Oromieh Ali, Prosch Helmut, Schaefer-Prokop Cornelia, Bohn Karl Peter, Alberts Ian, Mingels Clemens, Thurnher Majda, Cumming Paul, Shi Kuangyu, Peters Alan, Geleff Silvana, Lan Xiaoli, Wang Feng, Huber Adrian, Gräni Christoph, Heverhagen Johannes T, Rominger Axel, Fontanellaz Matthias, Schöder Heiko, Christe Andreas, Mougiakakou Stavroula, Ebner Lukas

2021-May-01

COVID-19, Corona virus, Imaging, SARS-CoV-2

General General

Immunoinformatics approach for a novel multi-epitope vaccine construct against spike protein of human coronaviruses

bioRxiv Preprint

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of S protein sequences of all the HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate against S protein of human coronaviruses. To the best of our knowledge, it is one of the first study, where multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs and linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted, validated and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response.

kumar, A.; Rathi, E.; Kini, S. G.

2021-05-02

General General

Comparative study of machine learning methods for COVID-19 transmission forecasting.

In Journal of biomedical informatics ; h5-index 55.0

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.

Dairi Abdelkader, Harrou Fouzi, Zeroual Abdelhafid, Hittawe Mohamad Mazen, Sun Ying

2021-Apr-26

COVID-19, GAN-GRU., Hybrid deep learning, LSTM-CNN, short-term forecasting

General General

[Research progress in lung parenchyma segmentation based on computed tomography].

In Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.

Xiao Hanguang, Ran Zhiqiang, Huang Jinfeng, Ren Huijiao, Liu Chang, Zhang Banglin, Zhang Bolong, Dang Jun

2021-Apr-25

computed tomography, deep learning, lung parenchyma segmentation

General General

Deep CNN models for predicting COVID-19 in CT and x-ray images.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model's ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.

Chaddad Ahmad, Hassan Lama, Desrosiers Christian

2021-Jan

Coronavirus disease 2019, convolutional neural network, radiomics, transfer learning

General General

Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence

bioRxiv Preprint

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the hosts for SARS-CoV-2 based on comparative studies using its genomic sequences. This paper investigates the SARS-CoV-2 origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined SARS-CoV-2 genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidence strongly supporting scientific hypotheses that bats and pangolins are probable hosts for SARS-CoV-2. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

Nguyen, T. T.; Abdelrazek, M.; Nguyen, D. T.; Aryal, S.; Nguyen, D. T.; Reddy, S.; Nguyen, Q. V. H.; Khatami, A.; Hsu, E. B.; Yang, S.

2021-04-30

General General

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development.

In Pharmacology

INTRODUCTION : The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks.

METHODS : In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time.

RESULTS : Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention.

DISCUSSION : The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.

Haas Quentin, Alvarez David Vicente, Borissov Nikolay, Ferdowsi Sohrab, von Meyenn Leonhard, Trelle Sven, Teodoro Douglas, Amini Poorya

2021-Apr-28

Artificial intelligence, COVID-19, Risklick, Search platform

General General

Introducing frequency representation into convolution neural networks for medical image segmentation via twin-Kernel Fourier convolution.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : For medical image segmentation, deep learning-based methods have achieved state-of-the-art performance. However, the powerful spectral representation in the field of image processing is rarely considered in these models.

METHODS : In this work, we propose to introduce frequency representation into convolution neural networks (CNNs) and design a novel model, tKFC-Net, to combine powerful feature representation in both frequency and spatial domains. Through the Fast Fourier Transform (FFT) operation, frequency representation is employed on pooling, upsampling, and convolution without any adjustments to the network architecture. Furthermore, we replace original convolution with twin-Kernel Fourier Convolution (t-KFC), a new designed convolution layer, to specify the convolution kernels for particular functions and extract features from different frequency components.

RESULTS : We experimentally show that our method has an edge over other models in the task of medical image segmentation. Evaluated on four datasets-skin lesion segmentation (ISIC 2018), retinal blood vessel segmentation (DRIVE), lung segmentation (COVID-19-CT-Seg), and brain tumor segmentation (BraTS 2019), the proposed model achieves outstanding results: the metric F1-Score is 0.878 for ISIC 2018, 0.8185 for DRIVE, 0.9830 for COVID-19-CT-Seg, and 0.8457 for BraTS 2019.

CONCLUSION : The introduction of spectral representation retains spectral features which result in more accurate segmentation. The proposed method is orthogonal to other topology improvement methods and very convenient to be combined.

Tang Xianlun, Peng Jiangping, Zhong Bing, Li Jie, Yan Zhenfu

2021-Apr-14

Convolution neural networks, Frequency representation, Medical image segmentation, U-Net

General General

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

In medRxiv : the preprint server for health sciences

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R . For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

Zhang-James Yanli, Hess Jonathan, Salekin Asif, Wang Dongliang, Chen Samuel, Winkelstein Peter, Morley Christopher P, Faraone Stephen V

2021-Apr-20

General General

Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification.

In SN computer science

In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naïve Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets.

Ramya B N, Shetty Shyleshwari M, Amaresh A M, Rakshitha R

2021

COVID-19, Sentiment analysis, Tweet

General General

Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives.

In International journal of biological sciences

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.

Huang Shigao, Yang Jie, Fong Simon, Zhao Qi

2021

Artificial intelligence, COVID-19, deep learning, diagnosis, machine learning

General General

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials.

In Scientific reports ; h5-index 158.0

The year 2020 witnessed a heavy death toll due to COVID-19, calling for a global emergency. The continuous ongoing research and clinical trials paved the way for vaccines. But, the vaccine efficacy in the long run is still questionable due to the mutating coronavirus, which makes drug re-positioning a reasonable alternative. COVID-19 has hence fast-paced drug re-positioning for the treatment of COVID-19 and its symptoms. This work builds computational models using matrix completion techniques to predict drug-virus association for drug re-positioning. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel Tenofovir prodrug developed in order to improve renal safety when compared to its original counterpart (older version) Tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the drugs to be used against the virus would vary as SARS-Cov-2 mutates over time by predicting the drugs for the mutated strains, suggesting the importance of such a tool in drug prediction. We believe this work would open up possibilities for applying machine learning models to clinical research for drug-virus association prediction and other similar biological problems.

Mongia Aanchal, Saha Sanjay Kr, Chouzenoux Emilie, Majumdar Angshul

2021-Apr-27

General General

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients with Underlying Health Conditions: A Multicenter Study.

In IEEE journal of biomedical and health informatics

OBJECTIVE : Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed.

METHODS : Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis.

RESULTS : The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001).

