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General General

Real-time face mask position recognition system based on MobileNet model.

In Smart health (Amsterdam, Netherlands)

COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen's Kappa) of this model on the proposed dataset and MaskedFace-Net, a publicly available synthetic dataset created by image editing. Its performance is also compared to other existing methods. The proposed MobileNet is found as the best model providing an accuracy, F1-score, and Cohen's Kappa of 99.23%, 99.22%, and 99.19%, respectively for face mask position recognition. It outperforms the accuracy of the best existing model by about 2%. Finally, an automatic face mask position recognition system has been developed, which can recognize if an individual is wearing a mask correctly or incorrectly. The proposed model performs very well with no drop in recognition accuracy from real images captured by a camera.

Rahman Md Hafizur, Jannat Mir Kanon Ara, Islam Md Shafiqul, Grossi Giuliano, Bursic Sathya, Aktaruzzaman Md

2023-Jan-31

COVID-19, Dataset, Face-mask position recognition, MobileNet, Real-time, Transfer learning

Internal Medicine Internal Medicine

Machine learning models for predicting severe COVID-19 outcomes in hospitals.

In Informatics in medicine unlocked

The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors.

Wendland Philipp, Schmitt Vanessa, Zimmermann Jörg, Häger Lukas, Göpel Siri, Schenkel-Häger Christof, Kschischo Maik

2023

Clinical decision support, Covid-19, Machine learning, Predictive modelling

General General

Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs.

In Journal of visual communication and image representation

The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.5D representation of volumetric chest CTs that allows exploring efficient 2D deep learning architectures while requiring volume-level annotations only. Our learning approach uses a siamese feature extraction backbone applied to each lung. It combines these features into a classification head that explores a novel combination of Squeeze-and-Excite strategies with Class Activation Maps. We experimented with public and in-house datasets and compared our results with state-of-the-art techniques. Our analyses show that our method provides better or comparable prediction quality and accurately distinguishes COVID-19 infections from other kinds of pneumonia and healthy lungs.

da Silveira Thiago L T, Pinto Paulo G L, Lermen Thiago S, Jung Cláudio R

2023-Mar

2.5D representation, COVID-19 diagnosis, Ground-glass opacity, Omnidirectional imaging

General General

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach.

In New generation computing

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R 2 score.

Satapathy Sandeep Kumar, Saravanan Shreyaa, Mishra Shruti, Mohanty Sachi Nandan

2023-Feb-01

Feature selection, Machine learning, Multidimensional, Poverty, Prediction, Regression

General General

Transfer learning for the efficient detection of COVID-19 from smartphone audio data.

In Pervasive and mobile computing

Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users' mobile devices. Considering these issues, and the limited amount of available data, in this paper we present the experimental evaluation of 3 different deep learning models, compared also with hand-crafted features, and of two main approaches of transfer learning in the considered scenario: both feature extraction and fine-tuning. Specifically, we considered VGGish, YAMNET, and L3-Net (including 12 different configurations) evaluated through user-independent experiments on 4 different datasets (13,447 samples in total). Results clearly show the advantages of L3-Net in all the experimental settings as it overcomes the other solutions by 12.3% in terms of Precision-Recall AUC as features extractor, and by 10% when the model is fine-tuned. Moreover, we note that to fine-tune only the fully-connected layers of the pre-trained models generally leads to worse performances, with an average drop of 6.6% with respect to feature extraction. Finally, we evaluate the memory footprints of the different models for their possible applications on commercial mobile devices.

Campana Mattia Giovanni, Delmastro Franca, Pagani Elena

2023-Feb

COVID-19, Deep audio embeddings, Deep learning, Transfer learning, m-health

General General

A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans.

In Neuroscience informatics

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.

Hasija Sanskar, Akash Peddaputha, Bhargav Hemanth Maganti, Kumar Ankit, Sharma Sanjeev

2022-Dec

CNN, COVID-19, Chest CT scan, Classification metrics, Multiclass classification, Two binary classifications

General General

Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA.

In Information processing & management

Infodemics are intertwined with the COVID-19 pandemic, affecting people's perception and social order. To curb the spread of COVID-19 related false rumors, fuzzy-set qualitative comparative analysis (fsQCA) is used to find configurational pathways to enhance rumor refutation effectiveness. In this paper, a total of 1,903 COVID-19 related false rumor refutation microblogs on Sina Weibo are collected by a web crawler from January 1, 2022 to April 20, 2022, and 10 main conditions affecting rumor refutation effectiveness index (REI) are identified based on "three rules of epidemics". To reduce data redundancy, five ensemble machine learning models are established and tuned, among which Light Gradient Boosting Machine (LGBM) regression model has the best performance. Then five core conditions are extracted by feature importance ranking of LGBM. Based on fsQCA with the five core conditions, REI enhancement can be achieved through three different pathway elements configurations solutions: "Highly influential microblogger * high followers' stickiness microblogger", "high followers' stickiness microblogger * highly active microblogger * concise information description" and "high followers' stickiness microblogger * the sentiment tendency of the topic * concise information description". Finally, decision-making suggestions for false rumor refutation platforms and new ideas for improving false rumor refutation effectiveness are proposed. The innovation of this paper reflects in exploring the REI enhancement strategy from the perspective of configuration for the first time.

Li Zongmin, Zhao Ye, Duan Tie, Dai Jingqi

2023-May

COVID-19, Fuzzy-set qualitative comparative analysis (FsQCA), Infodemic, LGBM regression model, Rumor refutation effectiveness

General General

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective.

In Computers & industrial engineering

The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.

Ahmed Tazim, Lekha Karmaker Chitra, Benta Nasir Sumaiya, Abdul Moktadir Md, Kumar Paul Sanjoy

2023-Jan-31

Bayesian Best-Worst Method, Industry 5.0, Post-COVID-19 pandemic, artificial intelligence, supply chain resilience

General General

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.

In Scientific reports ; h5-index 158.0

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

Clyde Austin, Liu Xuefeng, Brettin Thomas, Yoo Hyunseung, Partin Alexander, Babuji Yadu, Blaiszik Ben, Mohd-Yusof Jamaludin, Merzky Andre, Turilli Matteo, Jha Shantenu, Ramanathan Arvind, Stevens Rick

2023-Feb-06

General General

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

In Journal of intensive care medicine ; h5-index 29.0

BACKGROUND : Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.

METHODS : We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.

RESULTS : The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.

CONCLUSIONS : Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

Siepel Sander, Dam Tariq A, Fleuren Lucas M, Girbes Armand R J, Hoogendoorn Mark, Thoral Patrick J, Elbers Paul W G, Bennis Frank C

2023-Feb-06

clinical phenotype half-life, clinical phenotypes, clustering, coronavirus disease 2019, endotypes, intensive care, subphenotypes

General General

A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models.

In Journal of real-time image processing

As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. Depending on the size of the indoors, the number of persons that can fit in that area varies. Then, the size of the indoor area should be measured and the maximum number of people should be calculated accordingly. Computers can be used to ensure the correct application of the capacity rule in indoors monitored by cameras. In this study, a method is proposed to measure the size of a prespecified region in the video and count the people there in real time. According to this method: (1) predetermining the borders of a region on the video, (2) identification and counting of people in this specified region, (3) it is aimed to estimate the size of the specified area and to find the maximum number of people it can take. For this purpose, the You Only Look Once (YOLO) object detection model was used. In addition, Microsoft COCO dataset pre-trained weights were used to identify and label persons. YOLO models were tested separately in the proposed method and their performances were analyzed. Mean average precision (mAP), frame per second (fps), and accuracy rate metrics were found for the detection of persons in the specified region. While the YOLO v3 model achieved the highest value in accuracy rate and mAP (both 0.50 and 0.75) metrics, the YOLO v5s model achieved the highest fps rate among non-Tiny models.

Gündüz Mehmet Şirin, Işık Gültekin

2023

Area estimation, Deep learning, People counting, Person detection, Real-time video processing, YOLO

General General

[Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia].

In Radiologia

OBJECTIVE : Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS : Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS : One hundred fourteen patients (57.4 ± 14.2 years, 65 -57%- men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26 seconds of radiological time.

CONCLUSIONS : Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Plasencia-Martínez Juana María, Pérez-Costa Rafael, Ballesta-Ruiz Mónica, María García-Santos José

2023-Jan-31

AI (Artificial Intelligence), Biomedical Technology, COVID 19, Coronavirus, Prognoses, Radiography

Public Health Public Health

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

In Zeitschrift fur Gesundheitswissenschaften = Journal of public health

AIM : This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown.

SUBJECT AND METHODS : After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results.

RESULTS : First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy.

CONCLUSION : Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

Che ShaoPeng, Wang Xiaoke, Zhang Shunan, Kim Jang Hyun

2023-Jan-31

COVID-19, Crisis and Emergency Risk Communication, Daily new cases, Deep learning, Public health agency, Semantic network analysis

Public Health Public Health

Whale Optimization with Random Contraction and Rosenbrock Method for COVID-19 disease prediction.

In Biomedical signal processing and control

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

Zhang Meilin, Wu Qianxi, Chen Huiling, Asghar Heidari Ali, Cai Zhennao, Li Jiaren, Md Abdelrahim Elsaid, Mansour Romany F

2023-Feb-01

COVID-19, Feature selection, Random contraction strategy, Rosenbrock method, Swarm intelligence, Whale optimization algorithm

General General

Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms.

In Procedia computer science

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

Shobhika Kumar, Prashant Chandra

2023

COVID-19, DASS, Machine learning algorithms, mental health

General General

A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk.

In Procedia computer science

Corona Virus Disease 2019 (COVID-19) is caused by Severe Acute Syndrome Corona Virus 2 (SARS-COV-2). It has become a pandemic disease of the 21st century, killing many lives. During this pandemic situation, precautious measures like social distancing and wearing face mask are being followed globally to break the COVID chain. A pre-programmed viewing system is needed to monitor whether these COVID-19 appropriate behaviours are being followed by the commoners and to ensure COVID-19 preventive measures are followed appropriately. In this work, a deep learning based predictive model and live risk analysis application has been proposed, which detects the high-risk prone areas based on social distancing measures among individuals and face mask wearing tendency of the commoners. The proposed system utilizes ImageNet-1000 dataset for human detection using You Only Look Once (YOLOv3) object detection algorithm; Residual Neural Network (ResNet50v2) uses Kaggle dataset and Real-World Masked Face Dataset (RMFD) for detecting if the persons are face masked or not. Detected human beings (in side-view) are transformed to top view using Top-View Transform Model (TVTM) followed by the calculation of interpersonal distance between the pedestrians and categorized them into three classes include high risk, medium risk, low risk. This unified predictive model provided an accuracy of 97.66%, precision of 97.84%, and F1-Score of 97.92%.

Kaviya P, Chitra P, Selvakumar B

2023

COVID-19, Deep Learning, Face Mask Detection, ResNet50v2, Social Distance Prediction, Top View Transform Model (TVTM), YOLOv3

General General

Coronavirus disease identification using Multi-subband feature analysis in DWT domain.

In Procedia computer science

Coronavirus disease early identification and differentiating it with other lung infections is a complex and time-consuming task. At present RT-PCR and Antigen tests are used for diagnosis, but the whole process is tedious, time exhausting and sometimes gives inaccurate results. Radiological scans like CT scan and X-rays are often considered for confirmation of infection, as it contains vital information about region of infection, disease state and severity, texture, size and opacity of infection. Automated machine learning techniques along with CXR (Chest X-ray) images can serve as alternative approach for Covid-19 diagnosis and differentiating it with other health conditions. In this work, Covid-19 disease identification is performed based on multi-subband feature extraction using 2D Discrete Wavelet Transform (DWT) on CX-Ray images. The CX-ray images are decomposed into multi-subbands of frequencies using DWT. The quarter-sized decomposed low and high frequency components are concatenated into single feature vector. In order to find suitable wavelet filter for extracting features from CX-ray images, a rigorous experimentation is carried out among various wavelet families such as Haar, Daubechies, Symlets, Biorthogonal and their respective members that have different vanishing moment and regularity properties. The feature vector is then used for training machine learning model based on support vector machine classifier. Experimental result shows that the classification model based on Haar wavelet feature extraction performs better as compared to other wavelet families with classification accuracy of 100%.

Ali Nikhat, Yadav Jyotsna

2023

Covid-19, Discrete wavelet transform (DWT), X-ray, classification, machine learning, multi-subband

General General

Data Mining Based Techniques for Covid-19 Predictions.

In Procedia computer science

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic.

Rane Rahul, Dubey Aditya, Rasool Akhtar, Wadhvani Rajesh

2023

Deep Learning Models, Soft Computing-based Models, Stochastic Forecasting Models, Supervised ML Models

General General

SCS-Net: An efficient and practical approach towards Face Mask Detection.

In Procedia computer science

Much work has been done in the computer vision domain for the problem of facial mask detection to curb the spread of the Coronavirus disease (COVID-19). Preventive measures developed using deep learning-based models have got enormous attention. With the state-of-the-art results touching perfect accuracies on various models and datasets, two very practical problems are still not addressed - the deployability of the model in the real world and the crucial cases of incorrectly worn masks. To this end, our method proposes a lightweight deep learning model with just 0.12M parameters having up to 496 times reduction as compared to some of the existing models. Our novel architecture of the deep learning model is designed for practical implications in the real world. We also augment an existing dataset with a large set of incorrectly masked face images leading to a more balanced three-class classification problem. A large collection of 25296 synthetically designed incorrect face mask images are provided. This is the first of its kind of data to be proposed with equal diversity and quantity. The proposed model achieves a competitive accuracy of 95.41% on two class classification and 95.54% on the extended three class classification with minimum number of parameters in comparison. The performance of the proposed system is assessed with various state-of-the-art literature and experimental results indicate that our solution is more realistic and rational than many existing works which use overly massive models unsuitable for practical deployability.

Masud Umar, Siddiqui Momin, Sadiq Mohd, Masood Sarfaraz

2023

CNNs, cosine similarity, covid-19, deep learning, face mask detection, image classification

General General

VGG-COVIDNet: A Novel model for COVID detection from X-Ray and CT Scan images.

In Procedia computer science

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan.

Goyal Lakshay, Dhull Anuradha, Singh Akansha, Kukreja Sonal, Singh Krishna Kant

2023

Covid-19;Deep Learning, VGG-NET;Medical Iamging

Radiology Radiology

AMSFMap Methodology to improve prediction accuracy of CNN model for Covid19 using X-ray images.

In Procedia computer science

A serious medical issue reported at the center of media worldwide, Since December, 2019 is the Covid19 pandemic. As declared by World Health Organization, confirmed cases of Covid19 have been 579,893,790 including 6,415,070 deaths as of 29 July 2022. Even new cases reported in last 24 hours are 20,409 in India. This needs to diagnose and timely treatment of Covid-19 is essential to prevent hurdles including death. The author developed deep learning based Covid19 diagnosis and severity prediction models using x-ray images with hope that this technology can increase access to radiology expertise in remote places where availability of expert radiologist is limited. The researchers proposed and implemented Attentive Multi Scale Feature map based deep Network (AMSF-Net) for x- ray image classification with improved accuracy. In binary classification, x-ray images are classified as normal or Covid19. Multiclass classification classifies x-ray images into mild, moderate or severe infection of Covid19. The researchers utilized lower layers features in addition to features from highest level with different scale to increase ability of CNN to learn fine-grained features. Channel attention also incorporated to amplify features of important channels. ROI based cropping and AHE employed to enhance content of training image. Image augmentation utilized to increase dataset size. To address the issue of the class imbalance problem, focal loss has been applied. Sensitivity, precision, accuracy and F1 score metrics are used for performance evaluation. The author achieved 78% accuracy for binary classification. Precision, recall and F1 score values for positive class is 85, 67 and 75, respectively while 73, 88 and 80 for negative class. Classification accuracy of mild, moderate and sever class is 90, 97 and 96. Average accuracy of 95 % achieved with superior performance compared to existing methods.

Chauhan Hetal, Modi Kirit

2023

CNN, Channel Attention, Covid-19 Diagnosis, Deep Learning, Multi Scale Features, Severity Prediction

General General

COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net.

In Procedia computer science

Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging. The researchers investigated image preprocessing techniques to accomplish this problem and to enable more accurate segmentation by the AI-based systems. This study proposes a COVID-19 Lung-CT segmentation system based on histogram-based non-parametric region localization and enhancement (LE) methods prior to the U-Net architecture. The COVID-19-infected lung CT images were initially processed by the LE method, and the infected regions were detected and enhanced to provide more discriminative features to the deep learning segmentation methods. The U-Net is trained using the enhanced images to segment the regions affected by COVID-19. The proposed system achieved 97.75%, 0.85, and 0.74 accuracy, dice score, and Jaccard index, respectively. The comparison results suggested that the use of LE methods as a preprocessing step in CT Lung images significantly improved the feature extraction and segmentation abilities of the U-Net model by a 0.21 dice score. The results might lead to implementing the LE method in segmenting varied medical images.

Ilhan Ahmet, Alpan Kezban, Sekeroglu Boran, Abiyev Rahib

2023

COVID-19, Enhancement, Localization, Lung CT, U-Net

General General

Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model.

In Procedia computer science

Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated Learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy.

Ambesange Sateesh, Annappa B, Koolagudi Shashidhar G

2023

Federated Learning, Federated Transfer Learning, Lung image segmentation, MRI image segmentation, Transfer Learning, U-net Architecture, X-ray Image segmentation, data privacy

General General

Impact on Air Quality Index of India Due to Lockdown.

In Procedia computer science

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40-45%), compared before the lockdown period, followed by NO2 (27-35%), Sulphur Dioxide (SO2) (2-10%), PM2.5 (35-40%), but the Ozone (O3) rises (12-25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality.

Dubey Aditya, Rasool Akhtar

2023

Deep Learning Models, Soft Computing-based Models, Stochastic Forecasting Models, Supervised ML Models

General General

DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites.

In Analytical biochemistry

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.

Jing Yuanyuan, Zhang Shengli, Wang Houqiang

2023-Feb-03

Bahdanau attention mechanism, Non-classical HLA binding sites, SENet attention mechanism, Word embedding

Public Health Public Health

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.

In Computers in biology and medicine

AIM : COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples.

METHODS : In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic explanations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19- associated biomarker candidate genes and improve the final model's interpretability.

RESULTS : For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class.

CONCLUSIONS : The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.

Yagin Fatma Hilal, Cicek İpek Balikci, Alkhateeb Abedalrhman, Yagin Burak, Colak Cemil, Azzeh Mohammad, Akbulut Sami

2023-Feb-01

COVID-19, Explainable artificial intelligence, LIME, SHAP, XGBoost

Radiology Radiology

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

In Computers in biology and medicine

BACKGROUND : The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial.

METHODS : A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP).

RESULTS : LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods.

CONCLUSIONS : The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

Wu Yanan, Qi Qianqian, Qi Shouliang, Yang Liming, Wang Hanlin, Yu Hui, Li Jianpeng, Wang Gang, Zhang Ping, Liang Zhenyu, Chen Rongchang

2023-Jan-23

COVID-19, Capsule network, Community-acquired pneumonia, Computed tomography, Maximum intensity projection

General General

[CRISPR-based molecular diagnostics: a review].

In Sheng wu gong cheng xue bao = Chinese journal of biotechnology

Rapid and accurate detection technologies are crucial for disease prevention and control. In particular, the COVID-19 pandemic has posed a great threat to our society, highlighting the importance of rapid and highly sensitive detection techniques. In recent years, CRISPR/Cas-based gene editing technique has brought revolutionary advances in biotechnology. Due to its fast, accurate, sensitive, and cost-effective characteristics, the CRISPR-based nucleic acid detection technology is revolutionizing molecular diagnosis. CRISPR-based diagnostics has been applied in many fields, such as detection of infectious diseases, genetic diseases, cancer mutation, and food safety. This review summarized the advances in CRISPR-based nucleic acid detection systems and its applications. Perspectives on intelligent diagnostics with CRISPR-based nucleic acid detection and artificial intelligence were also provided.

Sun Wenjun, Huang Xingxu, Wang Xinjie

2023-Jan-25

CRISPR, CRISPR-based detection, gene editing, molecular detection, nucleic acid detection, point-of-care testing (POCT)

General General

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model.

In Frontiers in medicine

INTRODUCTION : Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).

METHODS : In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.

RESULTS : Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.

CONCLUSION : Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Karampitsakos Theodoros, Sotiropoulou Vasilina, Katsaras Matthaios, Tsiri Panagiota, Georgakopoulou Vasiliki E, Papanikolaou Ilias C, Bibaki Eleni, Tomos Ioannis, Lambiri Irini, Papaioannou Ourania, Zarkadi Eirini, Antonakis Emmanouil, Pandi Aggeliki, Malakounidou Elli, Sampsonas Fotios, Makrodimitri Sotiria, Chrysikos Serafeim, Hillas Georgios, Dimakou Katerina, Tzanakis Nikolaos, Sipsas Nikolaos V, Antoniou Katerina, Tzouvelekis Argyris

2022

antifibrotics, interstitial lung disease, long COVID, machine learning, post-COVID-19

Ophthalmology Ophthalmology

Tele-Glaucoma Using a New Smartphone-based Tool for Visual Field Assessment.

In Journal of glaucoma

PRECIS : Covid-19 underlines the importance of telemedical diagnostics. The Sb-C is a newly developed digital application allowing visual field testing using a head-mounted device and a smartphone. It enables visual field screening remotely from a clinic.

BACKGROUND : Smartphone-based campimetry (Sb-C) is a newly developed tool for functional ophthalmic diagnosis. This study aimed to examine the comparability of the Sb-C and Octopus 900 to ensure ophthalmologic care in times of social distancing.

METHODS : 93 eyes were included in the study. After an ophthalmological examination, the visual field was tested by the Octopus program G1 and by the smartphone-based campimeter. The Sb-C was performed using VR-glasses and an iPhone 6. The software Sb-C was downloaded and installed as SmartCampiTracker app and is examining the 30° visual field with 59 test positions corresponding to the G pattern of Octopus G1. Sensitivities were recorded and saved on the app. Additionally, test-retest reliability was tested on 6 ophthalmologically healthy participants.

RESULTS : The group comprised 48 women and 45 men (mean age: 62.52±12.2 y) including 19 controls, 17 patients with ocular hypertension, 11 preperimetric glaucomas, and 46 perimetric glaucomas. The mean sensitivity (MS) of all points of G1 perimetry was 23.13 dB (95% CI: 22.08-24.18). The MS of the Sb-C was 21.23 dB (95% CI: 20.37-22.08). The correlation between the mean MS measured by G1 perimetry and the Sb-C was strong (r=0.815, P<0.05). The test-retest reliability showed a correlation of r=0.591 (P<0.05.

CONCLUSION : With some technical adjustments, the Sb-C shows promise for screening glaucoma and monitoring disease progression remotely from an ophthalmologic clinic.

Grau Elisabeth, Andrae Stefan, Horn Folkert, Hohberger Bettina, Ring Matthias, Michelson Georg

2022-Nov-29

General General

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.

In JMIR mental health

BACKGROUND : Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE : This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS : A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS : A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS : These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Tornero-Costa Roberto, Martinez-Millana Antonio, Azzopardi-Muscat Natasha, Lazeri Ledia, Traver Vicente, Novillo-Ortiz David

2023-Feb-02

artificial intelligence, health research, mental health, research methodology, research quality, review methodology, systematic review, trial methodology

Pathology Pathology

Host immunological responses facilitate development of SARS-CoV-2 mutations in patients receiving monoclonal antibody treatments.

In The Journal of clinical investigation ; h5-index 129.0

BACKGROUND : The role of host immunity in emergence of evasive SARS-CoV-2 Spike mutations under therapeutic monoclonal antibody (mAb) pressure remains to be explored.

METHODS : In a prospective, observational, monocentric ORCHESTRA cohort study, conducted between March 2021 and November 2022, mild-to-moderately ill COVID-19 patients (n=204) receiving bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, or sotrovimab were longitudinally studied over 28 days for viral loads, de novo Spike mutations, mAb kinetics, seroneutralization against infecting variants of concern, and T-cell immunity. Additionally, a machine learning-based circulating immune-related (CIB) biomarker profile predictive of evasive Spike mutations was constructed and confirmed in an independent dataset (n=19) that included patients receiving sotrovimab or tixagevimab/cilgavimab.

RESULTS : Patients treated with various mAbs developed evasive Spike mutations with remarkable speed and high specificity to the targeted mAb-binding sites. Immunocompromised patients receiving mAb therapy not only continued to display significantly higher viral loads, but also showed higher likelihood of developing de novo Spike mutations. Development of escape mutants also strongly correlated with neutralizing capacity of the therapeutic mAbs and T-cell immunity, suggesting immune pressure as an important driver of escape mutations. Lastly, we showed that an anti-inflammatory and healing-promoting host milieu facilitates Spike mutations, where 4 CIBs identified patients at high risk of developing escape mutations against therapeutic mAbs with high accuracy.

CONCLUSIONS : Our data demonstrate that host-driven immune and non-immune responses are essential for development of mutant SARS-CoV-2. These data also support point-of-care decision-making in reducing the risk of mAb treatment failure and improving mitigation strategies for possible dissemination of escape SARS-CoV-2 mutants.

Gupta Akshita, Konnova Angelina, Smet Mathias, Berkell Matilda, Savoldi Alessia, Morra Matteo, Van Averbeke Vincent, De Winter Fien Hr, Peserico Denise, Danese Elisa, Hotterbeekx An, Righi Elda, De Nardo Pasquale, Tacconelli Evelina, Malhotra-Kumar Surbhi, Kumar-Singh Samir

2023-Feb-09

COVID-19, Cellular immune response

General General

Improving effectiveness of online learning for higher education students during the COVID-19 pandemic.

In Frontiers in psychology ; h5-index 92.0

During the COVID-19 pandemic, online learning has become one of the important ways of higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has become the focus of education research, and the role of the "online learning community" cannot be ignored. In the context of the Internet of Things (IoT), we try to build up a new online learning community model: (1) First, we introduce the Kolb learning style theory to identify different online learning styles; (2) Second, we use a clustering algorithm to identify the nature of different learning style groups; and (3) Third, we introduce the group dynamics theory to design the dimensions of the questionnaire and combine the Analytic Hierarchy Process (AHP) method to identify the key influencing factors of the online learning community. We take business administration majors and students in universities as an example. The results show that (1) as a machine learning method, the clustering algorithm method is superior to the random construction method in identifying different learning style groups, and (2) our method can well judge the importance of each factor based on hierarchical analysis and clarify the different roles of factors in the process of knowledge transfer. This study can provide a useful reference for the sustainable development of online learning in higher education.

Li Xuelan, Pei Zhiqiang

2022

COVID-19, analytic hierarchy process, cluster analysis, group dynamics theory, online learning community

General General

Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : Within the technological development path, chatbots are considered an important tool for economic and social entities to become more efficient and to develop customer-centric experiences that mimic human behavior. Although artificial intelligence is increasingly used, there is a lack of empirical studies that aim to understand consumers' experience with chatbots. Moreover, in a context characterized by constant population aging and an increased life-expectancy, the way aging adults perceive technology becomes of great interest. However, based on the digital divide (unequal access to technology, knowledge, and resources), and since young adults (aged between 18 and 34 years old) are considered to have greater affinity for technology, most of the research is dedicated to their perception. The present paper investigates the way chatbots are perceived by middle-aged and aging adults in Romania.

METHODS : An online opinion survey has been conducted. The age-range of the subjects is 40-78 years old, a convenience sampling technique being used (N = 235). The timeframe of the study is May-June 2021. Thus, the COVID-19 pandemic is the core context of the research. A covariance-based structural equation modelling (CB-SEM) has been used to test the theoretical assumptions as it is a procedure used for complex conceptual models and theory testing.

RESULTS : The results show that while perceived ease of use is explained by the effort, the competence, and the perceive external control in interacting with chatbots, perceived usefulness is supported by the perceived ease of use and subjective norms. Furthermore, individuals are likely to further use chatbots (behavioral intention) if they consider this interaction useful and if the others' opinion is in favor of using it. Gender and age seem to have no effect on behavioral intention. As studies on chatbots and aging adults are few and are mainly investigating reactions in the healthcare domain, this research is one of the first attempts to better understand the way chatbots in a not domain-specific context are perceived later in life. Likewise, judging from a business perspective, the results can help economic and social organizations to improve and adapt AI-based interaction for the aging customers.

Iancu Ioana, Iancu Bogdan

2022

behavioral intention, chatbots, middle-aged and aging adults, perceived ease of use, perceived usefulness, technology acceptance model

Public Health Public Health

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

In Journal of exposure science & environmental epidemiology ; h5-index 34.0

BACKGROUND : Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales.

OBJECTIVE : To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches.

METHODS : We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches.

RESULTS : We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models.

SIGNIFICANCE : The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

Ren Xiang, Mi Zhongyuan, Georgopoulos Panos G

2023-Feb-01

Bayesian geospatial modeling, COVID-19, Explainable machine learning, Exposome and socioexposome, Social/environmental health disparities

General General

Future trajectory of respiratory infections following the COVID-19 pandemic in Hong Kong.

In Chaos (Woodbury, N.Y.)

The accumulation of susceptible populations for respiratory infectious diseases (RIDs) when COVID-19-targeted non-pharmaceutical interventions (NPIs) were in place might pose a greater risk of future RID outbreaks. We examined the timing and magnitude of RID resurgence after lifting COVID-19-targeted NPIs and assessed the burdens on the health system. We proposed the Threshold-based Control Method (TCM) to identify data-driven solutions to maintain the resilience of the health system by re-introducing NPIs when the number of severe infections reaches a threshold. There will be outbreaks of all RIDs with staggered peak times after lifting COVID-19-targeted NPIs. Such a large-scale resurgence of RID patients will impose a significant risk of overwhelming the health system. With a strict NPI strategy, a TCM-initiated threshold of 600 severe infections can ensure a sufficient supply of hospital beds for all hospitalized severely infected patients. The proposed TCM identifies effective dynamic NPIs, which facilitate future NPI relaxation policymaking.

Cheng Weibin, Zhou Hanchu, Ye Yang, Chen Yifan, Jing Fengshi, Cao Zhidong, Zeng Daniel Dajun, Zhang Qingpeng

2023-Jan

General General

Recent Issues in Medical Journal Publishing and Editing Policies: Adoption of Artificial Intelligence, Preprints, Open Peer Review, Model Text Recycling Policies, Best Practice in Scholarly Publishing 4th Version, and Country Names in Titles.

In Neurointervention

In Korea, many editors of medical journal are also publishers; therefore, they need to not only manage peer review, but also understand current trends and policies in journal publishing and editing. This article aims to highlight some of these policies with examples. First, the use of artificial intelligence tools in journal publishing has increased, including for manuscript editing and plagiarism detection. Second, preprint publications, which have not been peer-reviewed, are becoming more common. During the COVID-19 pandemic, medical journals have been more willing to accept preprints to adjust rapidly changing pandemic health issues, leading to a significant increase in their use. Third, open peer review with reviewer comments is becoming more widespread, including the mandatory publication of peer-reviewed manuscripts with comments. Fourth, model text recycling policies provide guidelines for researchers and editors on how to appropriately recycle text, for example, in the background section of the Introduction or the Methods section. Fifth, journals should take into account the recently updated 4th version of the Principles of Transparency and Best Practice in Scholarly Publishing, released in 2022. This version includes more detailed guidelines on journal websites, peer review processes, advisory boards, and author fees. Finally, it recommends that titles of human studies include country names to clarify the cultural context of the research. Each editor must decide whether to adopt these six policies for their journals. Editor-publishers of society journals are encouraged to familiarize themselves with these policies so that they can implement them in their journals as appropriate.

Huh Sun

2023-Feb-01

Artificial intelligence, Culture, Peer review, Policy, Scholarly communication

Public Health Public Health

Voice assistants' responses to questions about the COVID-19 vaccine: a national cross-sectional study.

In JMIR formative research

BACKGROUND : Artificial intelligence (AI)-powered voice assistants (VAs) - like Apple Siri, Google Assistant, and Amazon Alexa - interact with users in natural language and are capable of responding to simple commands, searching the internet, and answering questions. Despite being an increasingly popular way for the public to access health information, VAs could be a source of ambiguous or potentially biased information.

OBJECTIVE : In response to the ongoing prevalence of vaccine misinformation and disinformation, this study aims to evaluate how smartphone VAs respond to information- and recommendation-seeking inquiries regarding the COVID-19 vaccine.

METHODS : A national cross-sectional survey of English-speaking adults who owned a smartphone with a VA installed, conducted online from April 22-28, 2021. The primary outcomes were the VAs' responses to two questions: "Should I get the COVID vaccine?" and "Is the COVID vaccine safe?". Directed content analysis was used to assign a negative, neutral, or positive connotation to each response and website title provided by the VAs. Statistical significance was assessed using the t test (parametric) or Mann-Whitney U (nonparametric) test for continuous variables and the χ2 or Fisher exact test for categorical variables.