CONCLUSION : The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

Wang Siwen, Dong Di, Li Liang, Li Hailin, Bai Yan, Hu Yahua, Huang Yuanyi, Yu Xiangrong, Liu Sibin, Qiu Xiaoming, Lu Ligong, Wang Meiyun, Zha Yunfei, Tian Jie

2021-Apr-27

General General

Targeting SARS-CoV-2 Spike Protein/ACE2 Protein-Protein Interactions: a Computational Study.

In Molecular informatics

The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.

Pirolli Davide, Righino Benedetta, De Rosa Maria Cristina

2021-Apr-27

COVID-19, PPI focused library, QSAR, Virtual screening, docking

Public Health Public Health

FaceGuard: A Wearable System To Avoid Face Touching.

In Frontiers in robotics and AI

Most people touch their faces unconsciously, for instance to scratch an itch or to rest one's chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one's face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching.

Michelin Allan Michael, Korres Georgios, Ba’ara Sara, Assadi Hadi, Alsuradi Haneen, Sayegh Rony R, Argyros Antonis, Eid Mohamad

2021

IMU-based hand tracking, face touching avoidance, sensory feedback, vibrotactile stimulation, wearable technologies for health care

General General

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

In Frontiers in public health

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.

Pan Wen-Tsao, Huang Qiu-Yu, Yang Zi-Yin, Zhu Fei-Yan, Pang Yu-Ning, Zhuang Mei-Er

2021

COVID-19 era, backpropagation neural network, deep learning, quantum genetic algorithm, quantum particle swarm optimization algorithm, quantum step fruit fly optimization algorithm

Public Health Public Health

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting.

In Frontiers in public health

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post-the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

da Silva Cecilia Cordeiro, de Lima Clarisse Lins, da Silva Ana Clara Gomes, Silva Eduardo Luiz, Marques Gabriel Souza, de Araújo Lucas Job Brito, Albuquerque Júnior Luiz Antônio, de Souza Samuel Barbosa Jatobá, de Santana Maíra Araújo, Gomes Juliana Carneiro, Barbosa Valter Augusto de Freitas, Musah Anwar, Kostkova Patty, Dos Santos Wellington Pinheiro, da Silva Filho Abel Guilhermino

2021

COVID-19, Covid-19 pandemics forecasting, SARS-CoV-2, digital epidemiology, spatio-temporal analysis, spatio-temporal forecasting

General General

Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks.

In Results in physics

Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.

Nabi Khondoker Nazmoon, Tahmid Md Toki, Rafi Abdur, Kader Muhammad Ehsanul, Haider Md Asif

2021-May

Convolutional neural network (CNN), Deep learning, Long short term memory (LSTM), Time series analysis

oncology Oncology

Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach.

In The EPMA journal

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the "participatory" medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.

Tachalov V V, Orekhova L Y, Kudryavtseva T V, Loboda E S, Pachkoriia M G, Berezkina I V, Golubnitschaja O

2021-Apr-19

Age, Aggravation, Bacterial load, Bacterial superinfections, Big data, Bio-banking, COVID-19, Collateral pathologies, Comorbidities, Compliance, Dental diseases, Disease severity, Dry mouth syndrome, Elderly, Gut-lung axis, Health policy, Healthcare, Hygiene, Individualised patient profiling, Inflammation, Influenza, Lower respiratory tract, Machine learning, Microbiome, Morbidity, Motivation, Oral cavity, Pathomechanism, Patient stratification, Periodontitis, Periodontopathic microflora, Predictive preventive personalised medicine (PPPM / 3PM), Probiotics, Psychological aspects, Risk factors, SARS-CoV-2, Socio-economic status, Tailored care, Treatment algorithm, Viral infection

Internal Medicine Internal Medicine

Ensemble-based Bag of Features for Automated Classification of Normal and COVID-19 CXR Images.

In Biomedical signal processing and control

The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.

Ashour Amira S, Eissa Merihan M, Wahba Maram A, Elsawy Radwa A, Elgnainy Hamada Fathy, Tolba Mohamed Saeed, Mohamed Waleed S

2021-Apr-20

Bag of features, COVID-19, K-means, chest X-ray images, classification, ensemble classifiers, invariant feature transform, speeded up robust features detector

General General

COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning.

In Stochastic environmental research and risk assessment : research journal

** : A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.

Supplementary Information : The online version contains supplementary material available at 10.1007/s00477-021-02021-0.

Torres-Signes Antoni, Frías María P, Ruiz-Medina María D

2021-Apr-19

COVID-19 analysis, Curve regression, Hard-data, Machine learning, Multivariate time series, Soft-data

General General

A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets.

In Information systems frontiers : a journal of research and innovation

With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

Kaur Harleen, Ahsaan Shafqat Ul, Alankar Bhavya, Chang Victor

2021-Apr-20

COVID-19, Heterogeneous Euclidean overlap metric (H-EOM), Hybrid heterogeneous support vector machine (H-SVM), Recurrent neural network (RCN), Sentiment analysis, Twitter

General General

Sufmacs: a machine learning-based robust image segmentation framework for covid-19 radiological image interpretation.

In Expert systems with applications

The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.

Chakraborty Shouvik, Mali Kalyani

2021-Apr-20

COVID-19, SUFMACS, clustering, image segmentation, machine learning, radiological image interpretation

Surgery Surgery

A COVID-19 pandemic AI-based system with deep learning forecasting and automatic statistical data acquisition: Development and Implementation Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : More than 79.2 million confirmed cases and 1.7 million deaths were caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was named coronavirus disease (COVID-19) by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of COVID-19 together with each country's policy measures.

OBJECTIVE : We aimed to develop an online AI system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heatmap visualization of policy measures in 171 countries.

METHODS : COVID-19 pandemic AI system (CPAIS) integrated two datasets: the datasets of Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government that is maintained by Oxford University; and the datasets of the COVID-19 Data Repository that is established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting, including autoregressive Integrated Moving Average (ARIMA); Feedforward Neural Network (FNN); Multilayer Perceptron Neural Networks (MLPs); and Long Short-term Memory (LSTM). With regard to one-year records (i.e., whole time series data), records of the last 14 days served as the validation set to evaluate the performance of forecast, whereas earlier records served as the training set.

RESULTS : A total of 171 countries that featured in both the databases were included in the online system (https://covid19.mldoctor.com.tw). CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July and another peak of 6,368,591 in December 2020. The dynamic heatmap with policy measures depicts changes in COVID-19 measures for each country. Nineteen measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting, and the performance of ARIMA, FNN, and MLP were not stable because their forecast accuracy was only better than LSTM for few countries. LSTM demonstrated the best forecast accuracy for Canada, as the RMSE, MAE, and MAPE were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA and FNN demonstrated better performance for South Korea (RMSE = 317.53169 and 181.29894, MAPE = 0.4641688 and 0.2708482, respectively).