RESULTS : Of the 466 survey respondents included in the final analysis, 404 (86.7%) used Apple Siri, 53 (11.4%) used Google Assistant, and 9 (1.9%) used Amazon Alexa. In response to the question "Is the COVID vaccine safe?" 89.9% of users received a direct response, of which 97.3% had a positive connotation encouraging users to get vaccinated. Of the websites presented, only 5.3% had a positive connotation and 94.7% had a neutral connotation. In response to the question "Should I get the COVID vaccine?" 93.1% of users received a list of websites, of which 91.5% had a neutral connotation. For both COVID-19 vaccine-related questions, there was no association between the connotation of a response and the age, gender, zip code, race/ethnicity, or education level of the respondent.

CONCLUSIONS : Our study found that VAs were much more likely to respond directly with positive connotations to the question, "Is the COVID vaccine safe?" but not respond directly and provide a list of websites with neutral connotations to the question, "Should I get the COVID vaccine?" To our knowledge, this is the first study to evaluate how VAs respond to both information- and recommendation-seeking inquiries regarding the COVID-19 vaccine. These findings add to our growing understanding of both the opportunities and pitfalls of VAs in supporting public health information dissemination.

Sossenheimer Philip, Hong Grace, Devon-Sand Anna, Lin Steven

2023-Jan-29

General General

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.

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

A long-standing challenge in pneumonia diagnosis is recognizing the pathological lung texture, especially the ground-glass appearance pathological texture. One main difficulty lies in precisely extracting and recognizing the pathological features. The patients, especially those with mild symptoms, show very little difference in lung texture, neither conventional computer vision methods nor convolutional neural networks perform well on pneumonia diagnosis based on chest X-ray (CXR) images. In the meanwhile, the Coronavirus Disease 2019 (COVID-19) pandemic continues wreaking havoc around the world, where quick and accurate diagnosis backed by CXR images is in high demand. Rather than simply recognizing the patterns, extracting feature maps from the original CXR image is what we need in the classification process. Thus, we propose a Vision Transformer (VIT)-based model called PneuNet to make an accurate diagnosis backed by channel-based attention through X-ray images of the lung, where multi-head attention is applied on channel patches rather than feature patches. The techniques presented in this paper are oriented toward the medical application of deep neural networks and VIT. Extensive experiment results show that our method can reach 94.96% accuracy in the three-categories classification problem on the test set, which outperforms previous deep learning models.

Wang Tianmu, Nie Zhenguo, Wang Ruijing, Xu Qingfeng, Huang Hongshi, Xu Handing, Xie Fugui, Liu Xin-Jun

2023-Jan-31

COVID-19, Deep learning, Multi-head attention, Pneumonia diagnosis, Vision Transformer

General General

Data-driven analysis and predictive modeling on COVID-19.

In Concurrency and computation : practice & experience

The coronavirus (COVID-19) started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID-19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data-driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID-19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches.

Sharma Sonam, Alsmadi Izzat, Alkhawaldeh Rami S, Al-Ahmad Bilal

2022-Dec-25

COVID‐19, gender of patients, global growth rate, predictive modeling, social distancing

General General

A face mask detection system: An approach to fight with COVID-19 scenario.

In Concurrency and computation : practice & experience

A new coronavirus has caused a pandemic crisis around the globe. According to the WHO, this is an infectious illness that spreads from person to person. Therefore, the only way to avoid this infection is to take precautions. Wearing a mask is the most critical COVID-19 protection method because it prevents the virus from spreading from an infected person to a healthy one. This study reflects a deep learning method to create a system for detecting Face Masks. The paper proposes a unique FMDRT (Face Mask Dataset in Real-Time) dataset to determine whether a person is wearing a mask or not. The RFMD and Face Mask datasets are also taken from the internet to evaluate the performance of the proposed method. The CLAHE preprocessing method is employed to enhance the image quality, then resizing and Image augmentation techniques are used to convert it into a standard format and increase the size of the dataset, respectively. The pretrained Caffe face detector model is used to detect the faces, and then the lightweight transfer learning-based Xception model is applied for the feature extraction process. This paper recommended a novel model that is, CL-SSDXcept to distinguish the Face Mask or no mask images. However, accession with the MobileNetV2, VGG16, VGG19, and InceptionV3 models with different hyperparameter settings has been tested on the FMDRT dataset. We have also compared the results of the synthesized dataset FMDRT to the existing Face Mask datasets. The experimental results attained 98% test accuracy on the suggested dataset 'FMDRT' using the CL-SSDXcept method. The empirical findings have been reported at 50 iterations with tuned hyperparameter values with an average accuracy 98% and a loss of 0.05.

Jayaswal Ruchi, Dixit Manish

2022-Dec-25

3D‐face masks, CL‐SSDXcept, COVID‐19, DNN models, face mask detection, hyperparameters, optimizers

General General

Rationing scarce healthcare capacity: A study of the ventilator allocation guidelines during the COVID-19 pandemic.

In Production and operations management

In the United States, even though national guidelines for allocating scarce healthcare resources are lacking, 26 states have specific ventilator allocation guidelines to be invoked in case of a shortage. While several states developed their guidelines in response to the recent COVID-19 pandemic, New York State developed these guidelines in 2015 as "pandemic influenza is a foreseeable threat, one that we cannot ignore." The primary objective of this study is to assess the existing procedures and priority rules in place for allocating/rationing scarce ventilator capacity and propose alternative (and improved) priority schemes. We first build machine learning models using inpatient records of COVID-19 patients admitted to New York-Presbyterian/Columbia University Irving Medical Center and an affiliated community health center to predict survival probabilities as well as ventilator length-of-use. Then, we use the resulting point estimators and their uncertainties as inputs for a multiclass priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P (Sequential Organ Failure Assessment based prioritization), which most closely mimics the existing practice by prioritizing patients with sufficiently low SOFA scores; (ii) ISP (incremental survival probability), which assigns priority based on patient-level survival predictions; and (iii) ISP-LU (incremental survival probability per length-of-use), which takes into account survival predictions and resource use duration. Our findings highlight that our proposed priority scheme, ISP-LU, achieves a demonstrable improvement over the other two alternatives. Specifically, the expected number of survivals increases and death risk while waiting for ventilator use decreases. We also show that ISP-LU is a robust priority scheme whose implementation yields a Pareto-improvement over both SOFA-P and ISP in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.

Anderson David R, Aydinliyim Tolga, Bjarnadóttir Margrét V, Çil Eren B, Anderson Michaela R

2023-Jan-22

COVID‐19, fairness, machine learning, multiclass queueing with abandonments, priority scheduling, resource allocation, scarce ventilator capacity

General General

Federated learning based Covid-19 detection.

In Expert systems

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Chowdhury Deepraj, Banerjee Soham, Sannigrahi Madhushree, Chakraborty Arka, Das Anik, Dey Ajoy, Dwivedi Ashutosh Dhar

2022-Nov-02

COVID‐19, CXR images, Internet of Medical Things (IoMT), Xception, cybersecurity, federated learning, privacy, transfer learning

General General

The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic

ArXiv Preprint

Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world. To gain further insight into this matter, we introduce the Real World Worry Waves Dataset (RW3D). The dataset combines rich open-ended free-text responses with survey data on emotions, significant life events, and psychological stressors in a repeated-measures design in the UK over three years (2020: n=2441, 2021: n=1716 and 2022: n=1152). This paper provides background information on the data collection procedure, the recorded variables, participants' demographics, and higher-order psychological and text-based derived variables that emerged from the data. The RW3D is a unique primary data resource that could inspire new research questions on the psychological impact of the pandemic, especially those that connect modalities (here: text data, psychological survey variables and demographics) over time.

Isabelle van der Vegt, Bennett Kleinberg

2023-02-01

General General

Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.

In Scientific reports ; h5-index 158.0

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Talkar Tanya, Low Daniel M, Simpkin Andrew J, Ghosh Satrajit, O’Keeffe Derek T, Quatieri Thomas F

2023-Jan-28

Radiology Radiology

Longitudinal changes in global structural brain connectivity and cognitive performance in former hospitalized COVID-19 survivors: an exploratory study.

In Experimental brain research

BACKGROUND : Long-term sequelae of COVID-19 can result in reduced functionality of the central nervous system and substandard quality of life. Gaining insight into the recovery trajectory of admitted COVID-19 patients on their cognitive performance and global structural brain connectivity may allow a better understanding of the diseases' relevance.

OBJECTIVES : To assess whole-brain structural connectivity in former non-intensive-care unit (ICU)- and ICU-admitted COVID-19 survivors over 2 months following hospital discharge and correlate structural connectivity measures to cognitive performance.

METHODS : Participants underwent Magnetic Resonance Imaging brain scans and a cognitive test battery after hospital discharge to evaluate structural connectivity and cognitive performance. Multilevel models were constructed for each graph measure and cognitive test, assessing the groups' influence, time since discharge, and interactions. Linear regression models estimated whether the graph measurements affected cognitive measures and whether they differed between ICU and non-ICU patients.

RESULTS : Six former ICU and six non-ICU patients completed the study. Across the various graph measures, the characteristic path length decreased over time (β = 0.97, p = 0.006). We detected no group-level effects (β = 1.07, p = 0.442) nor interaction effects (β = 1.02, p = 0.220). Cognitive performance improved for both non-ICU and ICU COVID-19 survivors on four out of seven cognitive tests 2 months later (p < 0.05).

CONCLUSION : Adverse effects of COVID-19 on brain functioning and structure abate over time. These results should be supported by future research including larger sample sizes, matched control groups of healthy non-infected individuals, and more extended follow-up periods.

Tassignon B, Radwan A, Blommaert J, Stas L, Allard S D, De Ridder F, De Waele E, Bulnes L C, Hoornaert N, Lacor P, Lathouwers E, Mertens R, Naeyaert M, Raeymaekers H, Seyler L, Van Binst A M, Van Imschoot L, Van Liedekerke L, Van Schependom J, Van Schuerbeek P, Vandekerckhove M, Meeusen R, Sunaert S, Nagels G, De Mey J, De Pauw K

2023-Jan-28

Magnetic resonance imaging, Recovery, SARS-CoV-2

Surgery Surgery

Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface.

In Scientific reports ; h5-index 158.0

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

Pirolli Davide, Righino Benedetta, Camponeschi Chiara, Ria Francesco, Di Sante Gabriele, De Rosa Maria Cristina

2023-Jan-27

General General

A survey of machine learning-based methods for COVID-19 medical image analysis.

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

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.

Sailunaz Kashfia, Özyer Tansel, Rokne Jon, Alhajj Reda

2023-Jan-28

COVID-19, Computer tomography, Deep learning, Machine learning, Medical image analysis, Transfer learning

Public Health Public Health

Development of an Artificial Intelligence-Guided Citizen-Centric Predictive Model for the Uptake of Maternal Health Services Among Pregnant Women Living in Urban Slum Settings in India: Protocol for a Cross-sectional Study With a Mixed Methods Design.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Pregnant women are considered a "high-risk" group with limited access to health facilities in urban slums in India. Barriers to using health services appropriately may lead to maternal and child mortality, morbidity, low birth weight, and children with stunted growth. With the increase in the use of artificial intelligence (AI) and machine learning in the health sector, we plan to develop a predictive model that can enable substantial uptake of maternal health services and improvements in adverse pregnancy health care outcomes from early diagnostics to treatment in urban slum settings.

OBJECTIVE : The objective of our study is to develop and evaluate the AI-guided citizen-centric platform that will support the uptake of maternal health services among pregnant women seeking antenatal care living in urban slum settings.

METHODS : We will conduct a cross-sectional study using a mixed methods approach to enroll 225 pregnant women aged 18-44 years, living in the urban slums of Delhi for more than 6 months, seeking antenatal care, and who have smartphones. Quantitative and qualitative data will be collected using an Open Data Kit Android-based tool. Variables gathered will include sociodemographics, clinical history, pregnancy history, dietary history, COVID-19 history, health care facility data, socioeconomic status, and pregnancy outcomes. All data gathered will be aggregated into a common database. We will use AI to predict the early at-risk pregnancy outcomes (in terms of the type of delivery method, term, and related complications) depending on the needs of the beneficiaries translating into effective service-delivery improvements in enhancing the use of maternal health services among pregnant women seeking antenatal care. The proposed research will help policy makers to prioritize resource planning, resource allocation, and the development of programs and policies to enhance maternal health outcomes. The academic research study has received ethical approval from the University Research Ethics Committee of Dehradun Institute of Technology (DIT) University, Dehradun, India.

RESULTS : The study was approved by the University Research Ethics Committee of DIT University, Dehradun, on July 4, 2021. Enrollment of the eligible participants will begin by April 2022 followed by the development of the predictive model by October 2022 till January 2023. The proposed AI-guided citizen-centric tool will be designed, developed, implemented, and evaluated using principles of human-centered design that will help to predict early at-risk pregnancy outcomes.

CONCLUSIONS : The proposed internet-enabled AI-guided prediction model will help identify the potential risk associated with pregnancies and enhance the uptake of maternal health services among those seeking antenatal care for safer deliveries. We will explore the scalability of the proposed platform up to different geographic locations for adoption for similar and other health conditions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : PRR1-10.2196/35452.

Shrivastava Rahul, Singhal Manmohan, Gupta Mansi, Joshi Ashish

2023-Jan-27

artificial intelligence, citizen centric, development, evaluation, informatics, machine learning, maternal health, predictive model

Public Health Public Health

Application of artificial intelligence to the public health education.

In Frontiers in public health

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Wang Xueyan, He Xiujing, Wei Jiawei, Liu Jianping, Li Yuanxi, Liu Xiaowei

2022

algorithm, artificial intelligence, big data, curriculum, education, public health

Public Health Public Health

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.

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

BACKGROUND : Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.

OBJECTIVE : This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.

METHODS : The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.

RESULTS : The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.

CONCLUSIONS : A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.

Raza Shaina, Schwartz Brian

2023-Jan-26

Artificial intelligence, COVID-19, Data cohort, Named entity, Natural language processing, Relation extraction, Transfer learning

Public Health Public Health

Insights in paediatric virology during the COVID-19 era (Review).

In Medicine international

The present article provides an overview of the key messages of the topics discussed at the '7th Workshop on Paediatric Virology', which was organised virtually on December 20, 2021 by the Institute of Paediatric Virology, located on the Island of Euboea in Greece. The workshop's plenary lectures were on: i) viral pandemics and epidemics in the ancient Mediterranean; ii) the impact of obesity on the outcome of viral infections in children and adolescents; and iii) COVID-19 and artificial intelligence. Despite the scarcity of evidence from fossils and remnants, viruses have been recognised as significant causes of several epidemics in the ancient Mediterranean. Paediatric obesity, a modifiable critical health risk factor, has been shown to impact on the development, progression and severity of viral infections. Thus, the prevention of paediatric obesity should be included in formulating public health policies and decision-making strategies against emerging global viral threats. During the current COVID-19 pandemic, artificial intelligence has been used to facilitate the identification, monitoring and prevention of SARS-CoV-2. In the future, it will play a fundamental role in the surveillance of epidemic-prone infectious diseases, in the repurposing of older therapies and in the design of novel therapeutic agents against viral infections. The collaboration between different medical specialties and other diverse scientific fields, including archaeology, history, epidemiology, nutritional technologies, mathematics, computer technology, engineering, medical law and ethics is essential for the successful management of paediatric viral infections. The current COVID-19 pandemic has underscored this need, which should be further encouraged in modern medical education.

Mammas Ioannis N, Liston Maria, Koletsi Patra, Vitoratou Dimitra-Irinna, Koutsaftiki Chryssie, Papatheodoropoulou Alexia, Kornarou Helen, Theodoridou Maria, Kramvis Anna, Drysdale Simon B, Spandidos Demetrios A

2022

Institute of Paediatric Virology, ancient Mediterranean, artificial intelligence, coronavirus disease 2019, obesity, paediatric virology, severe acute respiratory syndrome coronavirus 2, viral infections, viral pandemics

General General

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

In Journal of translational medicine

BACKGROUND : Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS : We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS : To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS : All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Ren Zhong-Hao, You Zhu-Hong, Zou Quan, Yu Chang-Qing, Ma Yan-Fang, Guan Yong-Jian, You Hai-Ru, Wang Xin-Fei, Pan Jie

2023-Jan-25

Drug–protein interactions, Joint learning, Meta-path, Multi-modal, Natural language processing, Sequence analysis

General General

An open-source molecular builder and free energy preparation workflow.

In Communications chemistry

Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow , along with a tutorial.

Bieniek Mateusz K, Cree Ben, Pirie Rachael, Horton Joshua T, Tatum Natalie J, Cole Daniel J

2022-Oct-27

General General

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

In Briefings in bioinformatics

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

Zhou Haiwei, Tan Wenxi, Shi Shaoping

2023-Jan-24

Gaussian prior, gated attention mechanism, methylation, residual network, weighted joint loss function

General General

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19.

In Biology methods & protocols

Artificial intelligence (AI) as a suite of technologies can complement systematic review and meta-analysis studies and answer questions that cannot be typically answered using traditional review protocols and reporting methods. The purpose of this protocol is to introduce a new protocol to complete systematic review and meta-analysis studies. In this work, systematic review, meta-analysis, and meta-analysis network based on selected AI technique, and for P < 0.05 are followed, with a view to responding to questions and challenges that the global population is facing in light of the COVID-19 pandemic. Finally, it is expected that conducting reviews by following the proposed protocol can provide suitable answers to some of the research questions raised due to COVID-19.

Salari Nader, Shohaimi Shamarina, Kiaei Aliakbar, Hosseinian-Far Amin, Mansouri Kamran, Ahmadi Arash, Mohammadi Masoud

2023

COVID-19, artificial intelligence, meta-analysis, network meta-analysis, protocol, systematic review

Public Health Public Health

Conspiracy beliefs and COVID-19 guideline adherence in adolescent psychiatric outpatients: the predictive role of adverse childhood experiences.

In Child and adolescent psychiatry and mental health

BACKGROUND : Conspiracy beliefs have become widespread throughout the COVID-19 pandemic. Previous studies have shown that endorsing conspiracy beliefs leads to lower protective guideline adherence (i.e., wearing face masks), posing a threat to public health measures. The current study expands this research across the lifespan, i.e., in a sample of adolescents with mental health problems. Here, we investigated the association between conspiracy beliefs and guideline adherence while also exploring the predictors of conspiracy beliefs.

METHODS : N = 93 adolescent psychiatric outpatients (57% female, mean age: 15.8) were assessed using anonymous paper-pencil questionnaires. Endorsement of generic and COVID-19 conspiracy beliefs was assessed, in addition to items measuring adherence to protective guidelines and mental health (stress, depressive symptoms, emotional/behavioral problems, and adverse childhood experiences). Multiple regressions and supervised machine learning (conditional random forests) were used for analyses.

RESULTS : Fourteen percent of our sample fully endorsed at least one COVID-19 conspiracy theory, while protective guidelines adherence was relatively high (M = 4.92, on a scale from 1 to 7). The endorsement of COVID-19 conspiracy beliefs-but not of generic conspiracy beliefs-was associated with lower guideline adherence (β = - 0.32, 95% CI - 0.53 to - 0.11, p < .001). Conditional random forests suggested that adverse childhood experiences and peer and conduct problems were relevant predictors of both conspiracy belief categories.

CONCLUSION : While a significant proportion of our sample of adolescents in psychiatric treatment endorsed conspiracy beliefs, the majority did not. Furthermore, and to some degree, contrary to public perception, we found that adolescents show relatively good adherence to public health measures-even while experiencing a high degree of mental distress. The predictive value of adverse childhood experiences and peer/conduct problems for conspiracy beliefs might be explained by compensatory mechanisms to ensure the safety, structure, and inclusion that conspiracies provide.

Goreis Andreas, Pfeffer Bettina, Zesch Heidi Elisabeth, Klinger Diana, Reiner Tamara, Bock Mercedes M, Ohmann Susanne, Sackl-Pammer Petra, Werneck-Rohrer Sonja, Eder Harald, Skala Katrin, Czernin Klara, Mairhofer Dunja, Rohringer Bernhard, Bedus Carolin, Lipp Ronja, Vesely Christine, Plener Paul L, Kothgassner Oswald D

2023-Jan-24

Adolescents, Adverse childhood experiences, COVID-19, Childhood Trauma, Conspiracy beliefs, Guideline adherence, Mental health

Public Health Public Health

Generating simple classification rules to predict local surges in COVID-19 hospitalizations.

In Health care management science

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Yaesoubi Reza, You Shiying, Xi Qin, Menzies Nicolas A, Tuite Ashleigh, Grad Yonatan H, Salomon Joshua A

2023-Jan-24

COVID-19, Decision tree, Machine learning, Prediction, Simulation, Surveillance

General General

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.

In Genetic epidemiology

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Liu Zihuan, Dai Wei, Wang Shiying, Yao Yisha, Zhang Heping

2023-Jan-24

COVID-19, SARS-CoV-2, TAS2R1, UK Biobank, deep learning

General General

Deep learning approach to security enforcement in cloud workflow orchestration.

In Journal of cloud computing (Heidelberg, Germany)

Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients' data managed by a workflow deployed on the cloud need to be protected, and communication of such data across multiple stakeholders should also be secured. In general, security threats in cloud environments have been studied extensively. Such threats include data breaches, data loss, denial of service, service rejection, and malicious insiders generated from issues such as multi-tenancy, loss of control over data and trust. Supporting the security of a cloud workflow deployed and executed over a dynamic environment, across different platforms, involving different stakeholders, and dynamic data is a difficult task and is the sole responsibility of cloud providers. Therefore, in this paper, we propose an architecture and a formal model for security enforcement in cloud workflow orchestration. The proposed architecture emphasizes monitoring cloud resources, workflow tasks, and the data to detect and predict anomalies in cloud workflow orchestration using a multi-modal approach that combines deep learning, one class classification, and clustering. It also features an adaptation scheme to cope with anomalies and mitigate their effect on the workflow cloud performance. Our prediction model captures unsupervised static and dynamic features as well as reduces the data dimensionality, which leads to better characterization of various cloud workflow tasks, and thus provides better prediction of potential attacks. We conduct a set of experiments to evaluate the proposed anomaly detection, prediction, and adaptation schemes using a real COVID-19 dataset of patient health records. The results of the training and prediction experiments show high anomaly prediction accuracy in terms of precision, recall, and F1 scores. Other experimental results maintained a high execution performance of the cloud workflow after applying adaptation strategy to respond to some detected anomalies. The experiments demonstrate how the proposed architecture prevents unnecessary wastage of resources due to anomaly detection and prediction.

El-Kassabi Hadeel T, Serhani Mohamed Adel, Masud Mohammad M, Shuaib Khaled, Khalil Khaled

2023

Anomaly detection, Cloud, Cloud workflow, Covid-19, Deep learning, Prediction, Security enforcement

General General

Protein-ligand binding affinity prediction with edge awareness and supervised attention.

In iScience

Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization.

Gu Yuliang, Zhang Xiangzhou, Xu Anqi, Chen Weiqi, Liu Kang, Wu Lijuan, Mo Shenglong, Hu Yong, Liu Mei, Luo Qichao

2023-Jan-20

Biocomputational method, Classification of proteins, Molecular interaction

General General

Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods.

In Heliyon

The article focuses on analyzing the robustness of Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in unemployment rate estimation. In this context, a stochastic trend in the unemployment rate was determined by using monthly data in Turkey. The oil price, real exchange rate, interest rate and unemployment rate variables are imported into the ARIMA and ANN models with 176 data samples for the period of 01.01.2008-31.08.2022. The results of the conventional linear ARIMA and nonlinear ANN regressor models are compared. The comparison results show that the ARMA (2,1) model is the most suitable model for the unemployment rate estimation. This conclusion was reached based on ARMA (2,1) and ANN's RMSE, MAE, MAPE and R2 parameters. From the results of the specified criteria, it was found that both models gave results close to the actual unemployment rate however ARMA (2,1) was the more appropriate model for the current data set. The actual unemployment data and the estimated values are also given verifying the better modeling of the developed ARMA (2,1) model. In addition, there are meaningful relationships between month variables and the employment rate. This result supports that the unemployment possesses chronic reasons in Turkey. On the other side, the unemployment rate forecasting error of the ARMA (2,1) is higher than the ANN model for the 2020-2021 period during the intense pandemic. This result is important because it shows that during the times of the economic uncertainty caused by the Covid-19 pandemic, forecasts employing the neural network model is observed to have lower errors than the results of autoregressive moving average model. Therefore, under an economic uncertainty, it is shown that modeling the unemployment rate using artificial neural network provides novel insights for economic forecasting.

Yamacli Dilek Surekci, Yamacli Serhan

2023-Jan

ANN, ARIMA, Estimation, Unemployment rate

Internal Medicine Internal Medicine

Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort.

In Open forum infectious diseases

BACKGROUND : The mortality rates of coronavirus disease 2019 (COVID-19) have been changed across the epidemiological waves. The aim was to investigate the differences in mortality rates of COVID-19 patients in Japan across the 6 epidemiological waves stratified by age group and Coronavirus Clinical Characterisation Consortium (4C) mortality score risk group.

METHODS : A total of 56 986 COVID-19 patients in the COVID-19 Registry Japan from 2 March 2020 to 1 February 2022 were enrolled. These patients were categorized into 4 risk groups based on their 4C mortality score. Mortality rates of each risk group were calculated separately for different age groups: 18-64, 65-74, 75-89, and ≥90 years. In addition, mortality rates across the wave periods were calculated separately in 2 age groups: <75 and ≥75 years. All calculated mortality rates were compared with reported data from the United Kingdom (UK) during the early epidemic.

RESULTS : The mortality rates of patients in Japan were significantly lower than in the UK across the board, with the exception of patients aged ≥90 years at very high risk. The mortality rates of patients aged ≥75 years at very high risk in the fourth and fifth wave periods showed no significant differences from those in the UK, whereas those in the sixth wave period were significantly lower in all age groups and in all risk groups.

CONCLUSIONS : The present analysis showed that COVID-19 patients had a lower mortality rate in the most recent sixth wave period, even among patients ≥75 years old at very high risk.

Baba Hiroaki, Ikumi Saori, Aoyama Shotaro, Ishikawa Tetsuo, Asai Yusuke, Matsunaga Nobuaki, Ohmagari Norio, Kanamori Hajime, Tokuda Koichi, Ueda Takuya, Kawakami Eiryo

2023-Jan

4C mortality score, COVID-19, Japan, elderly, epidemic wave

General General

CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19.

In Informatics in medicine unlocked

Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensive hyperparameter adjustment and are unsuitable for small datasets. By leveraging the data in a comparatively small dataset, few-shot learning algorithms aim to reduce the requirement of large datasets. This inspired us to develop a few-shot learning model for early detection of COVID-19 to reduce the post-effect of this dangerous disease. The proposed architecture combines few-shot learning with an ensemble of pre-trained convolutional neural networks to extract feature vectors from CT scan images for similarity learning. The proposed Triplet Siamese Network as the few-shot learning model classified CT scan images into Normal, COVID-19, and Community-Acquired Pneumonia. The suggested model achieved an overall accuracy of 98.719%, a specificity of 99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT scans per category for training data.

Ornob Tareque Rahman, Roy Gourab, Hassan Enamul

2023

COVID-19 diagnosis, CT scan images, Ensemble CNN, Few-shot learning, Triplet siamese network

General General

ELUCNN for explainable COVID-19 diagnosis.

In Soft computing

COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client-server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of 94.41 ± 0.98 , a specificity of 94.84 ± 1.21 , an accuracy of 94.62 ± 0.96 , and an F1 score of 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

Wang Shui-Hua, Satapathy Suresh Chandra, Xie Man-Xia, Zhang Yu-Dong

2023-Jan-13

COVID-19, Cloud computing, Convolutional neural network, Cross validation, Deep learning, Exponential linear unit, Mobile app, Multiple-way data augmentation, SARS-CoV-2

General General

Assessment of the digital competencies of university instructors through use of the machine learning method.

In SN social sciences

The explosion of COVID-19 has brought new challenges to the education industry, especially higher education. Digital competency is becoming an essential competency for higher education instructors, and how to assess instructors' digital competency is attracting increasing attention in higher education. However, most studies have used self-report questionnaires or manual reviews to assess digital competencies, which are time-consuming and potentially biased, and there is a current need for valid and effective assessment methods. To address this issue, this study uses machine learning to analyze syllabi to assess the extent to which university instructors have incorporated digital competency into their courses. The results show that not only is the proposed method feasible, but the results of the assessment using machine learning are highly consistent with those of the human assessment. This approach contributes to the assessment of digital competency in higher education institutions and provides evidence that can be used as a reference for future research on the development of digital competency in higher education institutions.

Yang Tzu-Chi

2023

Digital competence, Higher education, Machine learning, Syllabus analysis

General General

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.

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

The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.

Vinod Dasari Naga, Prabaharan S R S

2023-Jan-17

General General

Development of a Fast Fourier Transform-based Analytical Method for COVID-19 Diagnosis from Chest X-Ray Images Using GNU Octave.

In Journal of medical physics

PURPOSE : Many artificial intelligence-based computational procedures are developed to diagnose COVID-19 infection from chest X-ray (CXR) images, as diagnosis by CXR imaging is less time consuming and economically cheap compared to other detection procedures. Due to unavailability of skilled computer professionals and high computer architectural resource, majority of the employed methods are difficult to implement in rural and poor economic settings. Majority of such reports are devoid of codes and ignores related diseases (pneumonia). The absence of codes makes limitation in applying them widely. Hence, validation testing followed by evidence-based medical practice is difficult. The present work was aimed to develop a simple method that requires a less computational expertise and minimal level of computer resource, but with statistical inference.

MATERIALS AND METHODS : A Fast Fourier Transform-based (FFT) method was developed with GNU Octave, a free and open-source platform. This was employed to the images of CXR for further analysis. For statistical inference, two variables, i.e., the highest peak and number of peaks in the FFT distribution plot were considered.

RESULTS : The comparison of mean values among different groups (normal, COVID-19, viral, and bacterial pneumonia [BP]) showed statistical significance, especially when compared to normal, except between viral and BP groups.

CONCLUSION : Parametric statistical inference from our result showed high level of significance (P < 0.001). This is comparable to the available artificial intelligence-based methods (where accuracy is about 94%). Developed method is easy, availability with codes, and requires a minimal level of computer resource and can be tested with a small sample size in different demography, and hence, be implemented in a poor socioeconomic setting.

Majumder Durjoy

2022

COVID-19, Chest X-ray image, Fourier analysis, image analysis, pneumonia

Surgery Surgery

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients.

In Frontiers in cardiovascular medicine

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Li Hongyu, Sun Xinti, Li Zesheng, Zhao Ruiping, Li Meng, Hu Taohong

2022

acute myocardial infarction, bioinformatics, immune infiltration, machine learning, prognosis

General General

Analysis of individual characteristics influencing user polarization in COVID-19 vaccine hesitancy.

In Computers in human behavior ; h5-index 125.0

During the COVID-19 pandemic, vaccine hesitancy proved to be a major obstacle in efforts to control and mitigate the negative consequences of COVID-19. This study centered on the degree of polarization on social media about vaccine use and contributing factors to vaccine hesitancy among social media users. Examining the discussion about COVID-19 vaccine on the Weibo platform, a relatively comprehensive system of user features was constructed based on psychological theories and models such as the curiosity-drive theory and the big five model of personality. Then machine learning methods were used to explore the paramount impacting factors that led users into polarization. Findings revealed that factors reflecting the activity and effectiveness of social media use promoted user polarization. In contrast, features reflecting users' information processing ability and personal qualities had a negative impact on polarization. This study hopes to help healthcare organizations and governments understand and curb social media polarization around vaccine development in the face of future surges of pandemics.

Xie Lei, Wang Dandan, Ma Feicheng

2023-Jan-17

Big five model of personality, COVID-19 pandemic, Curiosity-drive theory, User polarization, Vaccine hesitancy

General General

Safety, Tolerability and Pharmacokinetics of Half-Life Extended SARS-CoV-2 Neutralizing Monoclonal Antibodies AZD7442 (Tixagevimab/Cilgavimab) in Healthy Adults.

In The Journal of infectious diseases ; h5-index 82.0

BACKGROUND : AZD7442 is a combination of extended half-life, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific neutralizing monoclonal antibodies (tixagevimab/cilgavimab).

METHODS : This phase 1, first-in-human, randomized, double-blind, placebo-controlled, dose-escalation study evaluated AZD7442 administered intramuscularly (300 mg) or intravenously (300, 1000, 3000 mg) in healthy adults (aged 18-55 years). The primary endpoint was safety and tolerability. Secondary endpoints included pharmacokinetics and anti-drug antibodies.

RESULTS : Between August 18-October 16, 2020, 60 participants enrolled; 50 received AZD7442 and 10 received placebo. Adverse events (all of mild/moderate intensity) occurred in 26 (52.0%) and 8 (80.0%) participants (AZD7442 and placebo groups, respectively). No infusion- or injection-site, or hypersensitivity reactions occurred. Tixagevimab and cilgavimab had mean half-lives of approximately 90 days (range: 87.0-95.3 [tixagevimab], 79.8--91.1 [cilgavimab]) and similar pharmacokinetic profiles over the 361-day study period. SARS-CoV-2-specific neutralizing antibody titers provided by AZD7442 were maintained above those in plasma from convalescent coronavirus disease-19 (COVID-19) patients.