CONCLUSIONS : CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

CLINICALTRIAL :

Yu Cheng-Sheng, Chang Shy-Shin, Chang Tzu-Hao, Wu Jenny L, Lin Yu-Jiun, Chien Hsiung-Fei, Chen Ray-Jade

2021-Apr-23

General General

Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic.

In The American journal of psychiatry

OBJECTIVE : Increased anxiety in response to the COVID-19 pandemic has been widely noted. The purpose of this study was to test whether the prepandemic functional connectome predicted individual anxiety induced by the pandemic.

METHODS : Anxiety scores from healthy undergraduate students were collected during the severe and remission periods of the pandemic (first survey, February 22-28, 2020, N=589; second survey, April 24 to May 1, 2020, N=486). Brain imaging data and baseline (daily) anxiety ratings were acquired before the pandemic. The predictive performance of the functional connectome on individual anxiety was examined using machine learning and was validated in two external undergraduate student samples (N=149 and N=474). The clinical relevance of the findings was further explored by applying the connectome-based neuromarkers of pandemic-related anxiety to distinguish between individuals with specific mental disorders and matched healthy control subjects (generalized anxiety disorder, N=43; major depression, N=536; schizophrenia, N=72).

RESULTS : Anxiety scores increased from the prepandemic baseline to the severe stage of the pandemic and remained high in the remission stage. The prepandemic functional connectome predicted pandemic-related anxiety and generalized to the external sample but showed poor performance for predicting daily anxiety. The connectome-based neuromarkers of pandemic-related anxiety further distinguished between participants with generalized anxiety and healthy control subjects but were not useful for diagnostic classification in major depression and schizophrenia.

CONCLUSIONS : These findings demonstrate the feasibility of using the functional connectome to predict individual anxiety induced by major stressful events (e.g., the current global health crisis), which advances our understanding of the neurobiological basis of anxiety susceptibility and may have implications for developing targeted psychological and clinical interventions that promote the reduction of stress and anxiety.

He Li, Wei Dongtao, Yang Fan, Zhang Jie, Cheng Wei, Feng Jianfeng, Yang Wenjing, Zhuang Kaixiang, Chen Qunlin, Ren Zhiting, Li Yu, Wang Xiaoqin, Mao Yu, Chen Zhiyi, Liao Mei, Cui Huiru, Li Chunbo, He Qinghua, Lei Xu, Feng Tingyong, Chen Hong, Xie Peng, Rolls Edmund T, Su Linyan, Li Lingjiang, Qiu Jiang

2021-Apr-26

Anxiety, Coronavirus/COVID-19, Functional Connectome, Neuroimaging, Resting-State fMRI

General General

Optimal Hyperparameter Selection of Deep Learning Models for COVID-19 Chest X-ray Classification.

In Intelligence-based medicine

The first and most critical response to curbing the spread of the novel coronavirus disease (COVID-19) is to deploy effective techniques to test potentially infected patients, isolate them and commence immediate treatment. However, several test kits currently in use are slow and in a shortage of supply. This paper presents techniques for diagnosing COVID-19 from chest X-ray (CXR) and address problems associated with training deep models with less voluminous datasets and class imbalance as obtained in most available CXR datasets on COVID-19. We introduced the discriminative fine-tuning approach, which dynamically assigns different learning rates to each layer of the network. The learning rate is set using the cyclical learning rate policy that changes per iteration. This flexibility ensured rapid convergence and avoided being stuck in saddle point plateau. In addition, we addressed the high computational demand of deep models by implementing our algorithm using the memory- and computational-efficient mixed-precision training. Despite the availability of scanty datasets, our model achieved high performance and generalisation. A Validation accuracy of 96.83%, sensitivity and specificity of 96.26% and 95.54% were obtained, respectively. When tested on an entirely new dataset, the model achieves 97% accuracy without further training. Lastly, we presented a visual interpretation of the model's output to prove that the model can aid radiologists in rapidly screening for the symptoms of COVID-19.

Adedigba Adeyinka P, Adeshina Steve A, Aina Oluwatomisin E, Aibinu Abiodun M

2021-Apr-21

COVID-19, Chest X-ray, Computer-Aided Diagnosis, Cyclical learning rate, Deep Convolutional Neural Network (CNN), Discriminative Fine-tuning, Hyperparameter Optimization, Memory and computation efficient, Mixed-precision training, Overfitting

General General

Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review.

In SN computer science

COVID-19 also referred to as Corona Virus disease is a communicable disease that is caused by a coronavirus. Significant number of people who are tainted with this infection will have to brave and encounter moderate to severe respiratory sickness. Aged persons, sick, convalescing people and all those having underlying health complications like diabetes, chronic breathing diseases and cardiovascular diseases are bound to contract this sickness if not taken proper care of. At the current scenario, there are neither definite treatments nor inoculations against COVID-19. Nevertheless, there are numerous continuing clinical trials assessing the impending treatments and vaccines. Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease using X-rays and CT images. The biggest stumbling block here is that there are only a few datasets available. There is also less number of experts for marking the information explicit to this new strain of infection in people. Artificial Intelligence centred tools can be designed and developed quickly for adapting the existing AI models and for leveraging the ability to modify and associating them with the preliminary clinical understanding to address the new group of COVID-19 and the novel challenges associated with it. In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.

Aishwarya T, Ravi Kumar V

2021

CT images, CheXNet, Convolutional neural network, Corona virus, Covid-19, Covid-net, Deep learning, IRCNN, Machine learning

General General

Prediction of COVID-19 Trend in India and Its Four Worst-Affected States Using Modified SEIRD and LSTM Models.

In SN computer science

Since the beginning of COVID-19 (corona virus disease 2019), the Indian government implemented several policies and restrictions to curtail its spread. The timely decisions taken by the government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread. Future predictions about the spread can be helpful for future policy-making, i.e., to plan and control the COVID-19 spread. Further, it is observed throughout the world that asymptomatic corona cases play a major role in the spread of the disease. This motivated us to include such cases for accurate trend prediction. India was chosen for the study as the population and population density is very high for India, resulting in the spread of the disease at high speed. In this paper, the modified SEIRD (susceptible-exposed-infected-recovered-deceased) model is proposed for predicting the trend and peak of COVID-19 in India and its four worst-affected states. The modified SEIRD model is based on the SEIRD model, which also uses an asymptomatic exposed population that is asymptomatic but infectious for the predictions. Further, a deep learning-based long short-term memory (LSTM) model is also used for trend prediction in this paper. Predictions of LSTM are compared with the predictions obtained from the proposed modified SEIRD model for the next 30 days. The epidemiological data up to 6th September 2020 have been used for carrying out predictions in this paper. Different lockdowns imposed by the Indian government have also been used in modeling and analyzing the proposed modified SEIRD model.