CONCLUSIONS : AZD7442 was well tolerated in healthy adults, showing a favorable safety profile across all doses. Depending on the SARS-CoV-2 variant, pharmacokinetic analyses suggest AZD7442 could offer protection for at least 6 months against symptomatic COVID-19 following a single 300 mg intramuscular administration.

CLINICAL TRIALS REGISTRATION : NCT04507256 (https://clinicaltrials.gov/ct2/show/NCT04507256).

Forte-Soto Pablo, Albayaty Muna, Brooks Dennis, Arends Rosalinda H, Tillinghast John, Aksyuk Anastasia A, Bouquet Jerome, Chen Cecil, Gebre Asfiha, Kubiak Robert J, Pilla Reddy Venkatesh, Seegobin Seth, Streicher Katie, Templeton Alison, Esser Mark T

2023-Jan-23

COVID-19, SARS-CoV-2, monoclonal antibody, pharmacokinetics, phase 1, safety, tolerability

Surgery Surgery

Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations.

In Translational behavioral medicine

Smartphone weight loss apps are constantly being developed but the essential elements needed by a multi-ethnic population with overweight and obesity remains unclear. Purpose: To explore the perceptions of an Asian multi-ethnic population with overweight and obesity on the essential elements of weight loss apps. Twenty two participants were purposively sampled from a specialist weight management clinic in Singapore from 13 April to 30 April 2021. Recorded interviews were conducted using face-to-face and videoconferencing modalities. Data saturation was reached at the 18th participant. Data analysis was performed using inductive content analysis with constant comparison between and within transcripts. Findings: Three themes and eight subthemes on the essential app components emerged-(a) comprehensive and flexible calorie counters; (b) holistic, gradual and individualized behavior change recommendations tailored for people with overweight and obesity, and (c) just-in-time reminders of future consequences. There was a need to incorporate flexible options for food logging; break down general recommendations into small steps towards sustainable changes; tailor app contents for people with overweight and obesity; and evoke one's considerations of future consequences. Future weight loss apps should be designed to meet the needs of those with overweight and obesity, the very population that needs assistance with weight loss. Future apps could consider leveraging the capacity of artificial intelligence to provide personalized weight management in terms of sustaining self-regulation behaviors, optimizing goal-setting and providing personalized and timely recommendations for weight loss.

Chew Han Shi Jocelyn, Lim Su Lin, Kim Guowei, Kayambu Geetha, So Bok Yan Jimmy, Shabbir Asim, Gao Yujia

2023-Jan-23

App, BMI, Behavior, Perceptions, Weight management, mHealth

Dermatology Dermatology

The role of mobile teledermoscopy in skin cancer triage and management during the COVID-19 pandemic.

In Indian journal of dermatology, venereology and leprology

The unprecedented onset of the COVID-19 crisis poses a significant challenge to all fields of medicine, including dermatology. Since the start of the coronavirus outbreak, a stark decline in new skin cancer diagnoses has been reported by countries worldwide. One of the greatest challenges during the pandemic has been the reduced access to face-to-face dermatologic evaluation and non-urgent procedures, such as biopsies or surgical excisions. Teledermatology is a well-integrated alternative when face-to-face dermatological assistance is not available. Teledermoscopy, an extension of teledermatology, comprises consulting dermoscopic images to improve the remote assessment of pigmented and non-pigmented lesions when direct visualisation of lesions is difficult. One of teledermoscopy's greatest strengths may be its utility as a triage and monitoring tool, which is critical in the early detection of skin cancer, as it can reduce the number of unnecessary referrals, wait times, and the cost of providing and receiving dermatological care. Mobile teledermoscopy may act as a communication tool between medical practitioners and patients. By using their smartphone (mobile phone) patients can monitor a suspicious skin lesion identified by their medical practitioner, or alternatively self-detect concerning lesions and forward valuable dermoscopic images for remote medical evaluation. Several mobile applications that allow users to photograph suspicious lesions with their smartphones and have them evaluated using artificial intelligence technology have recently emerged. With the growing popularity of mobile apps and consumer-involved healthcare, this will likely be a key component of skin cancer screening in the years to come. However, most of these applications apply artificial intelligence technology to assess clinical images rather than dermoscopic images, which may lead to lower diagnostic accuracy. Incorporating the direct-to-consumer mobile dermoscopy model in combination with mole-scanning artificial intelligence as a mobile app may be the future of skin cancer detection.

Lee Claudia, Witkowski Alexander, Żychowska Magdalena, Ludzik Joanna

2022-Dec-08

COVID-19, Dermoscopy, melanoma, skin cancer, smartphone, teledermoscopy

General General

A framework for designing AI systems that support community wellbeing.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : Designing artificial intelligence (AI) to support health and wellbeing is an important and broad challenge for technologists, designers, and policymakers. Drawing upon theories of AI and cybernetics, this article offers a design framework for designing intelligent systems to optimize human wellbeing. We focus on the production of wellbeing information feedback loops in complex community settings, and discuss the case study of My Wellness Check, an intelligent system designed to support the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic.

METHODS : The basis for our discussion is the community-led design of My Wellness Check, an intelligent system that supported the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic. Our system was designed to create an intelligent feedback loop to assess community wellbeing needs and to inform community action. This article provides an overview of our longitudinal assessment of students and staff wellbeing (n = 20,311) across two years of the COVID-19 pandemic.

RESULTS : We further share the results of a controlled experiment (n = 1,719) demonstrating the enhanced sensitivity and user experience of our context-sensitive wellbeing assessment.

DISCUSSION : Our approach to designing "AI for community wellbeing," may generalize to the systematic improvement of human wellbeing in other human-computer systems for large-scale governance (e.g., schools, businesses, NGOs, platforms). The two main contributions are: 1) showcasing a simple way to draw from AI theory to produce more intelligent human systems, and 2) introducing a human-centered, community-led approach that may be beneficial to the field of AI.

van der Maden Willem, Lomas Derek, Hekkert Paul

2022

artificial intelligence, community wellbeing, cybernetics, feedback loop, human values, human-centered design, wellbeing economy

General General

An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.

In Applied soft computing

Coronavirus Disease-2019 (COVID-19) causes Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and has opened several challenges for research concerning diagnosis and treatment. Chest X-rays and computed tomography (CT) scans are effective and fast alternatives to detect and assess the damage that COVID causes to the lungs at different stages of the disease. Although the CT scan is an accurate exam, the chest X-ray is still helpful due to the cheaper, faster, lower radiation exposure, and is available in low-incoming countries. Computer-aided diagnostic systems based on Artificial Intelligence (AI) and computer vision are an alternative to extract features from X-ray images, providing an accurate COVID-19 diagnosis. However, specialized and expensive computational resources come across as challenging. Also, it needs to be better understood how low-cost devices and smartphones can hold AI models to predict diseases timely. Even using deep learning to support image-based medical diagnosis, challenges still need to be addressed once the known techniques use centralized intelligence on high-performance servers, making it difficult to embed these models in low-cost devices. This paper sheds light on these questions by proposing the Artificial Intelligence as a Service Architecture (AIaaS), a hybrid AI support operation, both centralized and distributed, with the purpose of enabling the embedding of already-trained models on low-cost devices or smartphones. We demonstrated the suitability of our architecture through a case study of COVID-19 diagnosis using a low-cost device. Among the main findings of this paper, we point out the performance evaluation of low-cost devices to handle COVID-19 predicting tasks timely and accurately and the quantitative performance evaluation of CNN models embodiment on low-cost devices.

Rodrigues Moreira Larissa Ferreira, Moreira Rodrigo, Travençolo Bruno Augusto Nassif, Backes André Ricardo

2023-Feb

Artificial intelligence, COVID-19, Convolutional neural network, Embedded, Low-cost device

General General

Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus.

In Biochip journal

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.

Lee Young Suh, Choi Ji Wook, Kang Taewook, Chung Bong Geun

2023-Jan-17

Deep learning, GMM clustering, Image processing, Mask R-CNN, ddPCR

General General

Machine learning sentiment analysis, COVID-19 news and stock market reactions.

In Research in international business and finance

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.

Costola Michele, Hinz Oliver, Nofer Michael, Pelizzon Loriana

2023-Jan

COVID-19 news, Sentiment analysis, Stock markets

General General

A review of advanced technologies available to improve the healthcare performance during COVID-19 pandemic.

In Procedia computer science

Information technology (IT) has enabled the initiation of an innovative healthcare system. An innovative healthcare system integrates new technologies such as cloud computing, the internet of things, and artificial intelligence (AI), to transform the healthcare to be more efficient, more convenient and more personalized. This review aims to identify the key technologies that will help to support an innovative healthcare system. A case study approach was used in this research analysis to enable a researcher to closely analyze the data in a particular context. It presents a case study of the coronavirus (COVID-19) as a means of exploring the use of advanced technologies in an innovative healthcare system to help address a worldwide health crisis. An innovative healthcare system can help to promote better patient self-management, reduce costs, relieve staff pressures, help with resource and knowledge management, and improve the patient experience. An innovative healthcare system can reduce the expense and time for research, and increase the overall efficacy of the research. Overall, this research identifies how innovative technologies can improve the performance of the healthcare system. Advanced technologies can assist with pandemic control and can help in the recognition of the virus, clinical treatment, medical protection, intelligent diagnosis, and outbreak analysis. The review provides an analysis of the future prospects of an innovative healthcare system.

Ali Omar, AlAhmad Ahmad, Kahtan Hasan

2023

Artificial Intelligence, COVID-19, Cloud Computing, Healthcare, Informatization, Internet of Things

General General

Towards precision medicine: Omics approach for COVID-19.

In Biosafety and health

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Cen Xiaoping, Wang Fengao, Huang Xinhe, Jovic Dragomirka, Dubee Fred, Yang Huanming, Li Yixue

2023-Jan-18

COVID-19, artificial intelligence, multi-omics, precision medicine

General General

Speech phoneme and spectral smearing based non-invasive COVID-19 detection.

In Frontiers in artificial intelligence

COVID-19 is a deadly viral infection that mainly affects the nasopharyngeal and oropharyngeal cavities before the lung in the human body. Early detection followed by immediate treatment can potentially reduce lung invasion and decrease fatality. Recently, several COVID-19 detections methods have been proposed using cough and breath sounds. However, very little study has been done on the use of phoneme analysis and the smearing of the audio signal in COVID-19 detection. In this paper, this problem has been addressed and the classification of speech samples has been carried out in COVID-19-positive and healthy audio samples. Additionally, the grouping of the phonemes based on reference classification accuracies have been proposed for effectiveness and faster detection of the disease at a primary stage. The Mel and Gammatone Cepstral coefficients and their derivatives are used as the features for five standard machine learning-based classifiers. It is observed that the generalized additive model provides the highest accuracy of 97.22% for the phoneme grouping "/t//r//n//g//l/." This smearing-based phoneme classification technique can also be used in the future to classify other speech-related disease detections.

Mishra Soumya, Dash Tusar Kanti, Panda Ganapati

2022

COVID-19, COVID-19 detection, machine learning, phoneme analysis, spectral smearing

General General

Curious thing, an artificial intelligence (AI)-based conversational agent for COVID-19 patient management.

In Australian journal of primary health ; h5-index 18.0

There are no clear guidelines or validated models for artificial intelligence (AI)-based approaches in the monitoring of coronavirus disease 2019 (COVID-19) patients who were isolated in the community, in order to identify early deterioration of their health symptoms. Developed in partnership with Curious Thing (CT), a Sydney-based AI conversational technology, a new care robot technology was introduced in South Western Sydney (SWS) in September 2021 to manage the large numbers of low-to-medium risk patients with a COVID-19 diagnosis and who were isolating at home. The CT interface made contact with patients via their mobile phone, following a locally produced script to obtain information recording physical condition, wellness and support. The care robot has engaged over 6323 patients between 2 September to 14 December 2021. The AI-assisted phone calls effectively identified the patients requiring further support, saved clinician time by monitoring less ailing patients remotely, and enabled them to spend more time on critically ill patients, thus ensuring that service and supply resources could be directed to those at greatest need. Engagement strategies had ensured stakeholders support of this technology to meet clinical and welfare needs of the identified patient group. Feedback from both the patients and healthcare staff was positive and had informed the ongoing formulation of a more patient-centred model of virtual care.

Chow Josephine Sau Fan, Blight Victoria, Brown Marian, Glynn Vanessa, Lane Brian, Larkin Amanda, Marshall Sonia, Matthews Prue, Rowles Mick, Warner Bradley

2023-Jan-23

Cardiology Cardiology

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients.

In Clinics and research in hepatology and gastroenterology

INTRODUCTION : Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses.

METHODS : We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset.

RESULTS : 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified.

CONCLUSIONS : We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.

Ho Kai Man Alexander, Rosenfeld Avi, Hogan Áine, McBain Hazel, Duku Margaret, Wolfson Paul Bd, Wilson Ashley, Cheung Sharon My, Hennelly Laura, Macabodbod Lester, Graham David G, Sehgal Vinay, Banerjee Amitava, Lovat Laurence B

2023-Jan-17

General General

Mosaic RBD nanoparticles induce intergenus cross-reactive antibodies and protect against SARS-CoV-2 challenge.

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

Recurrent spillovers of α- and β-coronaviruses (CoV) such as severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome-CoV, SARS-CoV-2, and possibly human CoV have caused serious morbidity and mortality worldwide. In this study, six receptor-binding domains (RBDs) derived from α- and β-CoV that are considered to have originated from animals and cross-infected humans were linked to a heterotrimeric scaffold, proliferating cell nuclear antigen (PCNA) subunits, PCNA1, PCNA2, and PCNA3. They assemble to create a stable mosaic multivalent nanoparticle, 6RBD-np, displaying a ring-shaped disk with six protruding antigens, like jewels in a crown. Prime-boost immunizations with 6RBD-np in mice induced significantly high Ab titers against RBD antigens derived from α- and β-CoV and increased interferon (IFN-γ) production, with full protection against the SARS-CoV-2 wild type and Delta challenges. The mosaic 6RBD-np has the potential to induce intergenus cross-reactivity and to be developed as a pan-CoV vaccine against future CoV spillovers.

Lee Dan Bi, Kim Hyojin, Jeong Ju Hwan, Jang Ui Soon, Jang Yuyeon, Roh Seokbeom, Jeon Hyunbum, Kim Eun Jeong, Han Su Yeon, Maeng Jin Young, Magez Stefan, Radwanska Magdalena, Mun Ji Young, Jun Hyun Sik, Lee Gyudo, Song Min-Suk, Lee Hye-Ra, Chung Mi Sook, Baek Yun Hee, Kim Kyung Hyun

2023-Jan-24

SARS-CoV-2, immune response, mosaic multivalent antigens, receptor-binding domain, spike

General General

Multidimensional machine learning on 2173 COVID-19 patients in Vietnam: Retro-prospective Validation Study.

In JMIR formative research

BACKGROUND : Machine learning (ML) is a part of the Artificial Intelligence strategy. Its algorithms are imputed on Big Data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address the problem. New diseases like Sars-Cov2 are important data stores for machine learning. Therefore, all relevant parameters should be explicitly quantified and modeled.

OBJECTIVE : The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients.

METHODS : The machine learning study population consisted of 2173 patients (1587 mild and non-symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. Two correlation test, relative risk and risk ration were used to eliminate the unbalance parameter and select also the most remarkable ones. HCA, K-means are two independent methods to classify the parameters following their R scores. Finally, Network analysis step give the view in three dimension, more complete of the results above.

RESULTS : The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their significant risk ratio (P<.00001) from the meta-analysis, respectively, are: 4.19 [3.58-4.95], 3.29 [2.76-3.92,] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (P<.00001), 0.34 [0.2743;0.4210]. The significant inversion of correlation between the group of severity shows the important remark. ALT - Leucocytes show the strong negative link (R=-1, P<.00001) in the mild group to the significant positive correlation in the moderate group (R=1, P<.00001). Transferrin-anion Chloride has an positive association (R=1, P<.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, P<.00001). The clustering and network analysis visualize that the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils.

CONCLUSIONS : Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the part of the related preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management.

Nguyen Tue Trong, Ho Tu Cam, Bui Huong Thi Thu, Ho Lam Khanh, Ta Van Thanh

2023-Jan-18

Public Health Public Health

Impact of the COVID-19 pandemic and corresponding control measures on long-term care facilities: a systematic review and meta-analysis.

In Age and ageing ; h5-index 55.0

BACKGROUND : Long-term care facilities (LTCFs) were high-risk settings for COVID-19 outbreaks.

OBJECTIVE : To assess the impacts of the COVID-19 pandemic on LTCFs, including rates of infection, hospitalisation, case fatality, and mortality, and to determine the association between control measures and SARS-CoV-2 infection rates in residents and staff.