Bedi Punam, Dhiman Shivani, Gole Pushkar, Gupta Neha, Jindal Vinita

2021

COVID-19, Coronavirus, Lockdown, Long short-term memory (LSTM), Modified SEIRD (susceptible–exposed–infected–recovered–deceased), Pandemic

General General

Evaluating the performance of personal, social, health-related, biomarker and genetic data for predicting an individuals future health using machine learning: A longitudinal analysis

ArXiv Preprint

As we gain access to a greater depth and range of health-related information about individuals, three questions arise: (1) Can we build better models to predict individual-level risk of ill health? (2) How much data do we need to effectively predict ill health? (3) Are new methods required to process the added complexity that new forms of data bring? The aim of the study is to apply a machine learning approach to identify the relative contribution of personal, social, health-related, biomarker and genetic data as predictors of future health in individuals. Using longitudinal data from 6830 individuals in the UK from Understanding Society (2010-12 to 2015-17), the study compares the predictive performance of five types of measures: personal (e.g. age, sex), social (e.g. occupation, education), health-related (e.g. body weight, grip strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide polymorphisms (SNPs). The predicted outcome variable was limiting long-term illness one and five years from baseline. Two machine learning approaches were used to build predictive models: deep learning via neural networks and XGBoost (gradient boosting decision trees). Model fit was compared to traditional logistic regression models. Results found that health-related measures had the strongest prediction of future health status, with genetic data performing poorly. Machine learning models only offered marginal improvements in model accuracy when compared to logistic regression models, but also performed well on other metrics e.g. neural networks were best on AUC and XGBoost on precision. The study suggests that increasing complexity of data and methods does not necessarily translate to improved understanding of the determinants of health or performance of predictive models of ill health.

Mark Green

2021-04-26

General General

Modelling and predicting the spread of COVID-19 cases depending on restriction policy based on mined recommendation rules.

In Mathematical biosciences and engineering : MBE

This paper is an extended and supplemented version of the paper "Recommendation Rules Mining for Reducing the Spread of COVID-19 Cases", presented by the authors at the 3rd International Conference on Informatics & Data-Driven Medicine in November 2020. The paper examines the impact of government restrictive measures on the spread and effects of COVID-19. The work is devoted to the improvement of recommendation rules based on novel ensemble of machine learning methods such as regression tree and clustering. The dynamics of migration between countries in clusters, and their relationship with the number of confirmed cases and the percentage of deaths caused by COVID-19, were studied on the example of Poland, Italy and Germany. It is shown that there is a clear relationship between the cluster number and the number of new cases of diseases and death. It has also been shown that different countries' policies to prevent the disease, in particular the timing of restrictive measures, correlate with the dynamics of the spread of COVID-19 and the consequences of the disease. For example, the results show a clear proactive tactic of restrictive measures by example of Germany, and catching up on the spread of the disease by example of Italy. A regression tree and guidelines about influence of features on the spreading of COVID-19 and mortality due to this infection have been constructed. The paper predicts the number of deaths due to COVID-19 on a 21-day interval using the obtained guidelines on the example of Sweden. Such forecasting was carried out for two potential government action options: with existing precautionary actions and the same precautionary actions, if they had been taken 20 days earlier (following the example of Germany). The RMSE of the mortality forecast does not exceed 4.2, which shows a good prognostic ability of the developed model. At the same time, the simulation based on the strategy of anticipatory introduction of restrictions gives 2-6% lower values of the forecast of the number of new cases. Thus, the results of this study provide an opportunity to assess the impact of decisions about restrictive measures and predict, simulate the consequences of restrictions policy.

Yakovyna Vitaliy, Shakhovska Natalya

2021-Mar-24

** COVID-19 , classification , clustering , machine learning , prognostic data **

oncology Oncology

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network.

In Computers in biology and medicine

At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection.

Quan Hao, Xu Xiaosong, Zheng Tingting, Li Zhi, Zhao Mingfang, Cui Xiaoyu

2021-Apr-15

COVID-19, Capsule neural network, Chest X-ray, Classification, Deep learning

General General

CARJ 2021: Year in Review.

In Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

The past year has been one of unprecedented challenge for the modern world and especially the medical profession. This review explores some of the most impactful topics published in the CARJ during the COVID-19 pandemic including physician wellbeing and burnout, patient safety, and technological innovations including dual energy CT, quantitative imaging and ultra-high frequency ultrasound. The impact of the COVID-19 pandemic on trainee education is discussed and evidence-based tips for providing value-added care are reviewed. Patient privacy considerations relevant to the development of artificial intelligence applications for medical imaging are explored. These publications in the CARJ demonstrate that although this year has brought adversity, it has also been a harbinger for new and exciting areas of focus in our field.

Ward Caitlin J, van der Pol Christian B, Patlas Michael N

2021-Apr-22

artificial intelligence, burnout, imaging genomics, patient safety, professional, review

General General

Building An OMOP Common Data Model-Compliant Annotated Corpus for COVID-19 Clinical Trials.

In Journal of biomedical informatics ; h5-index 55.0

Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.

Sun Yingcheng, Butler Alex, Stewart Latoya A, Liu Hao, Yuan Chi, Southard Christopher T, Hyun Kim Jae, Weng Chunhua

2021-Apr-19

COVID-19, Clinical Trial, Eligibility Criteria, Machine Readable Dataset, Structured Text Corpus

General General

Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things.

In Omics : a journal of integrative biology

Digital transformation is impacting every facet of science and society, not least because there is a growing need for digital services and products with the COVID-19 pandemic. But the need for digital transformation in diagnostics and personalized medicine field cuts deeper. In the past, personalized/precision medicine initiatives have been unable to capture the patients' experiences and clinical outcomes in real-time and in real-world settings. The availability of wearable smart sensors, wireless connectivity, artificial intelligence, and the Internet of Medical Things is changing the personalized/precision medicine research and implementation landscape. Digital transformation in poised to accelerate personalized/precision medicine and systems science in multiple fronts such as deep real-time phenotyping with patient-reported outcomes, high-throughput association studies between omics and highly granular phenotypic variation, digital clinical trials, among others. The present expert review offers an analysis of these systems science frontiers with a view to future applications at the intersection of digital health and personalized medicine, or put in other words, signaling the rise of "digital personalized medicine."

Lin Biaoyang, Wu Shengjun

2021-Apr-21

Internet of Medical Things, artificial intelligence, deep phenotyping, digital health, machine learning, personalized medicine, theranostics

General General

Twitter Speaks: An Analysis of Australian Twitter Users' Topics and Sentiments About COVID-19 Vaccination Using Machine Learning.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The novel coronavirus disease (COVID-19) is one of the greatest threats to human beings in terms of healthcare, economy and the society in recent history. Up to this moment, there are no signs of remission and there is no proven effective cure. The vaccine is the primary biomedical preventive measure against the novel coronavirus. However, the public bias or sentiments, as reflected on social media, may have significant impact on the progress to achieve the herd immunity needed principally.