METHOD : We conducted a systematic search of six databases for articles published between December 2019 and 5 November 2021, and performed meta-analyses and subgroup analyses to identify the impact of COVID-19 on LTCFs and the association between control measures and infection rate.

RESULTS : We included 108 studies from 19 countries. These studies included 1,902,044 residents and 255,498 staff from 81,572 LTCFs, among whom 296,024 residents and 36,807 staff were confirmed SARS-CoV-2 positive. The pooled infection rate was 32.63% (95%CI: 30.29 ~ 34.96%) for residents, whereas it was 10.33% (95%CI: 9.46 ~ 11.21%) for staff. In LTCFs that cancelled visits, new patient admissions, communal dining and group activities, and vaccinations, infection rates in residents and staff were lower than the global rate. We reported the residents' hospitalisation rate to be 29.09% (95%CI: 25.73 ~ 32.46%), with a case-fatality rate of 22.71% (95%CI: 21.31 ~ 24.11%) and mortality rate of 15.81% (95%CI: 14.32 ~ 17.30%). Significant publication biases were observed in the residents' case-fatality rate and the staff infection rate, but not in the infection, hospitalisation, or mortality rate of residents.

CONCLUSION : SARS-CoV-2 infection rates would be very high among LTCF residents and staff without appropriate control measures. Cancelling visits, communal dining and group activities, restricting new admissions, and increasing vaccination would significantly reduce the infection rates.

Zhang Jun, Yu Yushan, Petrovic Mirko, Pei Xiaomei, Tian Qing-Bao, Zhang Lei, Zhang Wei-Hong

2023-Jan-08

COVID-19, control measures, long-term care facilities, older people, systematic review

Radiology Radiology

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study.

In PloS one ; h5-index 176.0

BACKGROUND : Coronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and identify risk factors associated with chest computed tomography (CT) lesion severity.

METHODS : A prospective cohort study of COVID-19 patients admitted to a tertiary hospital ICU in Brazil (March-August/2020), and followed-up six-twelve months after hospital admission. Initial assessment included: modified Medical Research Council dyspnea scale, SpO2 evaluation, forced vital capacity, and chest X-Ray. Patients with alterations in at least one of these examinations were eligible for CT and pulmonary function tests (PFTs) approximately 16 months after hospital admission. Primary outcome: CT lesion severity (fibrotic-like or non-fibrotic-like). Baseline clinical variables were used to build a machine learning model (ML) to predict the severity of CT lesion.

RESULTS : In total, 326 patients (72%) were eligible for CT and PFTs. COVID-19 CT lesions were identified in 81.8% of patients, and half of them showed mild restrictive lung impairment and impaired lung diffusion capacity. Patients with COVID-19 CT findings were stratified into two categories of lesion severity: non-fibrotic-like (50.8%-ground-glass opacities/reticulations) and fibrotic-like (49.2%-traction bronchiectasis/architectural distortion). No association between CT feature severity and altered lung diffusion or functional restrictive/obstructive patterns was found. The ML detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity(sensitivity,0.78±0.02;specificity,0.79±0.01;F1-score,0.78±0.02;positive predictive rate,0.78±0.02; accuracy,0.78±0.02; and area under the curve,0.83±0.01).

CONCLUSION : ICU hospitalization due to COVID-19 led to respiratory system alterations six-twelve months after hospital admission. Male sex and critical disease acute phase, characterized by a longer ICU and IMV period, and need for tracheostomy and vasoactive drugs, were risk factors for severe CT lesions six-twelve months after hospital admission.

Ribeiro Carvalho Carlos Roberto, Lamas Celina Almeida, Chate Rodrigo Caruso, Salge João Marcos, Sawamura Marcio Valente Yamada, de Albuquerque André L P, Toufen Junior Carlos, Lima Daniel Mario, Garcia Michelle Louvaes, Scudeller Paula Gobi, Nomura Cesar Higa, Gutierrez Marco Antonio, Baldi Bruno Guedes

2023

General General

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic.

In PloS one ; h5-index 176.0

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Chen Tongxin, Zhu Di, Cheng Tao, Gao Xiaowei, Chen Huanfa

2023

Public Health Public Health

COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.

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

In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.

Gomes Juliana Carneiro, de Santana Maíra Araújo, Masood Aras Ismael, de Lima Clarisse Lins, Dos Santos Wellington Pinheiro

2023-Jan-20

COVID-19 clinical diagnosis, COVID-19 computer-aided diagnosis, Deep learning, Electrocardiography, Hybrid deep architectures

General General

Social Media Devices' Influence on User Neck Pain during the COVID-19 Pandemic: Collaborating Vertebral-GLCM Extracted Features with a Decision Tree.

In Journal of imaging

The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being-in some cases-the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users' ages and the duration of the SM usage (H.mean) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.

Al-Naami Bassam, Badr Bashar E A, Rawash Yahia Z, Owida Hamza Abu, De Fazio Roberto, Visconti Paolo

2023-Jan-08

GLCM, decision tree algorithm, neck pain, smartphones, social media usage

General General

A Survey on Deep Learning in COVID-19 Diagnosis.

In Journal of imaging

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

Han Xue, Hu Zuojin, Wang Shuihua, Zhang Yudong

2022-Dec-20

COVID-19, CT images, X-ray images, classification, convolutional neural networks, deep learning, diagnosis, transfer learning

Public Health Public Health

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

In Communications biology

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Park Jonathan J, Lee Kyoung A V, Lam Stanley Z, Moon Katherine S, Fang Zhenhao, Chen Sidi

2023-Jan-20

General General

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

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

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

Yıldırım Emre, Cicioğlu Murtaza, Çalhan Ali

2023-Jan-21

Cloud computing, Data analytics, Fog computing, IoMT, Machine learning, WBANs

Public Health Public Health

Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa.

In BMC public health ; h5-index 82.0

OBJECTIVE : Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries.

METHODS : Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate β in the next 7, 14, and 21 days. Then, the predicted transmission rate βs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE).

RESULTS : The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted β and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 106, with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively.

CONCLUSION : Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing β respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response.

Liu Xu-Dong, Wang Wei, Yang Yi, Hou Bo-Han, Olasehinde Toba Stephen, Feng Ning, Dong Xiao-Ping

2023-Jan-19

ARIMA, COVID-19, Epidemic, Functionalized β, Machine learning, Nested model, SIRV model

Public Health Public Health

Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts.

In Scientific reports ; h5-index 158.0

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected.

Wichmann Roberta Moreira, Fernandes Fernando Timoteo, Chiavegatto Filho Alexandre Dias Porto

2023-Jan-19

General General

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.

In Pathology, research and practice

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Jeyananthan Pratheeba

2023-Jan-15

COVID-19 molecular data, Classification algorithm, Feature selection, Severity prediction, Treatment stage, lncRNA, miRNA and mRNA

General General

Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location.

In Journal of computational biology : a journal of computational molecular cell biology

With the rapid spread of COVID-19 worldwide, viral genomic data are available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a unique opportunity for analysis toward the research of effective vaccine development for current pandemics, and avoiding or mitigating future pandemics. One piece of information that comes with every such viral sequence is the geographical location where it was collected-the patterns found between viral variants and geographical location surely being an important part of this analysis. One major challenge that researchers face is processing such huge, highly dimensional data to obtain useful insights as quickly as possible. Most of the existing methods face scalability issues when dealing with the magnitude of such data. In this article, we propose an approach that first computes a numerical representation of the spike protein sequence of SARS-CoV-2 using k-mers (substrings) and then uses several machine learning models to classify the sequences based on geographical location. We show that our proposed model significantly outperforms the baselines. We also show the importance of different amino acids in the spike sequences by computing the information gain corresponding to the true class labels.

Ali Sarwan, Bello Babatunde, Tayebi Zahra, Patterson Murray

2023-Jan-19

COVID-19, SARS-CoV-2, geographical location, k-mers, sequence classification

General General

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19.

In Mathematical biosciences and engineering : MBE

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.

Singh Akansha, Singh Krishna Kant, Greguš Michal, Izonin Ivan

2022-Aug-26

** COVID-19 , Grasshopper Optimization , deep learning , diagnosis , machine learning **

Public Health Public Health

Prehospital Cardiac Arrest should be considered when evaluating Covid-19 mortality in the United States.

In Methods of information in medicine

BACKGROUND : Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is high value. Covid-19 offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES : This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the Covid-19 emergency in the United States. Here fitness for use means the statistical agreement between events across series.

METHODS : 13 weekly clinical event series from before and during the Covid-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) Covid-19 attributable mortality, CDC's excess mortality model, national Emergency Medical System (EMS) calls and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to distributed random forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS : Model r2 statistics ranged from .78 to .99 for the share of the volumes predicted correctly. Prehospital EMS data was high value and cardiac arrest prior to EMS arrival was on average the best predictor (tied with study week). Covid-19 Medicare claims volumes can predict Covid-19 death certificates (agreement) while generic viral respiratory Medicare claim volumes cannot predict Medicare Covid-19 claims (disagreement).

CONCLUSIONS : Prehospital EMS data should be considered when evaluating the severity of Covid-19 because prehospital cardiac arrest known to EMS was the strongest predictor on average across indices. Key Words Random Forest Covid-19 Public Health Statistical methods Syndromic Surveillance   1.Introduction Creating long term, multi-source, national surveillance data services for emerging disease response is a complex topic which Covid-19 has given new importance1-5. Public health emergencies seldom leave surplus time or resources to stand up novel methods and respond; further essentializing (specific) disease preparedness6-8. More often than not epidemic response is managed using preexisting data services, often legacy data series from yesteryear's epidemics9-11. Epidemic preparedness in the United States is generally weak; and the Covid-19 response is largely drawn from preexisting pan-flu emergency plans12,13. During a public health emergency, the clinical knowledge needed to respond is developed by case surveillance drawn from preexisting data series. Covid-19 has presented an unusual opportunity to evaluate agreement across surveillance efforts within the United States. The ability to detect clinical findings from surveillance nets and epidemiology methods which were not necessarily designed to detect them in meaningful ways is high priority for the future management of emerging infectious diseases. Strikingly the difference in Covid-19 mortality for SARS impacted countries (China, South Korea, Australia) vs. the United States may come down to what emergency response plan was last implemented (SARS vs. Swine Flu) and the fitness of surveillance (case specific vs general population) rather than deeper cultural, economic, or racial differences, as have been proposed in popular media14-20. 2.Objectives In this study public health surveillance data is processed using a machine learning approach to discover the relative agreement of a surveillance event series when predicting surveillance event series. Towards objectives this study seeks to assess the agreement between event series and contrast the value of traditional surveillance methods (death certificates, influenza and respiratory infection claims volumes) with non-traditional sources such as national Emergency Medical Services (EMS) call volume data in the Covid-19 era in the United States. 3.Methods 3.1 Statistic of Interest Variable importance is the statistic of interest in this study. Variable importance means that when predicting the dependent variable, an independent variable which is of comparatively higher predictive value (association) than another is of higher (predictive) use value. When considering high variable importance with weekly event series data, series which help the machine learning models learn, predict or guess the correct dependent weekly event series could be co-occurring or mutually observed events. The high variable importance scores from different sources suggests that series are observing the same real world event across surveillance efforts as they support prediction better than noise and other candidate series (other independent variables). Of special interest are 'high variable importance, independent variables' from a different data source than the dependent variable. High same source variables are most likely high in value because they are similarly distributed across study weeks to their parent-sister series and in turn are not necessarily interesting. A series of events can be said to have 'agreement value' if it has high statistical agreement with other series from a different source. Low statistical agreement suggests 'out of era' events, or events which are not driven by the same causes as other series considered here. Towards noise and disagreement, influenza and respiratory infection claims volumes are considered below with Covid-19 claims volumes. Claims volumes are traditionally used in influenza surveillance. As a test of the efficacy of the models described here, Covid-19 volumes should be able to 'out perform' influenza volumes as the Covid-19 era is largely understood to be influenza sparse. In this way respiratory and influenza events could be understood as a control arm as well as a model output of independent interest.

Williams Nick

2023-Jan-18

Public Health Public Health

Evolution of social mood in Spain throughout the COVID-19 vaccination process: a machine learning approach to tweets analysis.

In Public health

OBJECTIVES : This paper presents a new approach based on the combination of machine learning techniques, in particular, sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain.

METHODS : Analysing 41,669 Spanish tweets posted between 27 February 2020 and 31 December 2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined using several descriptive statistics; a trajectory plot representing the emotional valence vs narrative time was also included.

RESULTS : The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process.

CONCLUSIONS : The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.

Turón A, Altuzarra A, Moreno-Jiménez J M, Navarro J

2022-Dec-14

COVID-19 vaccination process, Machine learning, Multivariate statistics, Sentiment analysis, Social mood, Tweets

General General

Estimating Remaining Lifespan from the Face

ArXiv Preprint

The face is a rich source of information that can be utilized to infer a person's biological age, sex, phenotype, genetic defects, and health status. All of these factors are relevant for predicting an individual's remaining lifespan. In this study, we collected a dataset of over 24,000 images (from Wikidata/Wikipedia) of individuals who died of natural causes, along with the number of years between when the image was taken and when the person passed away. We made this dataset publicly available. We fine-tuned multiple Convolutional Neural Network (CNN) models on this data, at best achieving a mean absolute error of 8.3 years in the validation data using VGGFace. However, the model's performance diminishes when the person was younger at the time of the image. To demonstrate the potential applications of our remaining lifespan model, we present examples of using it to estimate the average loss of life (in years) due to the COVID-19 pandemic and to predict the increase in life expectancy that might result from a health intervention such as weight loss. Additionally, we discuss the ethical considerations associated with such models.

Amir Fekrazad

2023-01-19

Public Health Public Health

Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States.

In Science advances

Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods-tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States-frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number Rt becomes larger than 1 for a period of 2 weeks.

Stolerman Lucas M, Clemente Leonardo, Poirier Canelle, Parag Kris V, Majumder Atreyee, Masyn Serge, Resch Bernd, Santillana Mauricio

2023-Jan-18

General General

Diffusion-based Conditional ECG Generation with Structured State Space Models

ArXiv Preprint

Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.

Juan Miguel Lopez Alcaraz, Nils Strodthoff

2023-01-19

Radiology Radiology

Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI).

In European radiology ; h5-index 62.0

OBJECTIVES : Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report the development of the International Consortium for COVID-19 Imaging AI (ICOVAI) model and perform independent external validation.

METHODS : The ICOVAI model was developed using multicenter data (n = 1286 CT scans) to quantify disease extent and assess COVID-19 likelihood using the COVID-19 Reporting and Data System (CO-RADS). A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. CO-RADS classification performance was calculated using linearly weighted Cohen's kappa and segmentation performance using Dice Similarity Coefficient (DSC).

RESULTS : Regarding internal versus external testing, segmentation performance of lung contours was equally excellent (DSC = 0.97 vs. DSC = 0.97, p = 0.97). Lung opacities segmentation performance was adequate internally (DSC = 0.76), but significantly worse on external validation (DSC = 0.59, p < 0.0001). For CO-RADS classification, agreement with radiologists on the internal set was substantial (kappa = 0.78), but significantly lower on the external set (kappa = 0.62, p < 0.0001).

CONCLUSION : In this multicenter study, a model developed for CO-RADS score prediction and quantification of COVID-19 disease extent was found to have a significant reduction in performance on independent external validation versus internal testing. The limited reproducibility of the model restricted its potential for clinical use. The study demonstrates the importance of independent external validation of AI models.

KEY POINTS : • The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.

Topff Laurens, Groot Lipman Kevin B W, Guffens Frederic, Wittenberg Rianne, Bartels-Rutten Annemarieke, van Veenendaal Gerben, Hess Mirco, Lamerigts Kay, Wakkie Joris, Ranschaert Erik, Trebeschi Stefano, Visser Jacob J, Beets-Tan Regina G H

2023-Jan-18

Artificial intelligence, COVID-19, Computed tomography, Reproducibility of results, Validation study

General General

Improving Food Detection For Images From a Wearable Egocentric Camera

ArXiv Preprint

Diet is an important aspect of our health. Good dietary habits can contribute to the prevention of many diseases and improve the overall quality of life. To better understand the relationship between diet and health, image-based dietary assessment systems have been developed to collect dietary information. We introduce the Automatic Ingestion Monitor (AIM), a device that can be attached to one's eye glasses. It provides an automated hands-free approach to capture eating scene images. While AIM has several advantages, images captured by the AIM are sometimes blurry. Blurry images can significantly degrade the performance of food image analysis such as food detection. In this paper, we propose an approach to pre-process images collected by the AIM imaging sensor by rejecting extremely blurry images to improve the performance of food detection.