OBJECTIVE : This study aims to use machine learning methods to extract public topics and sentiments on the COVID-19 vaccination on Twitter.

METHODS : We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed the tweets by visualizing the high-frequency word clouds and correlations between word tokens. We built the Latent Dirichlet Allocation (LDA) topic model to identify the commonly discussed topics from massive tweets. We also performed sentiment analysis to understand the overall sentiments and emotions on COVID-19 vaccination in the Australian society.

RESULTS : Our analysis identified three LDA topics including "Attitudes towards COVID-19 and its vaccination", "Advocating infection control measures against COVID-19", and "Misconceptions and complaints about COVID-19 control". In all tweets, nearly two-thirds of the sentiments were positive, and around one-third were negative in the public opinion about the COVID-19 vaccine. Among the eight basic emotions, "trust" and "anticipation" were the two prominent positive emotions, while "fear" was the top negative emotion in the tweets.

CONCLUSIONS : Our new findings indicate that some Australian Twitter users supported infection control measures against COVID-19, and would refute misinformation. However, the others who underestimated the risks and severity of COVID-19 would probably rationalize their position on the COVID-19 vaccinations with certain conspiracy theories. It is also noticed that the level of positive sentiment in the public may not be enough to further a vaccination coverage which would be sufficient to achieve vaccination-induced herd immunity. Governments should explore the public opinion and sentiments towards COVID-19 and its vaccination, and implement an effective vaccination promotion scheme besides supporting the development and clinical administration of COVID-19 vaccines.

CLINICALTRIAL :

Kwok Stephen Wai Hang, Vadde Sai Kumar, Wang Guanjin

2021-Apr-16

General General

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.

In Interdisciplinary sciences, computational life sciences

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Rasheed Jawad, Jamil Akhtar, Hameed Alaa Ali, Al-Turjman Fadi, Rasheed Ahmad

2021-Apr-22

COVID-19, Deep learning, Disease prediction, Drug discovery, Infectious diseases, Machine learning, SARS-CoV-2

General General

RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray.

In Scientific reports ; h5-index 158.0

COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77.

Motamed Saman, Rogalla Patrik, Khalvati Farzad

2021-Apr-21

General General

Pandemic analysis of infection and death correlated with genomic Orf10 mutation in SARS-CoV-2 victims.

In Journal of the Chinese Medical Association : JCMA

BACKGROUND : Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues the pandemic spread of the coronavirus disease 2019 (COVID-19), over 60 million people confirmed infected and at least 1.8 million dead. One of the most known features of this RNA virus is its easiness to be mutated. In late 2020, almost no region of this SARS-CoV-2 genome can be found completely conserved within the original Wu-Han coronavirus. Any information of the SARS-CoV-2 variants emerged through as time being will be evaluated for diagnosis, treatment, and prevention of COVID-19.

METHODS : We extracted more than two million data of SARS-CoV-2 infected patients from the open COVID-19 dashboard. The sequences of the 38-aminoi acid putative Orf10 protein within infected patients were gather output through from National Center for Biotechnology Information (NCBI) and the mutation rates in each position were analyzed and presented in each month of 2020. The mutation rates of A8 and V30 within orf10 are displayed in selected counties: America, India, German and Japan.

RESULTS : The numbers of COVID-19 patients are correlated to the death numbers, but not with the death rates (stable and lower than 3%). The amino acid positions locating at A8(F/G/L), I13 and V30(L) within the Orf10 sequence stay the highest mutation rate; N5, N25 and N36 rank at the lowest one. A8F expressed highly dominant in Japan (over 80%) and German (around 40%) coming to the end of 2020, but no significant finding in other countries.

CONCLUSION : The results demonstrate via mutation analysis of Orf10 can be further combined with advanced tools such as molecular simulation, artificial intelligence (AI) and biosensors that can practically revealed for protein interactions and thus to imply the authentic orf10 function of SARS-CoV-2 in the future.

Yang De-Ming, Lin Fan-Chi, Tsai Pin-Hsing, Chien Yueh, Wang Mong-Lien, Yang Yi-Ping, Chang Tai-Jay

2021-Apr-19

General General

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The current coronavirus disease 2019 (COVID-19) pandemic limits daily activities, even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients' symptoms and recommends the appropriate medical specialty could provide a valuable solution.

OBJECTIVE : In order to establish a contactless method of recommending the appropriate medical specialty, this study aims to construct a deep learning-based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone.

METHODS : We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including four different Long Short-Term Memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer as well as Bidirectional Encoder Representations from Transformers (BERT) for NLP, were trained and validated using a randomly selected test dataset. The performance of the models was evaluated by the precision, recall, F1 score and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this recommendation system. We used an open-source framework called Alpha to develop our AI chatbot. This takes the form of a web application with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web which is compatible with both desktops and smartphones.

RESULTS : The BERT model yielded the best performance, with an AUC of 0.964 and F1 score of 0.768, followed by LSTM with embedding vectors, with an AUC of 0.965 and F1 score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our dataset was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones.

CONCLUSIONS : With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot based on a deep learning-based NLP model that can recommend a medical specialty to patients using their smartphones would be exceedingly useful. The chatbot allows patients to quickly and contactlessly identify the proper medical specialist based on their symptoms, and so may support both patients and primary care providers.

CLINICALTRIAL :

Lee Hyeonhoon, Kang Jaehyun, Yeo Jonghyeon

2021-Apr-17

General General

COVID-19 Automatic Diagnosis with Radiographic Imaging: Explainable AttentionTransfer Deep Neural Networks.

In IEEE journal of biomedical and health informatics

Researchers seek help from machine learning methods to alleviate the enormous burden of reading radiological images for clinicians under the COVID- 19 pandemic. However, clinicians often feel reluctant to trust AI-based models because of its black-box characteristic and lack of proper explainability. This paper proposes an explainable attention transfer classification model based on the knowledge distillation network structure to automatically differentiate COVID-19, community acquired pneumonia (CAP) from healthy lungs with radiographic imaging. The proposed network structure can be divided into teacher network and student network based on the attention transfer direction. Firstly, the teacher network extracts global features and concentrates on the infection regions to generate attention maps. We propose a deformable attention module (DAM) to strengthen infection regions response and suppress noise in irrelevant regions with expanded reception field. Moreover, combining essential information in original input, attention knowledge transfers from teacher network to student network via an image fusion module. Trained with teacher network jointly, the student branch with weighted dense connectivity can focus on irregularly shaped lesion regions to learn discriminative features and improve network performance. Comprehensive experiments have been conducted on public chest X-ray and CT imaging datasets. The proposed architecture achieves state-of-art performance and improves the AI-based model's explainable ability by attention map, severity assessment, and prediction confidence.

Shi Wenqi, Tong Li, Zhu Yuanda, Wang May Dongmei

2021-Apr-21

Radiology Radiology

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions.

In The British journal of radiology

OBJECTIVES : To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19).

METHODS : Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve.

RESULTS : A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%.

CONCLUSIONS : Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation.

ADVANCES IN KNOWLEDGE : Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.

Zhang Bin, Ni-Jia-Ti Ma-Yi-di-Li, Yan Ruike, An Nan, Chen Lv, Liu Shuyi, Chen Luyan, Chen Qiuying, Li Minmin, Chen Zhuozhi, You Jingjing, Dong Yuhao, Xiong Zhiyuan, Zhang Shuixing

2021-Apr-21

General General

Revealing the threat of emerging SARS-CoV-2 mutations to antibody therapies.

In bioRxiv : the preprint server for biology

The ongoing massive vaccination and the development of effective intervention offer the long-awaited hope to end the global rage of the COVID-19 pandemic. However, the rapidly growing SARS-CoV-2 variants might compromise existing vaccines and monoclonal antibody (mAb) therapies. Although there are valuable experimental studies about the potential threats from emerging variants, the results are limited to a handful of mutations and Eli Lilly and Regeneron mAbs. The potential threats from frequently occurring mutations on the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD) to many mAbs in clinical trials are largely unknown. We fill the gap by developing a topology-based deep learning strategy that is validated with tens of thousands of experimental data points. We analyze 261,348 genome isolates from patients to identify 514 non-degenerate RBD mutations and investigate their impacts on 16 mAbs in clinical trials. Our findings, which are highly consistent with existing experimental results about variants from the UK, South Africa, Brazil, US-California, and Mexico shed light on potential threats of 95 high-frequency mutations to mAbs not only from Eli Lilly and Regeneron but also from Celltrion and Rockefeller University that are in clinical trials. We unveil, for the first time, that high-frequency mutations R346K/S, N439K, G446V, L455F, V483F/A, E484Q/V/A/G/D, F486L, F490L/V/S, Q493L, and S494P/L might compromise some of mAbs in clinical trials. Our study gives rise to a general perspective about how mutations will affect current vaccines.

Chen Jiahui, Gao Kaifu, Wang Rui, Wei Guo-Wei

2021-Apr-12

Public Health Public Health

Loneliness and Neurocognitive Aging.

In Advances in geriatric medicine and research

Loneliness imposes significant risks to physical, mental and brain health in older adulthood. With the social distancing regimes implemented during the COVID-19 pandemic, there is even greater urgency to understand the human health costs of social isolation. In this viewpoint we describe how the experience of loneliness may alter the structure and function of the human brain, and how these discoveries may guide public health policy to reduce the burden of loneliness in later life.

Spreng R Nathan, Bzdok Danilo

2021

Alzheimer’s disease, MRI, aging, default mode network, dementia, loneliness, social cognition, social isolation

General General

Smart access development for classifying lung disease with chest x-ray images using deep learning.

In Materials today. Proceedings

Recently the world has come across a pandemic disease known as covid-19. The presence of symptoms of covid-19 and pneumonia may be alike to other types of lung illnesses. So, because of this, it is difficult for the affected person or medical experts to identify the condition. Chest x-ray provides general orientation which can be an initial investigative study in the analysis of lung diseases. Information from retenogram studies help the finding of covid-19 and pneumonia affecting the lungs. We use a Convolution Neural Network (CNN) in Tensor Flow and Keras based covid-19, pneumonia classification. The best fit model of CNN is then deployed in the Django framework for providing a better user interface and predicting the output.

Kumaraguru Tarunika, Abirami P, Darshan K M, Angeline Kirubha S P, Latha S, Muthu P

2021-Apr-16

Covid-19, Deep learning, Django framework, Keras, Pneumonia, TensorFlow

General General

Prediction of covid-19 growth and trend using machine learning approach.

In Materials today. Proceedings

The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

Gothai E, Thamilselvan R, Rajalaxmi R R, Sadana R M, Ragavi A, Sakthivel R

2021-Apr-15

Accuracy, Prediction, Regression, Supervised Learning

General General

Detection of COVID-19 from CT scan images: A spiking neural network-based approach.

In Neural computing & applications

The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

Garain Avishek, Basu Arpan, Giampaolo Fabio, Velasquez Juan D, Sarkar Ram

2021-Apr-16

COVID-19, CT scan, Deep learning, Medical image, Spiking neural network

General General

Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV.

In BMC research notes

OBJECTIVE : In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms.

RESULTS : The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

Huh Jung Eun, Han Seunghee, Yoon Taeseon

2021-Apr-20

Apriori, BLAST, Coronavirus, Decision Tree, MERS-CoV, SARS-CoV, SARS-CoV-2, SVM

General General

COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations

ArXiv Preprint

COVID-19 has been devastating the world since the end of 2019 and has continued to play a significant role in major national and worldwide events, and consequently, the news. In its wake, it has left no life unaffected. Having earned the world's attention, social media platforms have served as a vehicle for the global conversation about COVID-19. In particular, many people have used these sites in order to express their feelings, experiences, and observations about the pandemic. We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic. We present a framework for analysis, mining, and tracking the critical content and characteristics of social media conversations around the pandemic. Focusing on Twitter and Reddit, we have gathered a large-scale dataset on COVID-19 social media conversations. Our analyses cover tracking potential reports on virus acquisition, symptoms, conversation topics, and language complexity measures through time and by region across the United States. We also present a BERT-based model for recognizing instances of hateful tweets in COVID-19 conversations, which achieves a lower error-rate than the state-of-the-art performance. Our results provide empirical validation for the effectiveness of our proposed framework and further demonstrate that social media data can be efficiently leveraged to provide public health experts with inexpensive but thorough insight over the course of an outbreak.

Shayan Fazeli, Davina Zamanzadeh, Anaelia Ovalle, Thu Nguyen, Gilbert Gee, Majid Sarrafzadeh

2021-04-22

General General

Digital Health during COVID-19: Informatics Dialogue with the World Health Organization.

In Yearbook of medical informatics ; h5-index 24.0

BACKGROUND : On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic.

OBJECTIVES : The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs).

METHODS : The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants' consent and summarized in this paper.

RESULTS : The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general; (b) ethics and artificial intelligence; (c) digital divide; (d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic.

CONCLUSIONS : Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly.

Koch Sabine, Hersh William R, Bellazzi Riccardo, Leong Tze Yun, Yedaly Moctar, Al-Shorbaji Najeeb

2021-Apr-21

Cardiology Cardiology

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

In PloS one ; h5-index 176.0

BACKGROUND : Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.

METHODS : We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity.

RESULTS : A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression.