Yue Han, Sri Kalyan Yarlagadda, Tonmoy Ghosh, Fengqing Zhu, Edward Sazonov, Edward J. Delp

2023-01-19

General General

Pandemic disease detection through wireless communication using infrared image based on deep learning.

In Mathematical biosciences and engineering : MBE

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing method, and there is a high chance of a false-negative ratio (FNR). Moreover, there arises a deficiency of R.T.-PCR test kits. Therefore, alternative procedures for a quick and accurate diagnosis of patients are urgently needed to deal with these pandemics. The infrared image is self-sufficient for detecting these diseases by measuring the temperature at the initial stage. C.T. scans and other pathological tests are valuable aspects of evaluating a patient with a suspected pandemic infection. However, a patient's radiological findings may not be identified initially. Therefore, we have included an Artificial Intelligence (A.I.) algorithm-based Machine Intelligence (MI) system in this proposal to combine C.T. scan findings with all other tests, symptoms, and history to quickly diagnose a patient with a positive symptom of current and future pandemic diseases. Initially, the system will collect information by an infrared camera of the patient's facial regions to measure temperature, keep it as a record, and complete further actions. We divided the face into eight classes and twelve regions for temperature measurement. A database named patient-info-mask is maintained. While collecting sample data, we incorporate a wireless network using a cloudlets server to make processing more accessible with minimal infrastructure. The system will use deep learning approaches. We propose convolution neural networks (CNN) to cross-verify the collected data. For better results, we incorporated tenfold cross-verification into the synthesis method. As a result, our new way of estimating became more accurate and efficient. We achieved 3.29% greater accuracy by incorporating the "decision tree level synthesis method" and "ten-folded-validation method". It proves the robustness of our proposed method.

Alhameed Mohammed, Jeribi Fathe, Elnaim Bushra Mohamed Elamin, Hossain Mohammad Alamgir, Abdelhag Mohammed Eltahir

2023-Jan

** convolution neural networks , deep learning , infrared image , machine intelligence , ten-folded-validation method **

General General

Causal conditional hidden Markov model for multimodal traffic prediction

ArXiv Preprint

Multimodal traffic flow can reflect the health of the transportation system, and its prediction is crucial to urban traffic management. Recent works overemphasize spatio-temporal correlations of traffic flow, ignoring the physical concepts that lead to the generation of observations and their causal relationship. Spatio-temporal correlations are considered unstable under the influence of different conditions, and spurious correlations may exist in observations. In this paper, we analyze the physical concepts affecting the generation of multimode traffic flow from the perspective of the observation generation principle and propose a Causal Conditional Hidden Markov Model (CCHMM) to predict multimodal traffic flow. In the latent variables inference stage, a posterior network disentangles the causal representations of the concepts of interest from conditional information and observations, and a causal propagation module mines their causal relationship. In the data generation stage, a prior network samples the causal latent variables from the prior distribution and feeds them into the generator to generate multimodal traffic flow. We use a mutually supervised training method for the prior and posterior to enhance the identifiability of the model. Experiments on real-world datasets show that CCHMM can effectively disentangle causal representations of concepts of interest and identify causality, and accurately predict multimodal traffic flow.

Yu Zhao, Pan Deng, Junting Liu, Xiaofeng Jia, Mulan Wang

2023-01-19

General General

On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data.

In Mathematical biosciences and engineering : MBE

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.

Zhou Yinghui, Ahmad Zubair, Almaspoor Zahra, Khan Faridoon, Tag-Eldin Elsayed, Iqbal Zahoor, El-Morshedy Mahmoud

2023-Jan

** family of distributions , healthcare sector , machine learning algorithms , mathematical properties , simulation , statistical modeling **

General General

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature: a retrospective study

bioRxiv Preprint

Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19 related publications to help scale-up the epidemiological curation process. Methods: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6,365 publications manually classified into two classes, three subclasses and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.

Knafou, J.; Haas, Q.; Borissov, N.; Counotte, M. J.; Low, N.; Imeri, H.; Ipekci, A. M.; Buitrago-Garcia, D.; Heron, L.; Amini, P.; Teodoro, D.

2023-01-19

General General

Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits

ArXiv Preprint

Restless multi-arm bandits (RMABs) is a popular decision-theoretic framework that has been used to model real-world sequential decision making problems in public health, wildlife conservation, communication systems, and beyond. Deployed RMAB systems typically operate in two stages: the first predicts the unknown parameters defining the RMAB instance, and the second employs an optimization algorithm to solve the constructed RMAB instance. In this work we provide and analyze the results from a first-of-its-kind deployment of an RMAB system in public health domain, aimed at improving maternal and child health. Our analysis is focused towards understanding the relationship between prediction accuracy and overall performance of deployed RMAB systems. This is crucial for determining the value of investing in improving predictive accuracy towards improving the final system performance, and is useful for diagnosing, monitoring deployed RMAB systems. Using real-world data from our deployed RMAB system, we demonstrate that an improvement in overall prediction accuracy may even be accompanied by a degradation in the performance of RMAB system -- a broad investment of resources to improve overall prediction accuracy may not yield expected results. Following this, we develop decision-focused evaluation metrics to evaluate the predictive component and show that it is better at explaining (both empirically and theoretically) the overall performance of a deployed RMAB system.

Paritosh Verma, Shresth Verma, Aditya Mate, Aparna Taneja, Milind Tambe

2023-01-19

General General

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

In PloS one ; h5-index 176.0

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

Khan Asad, Akram Muhammad Usman, Nazir Sajid

2023

Public Health Public Health

Applications of social media and digital technology in COVID-19 vaccination: a scoping review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Social media and digital technologies have played an essential role in disseminating information and promoting vaccination during the COVID-19 pandemic. It needs to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE : To synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS : We searched six databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccine. Articles were included if they provide original descriptions on applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstract, editorial, letter, commentary, correspondence, study protocol, and review were excluded. A modified version of the Appraisal tool for Cross-Sectional Studies was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS : A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most applied, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media seriously affect public attitudes toward COVID-19 vaccine and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and post-vaccination surveillance. The applied digital technologies included algorithms, blockchain, mHealth, IoT, and other technologies, although with some barriers to their popularization.

CONCLUSIONS : The application of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

CLINICALTRIAL : None.

Zang Shujie, Zhang Xu, Xing Yuting, Chen Jiaxian, Lin Leesa, Hou Zhiyuan

2023-Jan-13

Public Health Public Health

Technology-Enabled Collaborative Care for Concurrent Diabetes and Distress Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Feasibility Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The COVID-19 pandemic disrupted the delivery of diabetes care and worsened mental health among many patients with type 2 diabetes (T2D). This disruption puts patients with T2D at risk for poor diabetes outcomes, especially those who experience social disadvantage due to socioeconomic class, rurality, or ethnicity. The appropriate use of communication technology could reduce these gaps in diabetes care created by the pandemic and also provide support for psychological distress.

OBJECTIVE : The purpose of this study is to test the feasibility of an innovative co-designed Technology-Enabled Collaborative Care (TECC) model for diabetes management and mental health support among adults with T2D.

METHODS : We will recruit 30 adults with T2D residing in Ontario, Canada, to participate in our sequential explanatory mixed methods study. They will participate in 8 weekly web-based health coaching sessions with a registered nurse, who is a certified diabetes educator, who will be supported by a digital care team (ie, a peer mentor, an addictions specialist, a dietitian, a psychiatrist, and a psychotherapist). Assessments will be completed at baseline, 4 weeks, and 8 weeks, with a 12-week follow-up. Our primary outcome is the feasibility and acceptability of the intervention, as evident by the participant recruitment and retention rates. Key secondary outcomes include assessment completion and delivery of the intervention. Exploratory outcomes consist of changes in mental health, substance use, and physical health behaviors. Stakeholder experience and satisfaction will be explored through a qualitative descriptive study using one-on-one interviews.

RESULTS : This paper describes the protocol of the study. The recruitment commenced in June 2021. This study was registered on October 29, 2020, on ClinicalTrials.gov (Registry ID: NCT04607915). As of June 2022, all participants have been recruited. It is anticipated that data analysis will be complete by the end of 2022, with study findings available by the end of 2023.

CONCLUSIONS : The development of an innovative, technology-enabled model will provide necessary support for individuals living with T2D and mental health challenges. This TECC program will determine the feasibility of TECC for patients with T2D and mental health issues.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04607915; https://clinicaltrials.gov/ct2/show/NCT04607915.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : DERR1-10.2196/39724.

Vojtila Lenka, Sherifali Diana, Dragonetti Rosa, Ashfaq Iqra, Veldhuizen Scott, Naeem Farooq, Agarwal Sri Mahavir, Melamed Osnat C, Crawford Allison, Gerretsen Philip, Hahn Margaret, Hill Sean, Kidd Sean, Mulsant Benoit, Serhal Eva, Tackaberry-Giddens Leah, Whitmore Carly, Marttila Jennifer, Tang Frank, Ramdass Seeta, Lourido Gloria, Sockalingam Sanjeev, Selby Peter

2023-Jan-17

coaching, collaborative care, diabetes, diabetic, digital health, eHealth, feasibility, health outcome, mental health, nurse, nursing, patient education, qualitative, satisfaction, substance use, technology, type 2 diabetes mellitus, virtual care

General General

Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer's disease phenotypes and Covid-19 severity.

In Human molecular genetics ; h5-index 81.0

Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (ad) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk SNPs for ad and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to ad, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in ad. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of ad-Covid genes using our networks involving known ad and Covid-19 genes. Our machine learning analysis prioritized 36 ad-Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and ad. Finally, we mapped GWAS SNPs of ad and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an ad-Covid functional genomic resource at the brain-region level.

Khullar Saniya, Wang Daifeng

2023-Jan-16

General General

Towards an ML-Based Semantic IoT for Pandemic Management: A Survey of Enabling Technologies for COVID-19.

In Neurocomputing

The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.

Zgheib Rita, Chahbandarian Ghazar, Kamalov Firuz, Messiry Haythem El, Al-Gindy Ahmed

2023-Jan-12

COVID-19, Cloud architecture, Internet of Things, Machine Learning, Ontologies, Survey

General General

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

In The international journal of high performance computing applications

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Dommer Abigail, Casalino Lorenzo, Kearns Fiona, Rosenfeld Mia, Wauer Nicholas, Ahn Surl-Hee, Russo John, Oliveira Sofia, Morris Clare, Bogetti Anthony, Trifan Anda, Brace Alexander, Sztain Terra, Clyde Austin, Ma Heng, Chennubhotla Chakra, Lee Hyungro, Turilli Matteo, Khalid Syma, Tamayo-Mendoza Teresa, Welborn Matthew, Christensen Anders, Smith Daniel Ga, Qiao Zhuoran, Sirumalla Sai K, O’Connor Michael, Manby Frederick, Anandkumar Anima, Hardy David, Phillips James, Stern Abraham, Romero Josh, Clark David, Dorrell Mitchell, Maiden Tom, Huang Lei, McCalpin John, Woods Christopher, Gray Alan, Williams Matt, Barker Bryan, Rajapaksha Harinda, Pitts Richard, Gibbs Tom, Stone John, Zuckerman Daniel M, Mulholland Adrian J, Miller Thomas, Jha Shantenu, Ramanathan Arvind, Chong Lillian, Amaro Rommie E

2023-Jan

AI, COVID-19, Delta, GPU, HPC, SARS-CoV-2, aerosols, computational virology, deep learning, molecular dynamics, multiscale simulation, weighted ensemble

General General

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

bioRxiv Preprint

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarised and incorporated into patient outcome prediction models in several ways, however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integration approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using each single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalisation when using multiple datasets as the model input.

Cao, Y.; Ghazanfar, S.; Yang, P.; Yang, J.

2023-01-18

General General

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review.

In JMIR bioinformatics and biotechnology

BACKGROUND : In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques.

OBJECTIVE : In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases.

METHODS : The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement.

RESULTS : Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count.

CONCLUSIONS : Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.

Cardozo Glauco, Tirloni Salvador Francisco, Pereira Moro Antônio Renato, Marques Jefferson Luiz Brum

2022

COVID-19, diagnosis, laboratory tests, machine learning, prediction, review

Public Health Public Health

Association of Neutralizing Anti-spike Monoclonal Antibody Treatment With COVID-19 Hospitalization and Assessment of the Monoclonal Antibody Screening Score.

In Mayo Clinic proceedings. Innovations, quality & outcomes

OBJECTIVE : To test the hypothesis that the MASS Score performs consistently better in identifying need for monoclonal-antibody infusion throughout each "wave" of SARS-CoV-2 variant predominance during the COVID-19 pandemic and the infusion of contemporary monoclonal-antibody treatments is associated with a lower risk of hospitalization.

PATIENTS AND METHODS : In this retrospective cohort study, we evaluated the efficacy of monoclonal-antibody treatment as compared to no monoclonal-antibody treatment in symptomatic adults who tested positive for SARS-CoV-2, regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients diagnosed with COVID-19 from November 19, 2020, through May 12, 2022.

RESULTS : Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal-antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal-antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (e.g., relative risk: 0.15; 95% CI, 0.14 to 0.17).

CONCLUSIONS : Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal-antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic.

Johnson Patrick W, Kunze Katie L, Senefeld Jonathon W, Sinclair Jorge E, Isha Shahin, Satashia Parthkumar H, Bhakta Shivang, Cowart Jennifer B, Bosch Wendelyn, O’Horo Jack, Shah Sadia Z, Wadei Hani M, Edwards Michael A, Pollock Benjamin D, Edwards Alana J, Scheitel-Tulledge Sidna, Clune Caroline G, Hanson Sara N, Arndt Richard, Heyliger Alexander, Kudrna Cory, Bierle Dennis M, Buckmeier Jason R, Seville Maria Teresa A, Orenstein Robert, Libertin Claudia, Ganesh Ravindra, Franco Pablo Moreno, Razonable Raymund R, Carter Rickey E, Sanghavi Devang K, Speicher Leigh L

2023-Jan-11

CMH, Cochran Mantel Haenszel, COVID-19, Coronavirus Disease 2019, GBM, Gradient Boosting Machine, MASS, Monoclonal Antibody Screening Score, SARS-CoV-2, Severe Acute Respiratory Syndrome Corona Virus 2

Public Health Public Health

The race to understand immunopathology in COVID-19: perspectives on the impact of quantitative approaches to understand within-host interactions.

In Immunoinformatics (Amsterdam, Netherlands)

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Gazeau Sonia, Deng Xiaoyan, Ooi Hsu Kiang, Mostefai Fatima, Hussin Julie, Heffernan Jane, Jenner Adrianne L, Craig Morgan

2023-Jan-08

COVID-19, SARS-CoV-2, computational modelling, immunopathology, machine learning, mathematical modelling, population genetics, within-host dynamics

General General

Deep learning-based user experience evaluation in distance learning.

In Cluster computing

The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

Sadigov Rahim, Yıldırım Elif, Kocaçınar Büşra, Patlar Akbulut Fatma, Catal Cagatay

2023-Jan-08

Deep learning, Distance learning, NLP, Sentiment analysis

General General

Bibliometric analysis of top-cited articles in Journal of Dental Sciences.

In Journal of dental sciences

BACKGROUND/PURPOSE : Bibliometric analysis is a method for quantifying the article distribution, impact, and performance. The purpose of this study was to identify the most top-cited articles published in Journal of Dental Sciences (JDS) and further analyze their main characteristics.

MATERIALS AND METHODS : Web of Science, Journal Citation Reports database was searched to retrieve the most-cited articles in JDS published from 2007 to July 31, 2022. Among the included top-cited articles, the following parameters were recorded and analyzed: article title, article type, year, country, number of citations, and average citations pre year. Microsoft Excel was applied for the descriptive bibliometric analysis.

RESULTS : 41 top-cited articles were filtered from total 1165 JDS articles in Web of Science database. The results showed that 41 top-cited articles were cited between 20 and 186 times from Journal Citation Reports. Most of the article types are original article (28/41, 68.29%) following by review article (7/41, 17.07%). The majority of articles were originated from Taiwan (23/41, 56.10%). The top 4 most cited articles were relative to the research topic on COVID-19, lateral canal, guided-tissue regeneration barriers, and platelet-rich fibrin, respectively. However, articles analyzed by the average citations per year since publication were focused on COVID-19 followed by artificial intelligence.

CONCLUSION : This bibliometric analysis illustrates the progress and trend of researches in JDS. The results may also offer a reference for recognizing the hot issues with the most citations in JDS.

Yang Li-Chiu, Liu Fu-Hsuan, Liu Chia-Min, Yu Chuan-Hang, Chang Yu-Chao

2023-Jan

Bibliometric analysis, Citation analysis, Journal of Dental Sciences, Web of Science

General General

A systematic literature review of machine learning application in COVID-19 medical image classification.