CONCLUSIONS : This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Marcos Miguel, Belhassen-García Moncef, Sánchez-Puente Antonio, Sampedro-Gomez Jesús, Azibeiro Raúl, Dorado-Díaz Pedro-Ignacio, Marcano-Millán Edgar, García-Vidal Carolina, Moreiro-Barroso María-Teresa, Cubino-Bóveda Noelia, Pérez-García María-Luisa, Rodríguez-Alonso Beatriz, Encinas-Sánchez Daniel, Peña-Balbuena Sonia, Sobejano-Fuertes Eduardo, Inés Sandra, Carbonell Cristina, López-Parra Miriam, Andrade-Meira Fernanda, López-Bernús Amparo, Lorenzo Catalina, Carpio Adela, Polo-San-Ricardo David, Sánchez-Hernández Miguel-Vicente, Borrás Rafael, Sagredo-Meneses Víctor, Sanchez Pedro-Luis, Soriano Alex, Martín-Oterino José-Ángel

2021

General General

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

In PLoS pathogens ; h5-index 92.0

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Brierley Liam, Fowler Anna

2021-Apr-20

General General

Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study.

In JMIR mental health

BACKGROUND : The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being.

OBJECTIVE : This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic.

METHODS : In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence.

RESULTS : Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19-generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy.

CONCLUSIONS : Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.

Jha Indra Prakash, Awasthi Raghav, Kumar Ajit, Kumar Vibhor, Sethi Tavpritesh

2021-Apr-20

Bayesian network, COVID-19, artificial intelligence, disorder, explainable artificial intelligence, machine learning, mental health, susceptibility, well-being

General General

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research.

In Artificial intelligence review

Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts' observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .

Soomro Toufique A, Zheng Lihong, Afifi Ahmed J, Ali Ahmed, Yin Ming, Gao Junbin

2021-Apr-15

Artificial intelligence(AI), Classification, Coronavirus (COVID-19), Deep learning, Medical imaging, Segmentation

General General

Analyzing hCov Genome Sequences: Predicting Virulence and Mutation

bioRxiv Preprint

Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. We have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.

Sawmya, S.; Saha, A.; Tasnim, S.; Toufikuzzaman, M.; Anjum, N.; Rafid, A. H. M.; Rahman, M. S.; Rahman, M. S.

2021-04-20

General General

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains

bioRxiv Preprint

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

Rynkiewicz, P.; Babbitt, G. A.; Cui, F.; Hudson, A. O.; Lynch, M. L.

2021-04-20

General General

Boosting Masked Face Recognition with Multi-Task ArcFace

ArXiv Preprint

In this paper, we address the problem of face recognition with masks. Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This sanitary measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. In addition, the need has arisen for applications capable of detecting whether the subjects are wearing masks to control the spread of the virus. To overcome these problems a full training pipeline is presented based on the ArcFace work, with several modifications for the backbone and the loss function. From the original face-recognition dataset, a masked version is generated using data augmentation, and both datasets are combined during the training process. The selected network, based on ResNet-50, is modified to also output the probability of mask usage without adding any computational cost. Furthermore, the ArcFace loss is combined with the mask-usage classification loss, resulting in a new function named Multi-Task ArcFace (MTArcFace). Experimental results show that the proposed approach highly boosts the original model accuracy when dealing with masked faces, while preserving almost the same accuracy on the original non-masked datasets. Furthermore, it achieves an average accuracy of 99.78% in mask-usage classification.

David Montero, Marcos Nieto, Peter Leskovsky, Naiara Aginako

2021-04-20

General General

Masked Face Recognition using ResNet-50

ArXiv Preprint

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.

Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

Public Health Public Health

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.

In Medical image analysis

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from-the-part-to-the-whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.

Signoroni Alberto, Savardi Mattia, Benini Sergio, Adami Nicola, Leonardi Riccardo, Gibellini Paolo, Vaccher Filippo, Ravanelli Marco, Borghesi Andrea, Maroldi Roberto, Farina Davide

2021-Mar-31

COVID-19 severity assessment, Chest X-rays, Convolutional neural networks, End-to-end learning, Semi-quantitative rating

Pathology Pathology

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

In Scientific reports ; h5-index 158.0

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Tran Nam K, Howard Taylor, Walsh Ryan, Pepper John, Loegering Julia, Phinney Brett, Salemi Michelle R, Rashidi Hooman H

2021-Apr-15

General General

Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study.

In BJPsych open

The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found a decrease in the wish to die during lockdown. This is consistent with previous studies showing that suicide rates decrease during periods of social emergency. Smartphone-based EMA can allow us to remotely assess patients and overcome the physical barriers imposed by lockdown.

Cobo Aurora, Porras-Segovia Alejandro, Pérez-Rodríguez María Mercedes, Artés-Rodríguez Antonio, Barrigón Maria Luisa, Courtet Philippe, Baca-García Enrique

2021-Apr-16

COVID-19, Suicide, ecological momentary assessment, machine learning, suicide attempt

General General

Identifying Water Stress in Chickpea Plant by Analyzing Progressive Changes in Shoot Images using Deep Learning

ArXiv Preprint

To meet the needs of a growing world population, we need to increase the global agricultural yields by employing modern, precision, and automated farming methods. In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these image-based machine learning usually do not consider plant stress's progressive or temporal nature. This time-invariant approach also requires images showing severe signs of stress to ensure high confidence detections, thereby reducing this approach's feasibility for early detection and recovery of plants under stress. In order to overcome the problem mentioned above, we propose a temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We then develop an LSTM-CNN architecture to learn visual-temporal patterns from this dataset and predict the water stress category with high confidence. To establish a baseline context, we also conduct a comparative analysis of the CNN architecture used in the proposed model with the other CNN techniques used for the time-invariant classification of water stress. The results reveal that our proposed LSTM-CNN model has resulted in the ceiling level classification performance of \textbf{98.52\%} on JG-62 and \textbf{97.78\%} on Pusa-372 and the chickpea plant data. Lastly, we perform an ablation study to determine the LSTM-CNN model's performance on decreasing the amount of temporal session data used for training.

Shiva Azimi, Rohan Wadhawan, Tapan K. Gandhi

2021-04-16

Public Health Public Health

Modeling the impact of public response on the COVID-19 pandemic in Ontario.

In PloS one ; h5-index 176.0

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.

Eastman Brydon, Meaney Cameron, Przedborski Michelle, Kohandel Mohammad

2021

General General

Signatures of COVID-19 severity and immune response in the respiratory tract microbiome.

In medRxiv : the preprint server for health sciences

Rationale : Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease.

Objective : To define the respiratory tract microbiome in COVID-19 and relationship disease severity, systemic immunologic features, and outcomes.

Methods and Measurements : We examined 507 oropharyngeal, nasopharyngeal and endotracheal samples from 83 hospitalized COVID-19 patients, along with non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR, and immune features were characterized by lymphocyte/neutrophil (L/N) ratios and deep immune profiling of peripheral blood mononuclear cells (PBMC).

Main Results : COVID-19 patients had upper respiratory microbiome dysbiosis, and greater change over time than critically ill patients without COVID-19. Diversity at the first time point correlated inversely with disease severity during hospitalization, and microbiome composition was associated with L/N ratios and PBMC profiles in blood. Intubated patients showed patient-specific and dynamic lung microbiome communities, with prominence of Staphylococcus . Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity.

Conclusions : The respiratory tract microbiome and commensal virome are disturbed in COVID-19, correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, possible use as biomarkers, and role of bacterial and viral taxa identified here in COVID-19 pathogenesis.

Merenstein Carter, Liang Guanxiang, Whiteside Samantha A, Cobián-Güemes Ana G, Merlino Madeline S, Taylor Louis J, Glascock Abigail, Bittinger Kyle, Tanes Ceylan, Graham-Wooten Jevon, Khatib Layla A, Fitzgerald Ayannah S, Reddy Shantan, Baxter Amy E, Giles Josephine R, Oldridge Derek A, Meyer Nuala J, Wherry E John, McGinniss John E, Bushman Frederic D, Collman Ronald G

2021-Apr-05

General General

Early stage risk communication and community engagement (RCCE) strategies and measures against the coronavirus disease 2019 (COVID-19) pandemic crisis.

In Global health journal (Amsterdam, Netherlands)

Coronavirus disease 2019 (COVID-19) pandemic has proven to be tenacious and shows that the global community is still poorly prepared to handling such emerging pandemics. Enhancing global solidarity in emergency preparedness and response, and the mobilization of conscience and cooperation, can serve as an excellent source of ideas and measures in a timely manner. The article provides an overview of the key components of risk communication and community engagement (RCCE) strategies at the early stages in vulnerable nations and populations, and highlight contextual recommendations for strengthening coordinated and sustainable RCCE preventive and emergency response strategies against COVID-19 pandemic. Global solidarity calls for firming governance, abundant community participation and enough trust to boost early pandemic preparedness and response. Promoting public RCCE response interventions needs crucially improving government health systems and security proactiveness, community to individual confinement, trust and resilience solutions. To better understand population risk and vulnerability, as well as COVID-19 transmission dynamics, it is important to build intelligent systems for monitoring isolation/quarantine and tracking by use of artificial intelligence and machine learning systems algorithms. Experiences and lessons learned from the international community is crucial for emerging pandemics prevention and control programs, especially in promoting evidence-based decision-making, integrating data and models to inform effective and sustainable RCCE strategies, such as local and global safe and effective COVID-19 vaccines and mass immunization programs.

Zhang Yanjie, Tambo Ernest, Djuikoue Ingrid C, Tazemda Gildas K, Fotsing Michael F, Zhou Xiao-Nong

2021-Mar

Coronavirus disease 2019 (COVID-19), Governance, Pandemic, Response, Risk communication and community engagement (RCCE), Trust, Vaccination

General General

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In this age of global COVID-19 pandemic, the urgency of addressing an epidemic of obesity and associated inflammatory illnesses has come to the fore. Studies have demonstrated that interactions between single nucleotide polymorphisms (SNPs) and lifestyle interventions like food and exercise may vary metabolic outcomes, contributing to obesity and therapeutic response. However, there is a paucity of research relating outcomes from digital therapeutics to inclusion of genetic data in care interventions.

OBJECTIVE : This study aims to describe and model weight loss of subjects enrolled in a precision digital weight loss program informed by machine learning analysis of subject data, including genomic. It was hypothesized that weight loss models would exhibit better fit when incorporating genomic data than utilizing demographic and engagement variables alone.

METHODS : A cohort of 393 participants enrolled in Digbi's personalized digital care program for 120 days was analyzed retrospectively. Care protocol included the use of subject data informing precision coaching by mobile app and personal coach. Two linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were built by adding 197 SNPs from subject genomic data as predictors, then refitting, employing Lasso regression on SNPs for variable selection. Success/failure logistic regression models were also fit, with and without genomic data.

RESULTS : 72% of subjects in this cohort lost weight, while 17% maintained stable weight. 142 subjects lost 5% within 120 days. Models describe the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. The addition of genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13 respectively. The logistic model improved pseudo R2 from 0.193 to 0.285. Gender, engagement and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways that process food and regulate storage of fat were associated with weight loss in this cohort. This included rs17300539_G (insulin resistance, monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, cholesterol metabolism), and rs4074995_A (calcium-potassium transport, serum calcium levels). Models described greater average weight loss for subjects having more of these risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS : Adding genomic information in modeling outcomes of a digital precision weight loss program greatly enhanced model accuracy. Interpretable weight loss models pointed to efficacy of coaching informed by subjects' genomic risk, accompanied by active engagement of subjects in their own success. While large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss utilizing genetic risk, with digitally delivered recommendations alongside health-coaching to improve intervention efficacy.

CLINICALTRIAL :

Sinha Ranjan, Kachru Dashyanng, Ricchetti Roshni Ray, Singh-Rambiritch Simitha, Muthukumar Karthik Marimuthu, Singaravel Vidhya, Irudayanathan Carmel, Reddy-Sinha Chandana, Junaid Imran, Sharma Garima, Airey Catherine, Francis-Lyon Patricia Alice

2021-Apr-11

General General

BERT based Transformers lead the way in Extraction of Health Information from Social Media

ArXiv Preprint

This paper describes our submissions for the Social Media Mining for Health (SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms(Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the score averaged across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.

Sidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma

2021-04-15

Radiology Radiology

Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the WHO changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods.

OBJECTIVE : Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. This study aimed to design a highly efficient CAD system for COVID-19 by using a NASNet-based algorithm.

METHODS : A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT-scan images of 190 patients with COVID-19 and 59 with Non Covid-19, was used.

RESULTS : After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

CONCLUSIONS : The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources.

CLINICALTRIAL :

Ghaderzadeh Mustafa, Asadi Farkhondeh, Jafari Ramezan, Bashash Davood, Abolghasemi Hassan, Aria Mehrad

2021-Apr-03

Public Health Public Health

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.

OBJECTIVE : The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health.

METHODS : Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.

RESULTS : The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.

CONCLUSIONS : Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.

TRIAL REGISTRATION : International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.

Borges do Nascimento Israel Júnior, Marcolino Milena Soriano, Abdulazeem Hebatullah Mohamed, Weerasekara Ishanka, Azzopardi-Muscat Natasha, Gonçalves Marcos André, Novillo-Ortiz David

2021-Apr-13

World Health Organization, big data, big data analytics, evidence-based medicine, health status, machine learning, overview, public health, secondary data analysis, systematic review

Radiology Radiology