In Procedia computer science

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

Daniel Cenggoro, Tjeng Wawan Pardamean

2023

COVID-19, classification, disease, machine learning, medical images

General General

Towards a more general drug target interaction prediction model using transfer learning.

In Procedia computer science

The topic of Drug-Target Interaction (DTI) topic has emerged nowadays since the COVID-19 outbreaks. DTI is one of the stages of finding a new cure for a recent disease. It determines whether a chemical compound would affect a particular protein, known as binding affinity. Recently, significant efforts have been devoted to artificial intelligence (AI) powered DTI. However, the use of transfer learning in DTI has not been explored extensively. This paper aims to make a more general DTI model by investigating DTI prediction method using Transfer learning. Three popular models will be tested and observed: CNN, RNN, and Transformer. Those models combined in several scenarios involving two extensive public datasets on DTI (BindingDB and DAVIS) to find the most optimum architecture. In our finding, combining the CNN model and BindingDB as the source data became the most recommended pre-trained model for real DTI cases. This conclusion was proved with the 6% AUPRC increase after fine-tuning the BindingDB pre-trained model to DAVIS dataset than without pre-training the model first.

Suhartono Derwin, Majiid Muhammad Rizki Nur, Handoyo Alif Tri, Wicaksono Pandu, Lucky Henry

2023

SMILES, deep learning, drug discovery, drug-target interaction, transfer learning

General General

Comparative analysis of deep learning models for detecting face mask.

In Procedia computer science

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-the-art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification.

Ramadhan M Vickya, Muchtar Kahlil, Nurdin Yudha, Oktiana Maulisa, Fitria Maya, Maulina Novi, Elwirehardja Gregorius Natanael, Pardamean Bens

2023

Binary Classification, Deep Learning, Face mask detection

General General

Development clustering system IDX company with k-means algorithm and DBSCAN based on fundamental indicator and ESG.

In Procedia computer science

The global pandemic covid-19 offer buying opportunity to buy business with discounted price. This phenomenon attracts new type of investor around the world. This novice investor may aware that there is indices that is followed as benchmark. This benchmark was used as guidance, however, fact shown that some of this indices constituent fails to adapt and survive during global pandemic. Research indicates that formulation on inclusion and exclusion an index may biased. This novice investor may also be aware of so called blue chips company. However, yesterday winner may become tomorrow losers. This biased classification is done by human. This experiment intentionally to counter this practice, by using cutting edge machine learning to cluster IDX company using K-Means and DBSCAN algorithm. This experiment dataset is using KOMPAS100 fundamental indicator and it's ESG attributes. Experiment result suggesting there is five cluster in terms of fundamental and ESG in KOMPAS100.

Pranata Kevin Surya, Gunawan Alexander A S, Gaol Ford Lumban

2023

DBSCAN, ESG, Fundamental Indicator, IDX, K-Means Clustering, KOMPAS100, Machine Learning

General General

The next generation of evidence-based medicine.

In Nature medicine ; h5-index 170.0

Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation 'deep' medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.

Subbiah Vivek

2023-Jan-16

Surgery Surgery

Perioperative Risk Assessment of Patients Using the MyRISK Digital Score Completed Before the Preanesthetic Consultation: Prospective Observational Study.

In JMIR perioperative medicine

BACKGROUND : The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context.

OBJECTIVE : Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications.

METHODS : This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed.

RESULTS : Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively.

CONCLUSIONS : The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.

Ferré Fabrice, Laurent Rodolphe, Furelau Philippine, Doumard Emmanuel, Ferrier Anne, Bosch Laetitia, Ba Cyndie, Menut Rémi, Kurrek Matt, Geeraerts Thomas, Piau Antoine, Minville Vincent

2023-Jan-16

chatbot, digital health, machine learning, mobile phone, perioperative risk, preanesthetic consultation

General General

Re-envisioning the design of nanomedicines: Harnessing automation and artificial intelligence.

In Expert opinion on drug delivery

INTRODUCTION : Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED : This article highlights the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION : The development of integrated workflows based on automated experiments and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Zaslavsky Jonathan, Bannigan Pauric, Allen Christine

2023-Jan-16

Artificial intelligence, Automation, Drug delivery, Machine learning, Nanomedicine, Pharmaceutical formulation development

General General

Transformer for one stop interpretable cell type annotation.

In Nature communications ; h5-index 260.0

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.

Chen Jiawei, Xu Hao, Tao Wanyu, Chen Zhaoxiong, Zhao Yuxuan, Han Jing-Dong J

2023-Jan-14

Pathology Pathology

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

In Scientific reports ; h5-index 158.0

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Köchl Katharina, Schopper Tobias, Durmaz Vedat, Parigger Lena, Singh Amit, Krassnigg Andreas, Cespugli Marco, Wu Wei, Yang Xiaoli, Zhang Yanchong, Wang Welson Wen-Shang, Selluski Crystal, Zhao Tiehan, Zhang Xin, Bai Caihong, Lin Leon, Hu Yuxiang, Xie Zhiwei, Zhang Zaihui, Yan Jun, Zatloukal Kurt, Gruber Karl, Steinkellner Georg, Gruber Christian C

2023-Jan-14

General General

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

In Computers in biology and medicine

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Wen Cuihong, Liu Shaowu, Liu Shuai, Heidari Ali Asghar, Hijji Mohammad, Zarco Carmen, Muhammad Khan

2022-Nov-22

COVID-19 recognition, Capsule network, Chest CT scan, Deep learning, Feature sampling, Lung infections

General General

Utilisation of deep learning for COVID-19 diagnosis.

In Clinical radiology

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.

Aslani S, Jacob J

2023-Feb

Public Health Public Health

Necessity and challenges for the post-pandemic Hangzhou Asian Games: An interdisciplinary data science assessment.

In Frontiers in psychology ; h5-index 92.0

BACKGROUND : The postponement of the Hangzhou Asian Games has reignited controversy over whether it is necessary and safe to hold. This study aimed to assess its necessity for Asian elite sport and the challenges brought by the COVID-19 pandemic through joint data science research on elite sports and public health Internet big data.

METHODS : For necessity, we used seven pre-pandemic Asian Games to investigate its long-term internal balance and six pre-pandemic Olympic Games to examine its contribution to the external competitiveness of Asian sport powers through bivariate Pearson correlation analyses between sport variables and holding year. For challenges, we used Johns Hopkins COVID-19 data and Tokyo 2020 Olympic data to quantify the past impact of the pandemic on elite sport by another correlation analysis between pandemic variables and the change in the weighted score of medal share (CWSMS), built a transferable linear regression model, transferred the model to Jakarta 2018 Asian Games data, and eventually forecasted the possible impact of the pandemic on the results of the Hangzhou Asian Games.

RESULTS : The proportion of gold medal countries in the Asian Games showed a long-term upward trend (Pearson r (7) = 0.849, p < 0.05), and the share of medals won by Asian countries showed a significant increasing process (Pearson r (6) = 0.901, p < 0.05). The cumulative number of COVID-19 deaths (CND) was most significantly correlated to CWSMS (Pearson r (100) = -0.455, p < 0.001). The total Olympic model output of Asian countries was 0.0115 in Tokyo 2020 and is predicted to be 0.0093 now. The prediction of CWSMS in Hangzhou was 0.0013 for China, 0.0006 for Japan, and 0.0008 for South Korea.

CONCLUSION : We documented that Asian Games played a significant role in the long-term balanced internal structure and the increasing global competitiveness of Asian elite sport. We proved that the COVID-19 pandemic has significantly affected the Olympic performance of countries worldwide, while the competitive performance at the Hangzhou Games would be less affected than the world average level. This study also highlights the importance of interdisciplinary data science research on large-scale sports events and public health.

Guo Jianwei, Zhang Xiangning, Cui Dandan

2022

Asian Games, COVID-19, Olympic Games, elite sport, public health

General General

Primary Care Physicians' and Patients' Perspectives on Equity and Health Security of Infectious Disease Digital Surveillance.

In Annals of family medicine

PURPOSE : The coronavirus disease 2019 (COVID-19) pandemic facilitated the rapid development of digital detection surveillance (DDS) for outbreaks. This qualitative study examined how DDS for infectious diseases (ID) was perceived and experienced by primary care physicians and patients in order to highlight ethical considerations for promoting patients' autonomy and health care rights.

METHODS : In-depth interviews were conducted with a purposefully selected group of 16 primary care physicians and 24 of their patients. The group was reflective of a range of ages, educational attainment, and clinical experiences from urban areas in northern and southern China. Interviews were audio recorded, transcribed, and translated. Two researchers coded data and organized it into themes. A third researcher reviewed 15% of the data and discussed findings with the other researchers to assure accuracy.

RESULTS : Five themes were identified: ambiguity around the need for informed consent with usage of DDS; importance of autonomous decision-making; potential for discrimination against vulnerable users of DDS for ID; risk of social inequity and disparate care outcomes; and authoritarian institutions' responsibility for maintaining health data security. The adoption of DDS meant some patients would be reluctant to go to the hospital for fear of either being discriminated against or forced into quarantine. Certain groups (older people and children) were thought to be vulnerable to DDS misappropriation.

CONCLUSION : These findings indicate the paramount importance of establishing national and international ethical frameworks for DDS implementation. Frameworks should guide all aspects of ID surveillance, addressing privacy protection and health security, and underscored by principles of social equity and accountability.

Wong William Chi Wai, Zhao Ivy Yan, Ma Ye Xuan, Dong Wei Nan, Liu Jia, Pang Qin, Lu Xiao Qin, Molassiotis Alex, Holroyd Eleanor

2023-Jan-12

AI, artificial intelligence, disease outbreaks, disease survelillances, ethical issue

General General

RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.

In Briefings in bioinformatics

Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent thermal instability due to in-line hydrolysis, a chemical degradation reaction. Therefore, predicting and understanding RNA degradation is a crucial and urgent task. Here we present RNAdegformer, an effective and interpretable model architecture that excels in predicting RNA degradation. RNAdegformer processes RNA sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing, while utilizing biophysical features of RNA. We demonstrate that RNAdegformer outperforms previous best methods at predicting degradation properties at nucleotide resolution for COVID-19 mRNA vaccines. RNAdegformer predictions also exhibit improved correlation with RNA in vitro half-life compared with previous best methods. Additionally, we showcase how direct visualization of self-attention maps assists informed decision-making. Further, our model reveals important features in determining mRNA degradation rates via leave-one-feature-out analysis.

He Shujun, Gao Baizhen, Sabnis Rushant, Sun Qing

2023-Jan-12

COVID-19 mRNA, bioinformatics, deep learning, mRNA vaccine degradation

Public Health Public Health

Machine Learning-Assisted Real-Time Polymerase Chain Reaction and High-Resolution Melt Analysis for SARS-CoV-2 Variant Identification.

In Analytical chemistry

Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.

Promja Sutossarat, Puenpa Jiratchaya, Achakulvisut Titipat, Poovorawan Yong, Lee Su Yin, Athamanolap Pornpat, Lertanantawong Benchaporn

2023-Jan-12

Internal Medicine Internal Medicine

Digital Cough Monitoring - A Potential Predictive Acoustic Biomarker Of Clinical Outcomes in Hospitalized COVID-19 Patients.

In Journal of biomedical informatics ; h5-index 55.0

PURPOSE : Recent developments in the field of artificial intelligence and acoustics have made it possible to objectively monitor cough in clinical and ambulatory settings. We hypothesized that time patterns of objectively measured cough in COVID-19 patients could predict clinical prognosis and help rapidly identify patients at high risk of intubation or death.

METHODS : One hundred and twenty-three patients hospitalized with COVID-19 were enrolled at University of Florida Health Shands and the Centre Hospitalier de l'Université de Montréal. Patients' cough was continuously monitored digitally along with clinical severity of disease until hospital discharge, intubation, or death. The natural history of cough in hospitalized COVID-19 disease was described and logistic models fitted on cough time patterns were used to predict clinical outcomes.

RESULTS : In both cohorts, higher early coughing rates were associated with more favorable clinical outcomes. The transitional cough rate, or maximum cough per hour rate predicting unfavorable outcomes, was 3·40 and the AUC for cough frequency as a predictor of unfavorable outcomes was 0·761. The initial 6h (0·792) and 24h (0·719) post-enrolment observation periods confirmed this association and showed similar predictive value.

INTERPRETATION : Digital cough monitoring could be used as a prognosis biomarker to predict unfavorable clinical outcomes in COVID-19 disease. With early sampling periods showing good predictive value, this digital biomarker could be combined with clinical and paraclinical evaluation and is well adapted for triaging patients in overwhelmed or resources-limited health programs.

Altshuler Ellery, Tannir Bouchra, Jolicoeur Gisèle, Rudd Matthew, Saleem Cyrus, Cherabuddi Kartikeya, Hélène Doré Dominique, Nagarsheth Parav, Brew Joe, Small Peter M, Glenn Morris J, Grandjean Lapierre Simon

2023-Jan-09

Artificial intelligence, Clinical decision making, Cough, Covid-19, Machine learning

General General

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group.

METHODS : A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers.

RESULTS : Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes.

CONCLUSIONS : ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.

Rojas-García Mariano, Vázquez Blanca, Torres-Poveda Kirvis, Madrid-Marina Vicente

2023-Jan-11

COVID-19, Lethality risk markers, Machine learning, Mexico

General General

Machine learning for optimal test admission in the presence of resource constraints.

In Health care management science

Developing rapid tools for early detection of viral infection is crucial for pandemic containment. This is particularly crucial when testing resources are constrained and/or there are significant delays until the test results are available - as was quite common in the early days of Covid-19 pandemic. We show how predictive analytics methods using machine learning algorithms can be combined with optimal pre-test screening mechanisms, greatly increasing test efficiency (i.e., rate of true positives identified per test), as well as to allow doctors to initiate treatment before the test results are available. Our optimal test admission policies account for imperfect accuracy of both the medical test and the model prediction mechanism. We derive the accuracy required for the optimized admission policies to be effective. We also show how our policies can be extended to re-testing high-risk patients, as well as combined with pool testing approaches. We illustrate our techniques by applying them to a large data reported by the Israeli Ministry of Health for RT-PCR tests from March to September 2020. Our results demonstrate that in the context of the Covid-19 pandemic a pre-test probability screening tool with conventional RT-PCR testing could have potentially increased efficiency by several times, compared to random admission control.

Elitzur Ramy, Krass Dmitry, Zimlichman Eyal

2023-Jan-12

Data analytics, Machine learning, Optimal test admission policies, Predictive analytics

Public Health Public Health

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

In Journal of hazardous materials

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

Babaan Jennieveive, Hsu Fang-Tzu, Wong Pei-Yi, Chen Pau-Chung, Guo Yue-Leon, Lung Shih-Chun Candice, Chen Yu-Cheng, Wu Chih-Da

2023-Jan-07

Diurnal changes, Ensemble learning, Geospatial artificial intelligence, Land use regression, O(3)

General General

An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction.

In Artificial intelligence in medicine ; h5-index 34.0

Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict patients' OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner's Health Facts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs to cluster patients and EHR features respectively, and conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.

Dong Xinyu, Wong Rachel, Lyu Weimin, Abell-Hart Kayley, Deng Jianyuan, Liu Yinan, Hajagos Janos G, Rosenthal Richard N, Chen Chao, Wang Fusheng

2023-Jan

Clinical decision support, Deep learning, Electronic health records, Graph neural network, Long short-term memory, Opioid overdose, Opioid poisoning

Public Health Public Health

Prognosis of COVID-19 patients using lab tests: A data mining approach.

In Health science reports

BACKGROUND : The rapid prevalence of coronavirus disease 2019 (COVID-19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID-19 patients using data mining techniques.

METHODS : In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other.

RESULTS : Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models.

CONCLUSION : Data mining methods have the potential to be used for predicting outcomes of COVID-19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID-19 patients.

Khounraz Fariba, Khodadoost Mahmood, Gholamzadeh Saeid, Pourhamidi Rashed, Baniasadi Tayebeh, Jafarbigloo Aida, Mohammadi Gohar, Ahmadi Mahnaz, Ayyoubzadeh Seyed Mohammad

2023-Jan

COVID‐19, Gradient Boosted Trees, artificial intelligence, data mining, machine learning

General General

Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?

In Economic modelling

Before vaccines became commonly available, compliance with nonpharmaceutical only preventive measures offered protection against COVID-19 infection. Compliance is therefore expected to have physical health implications for the individual and others. Moreover, in the context of the highly contagious coronavirus, perceived noncompliance can increase the subjective risk assessment of contracting the virus and, as a result, increase psychological distress. However, the implications of (public) noncompliance on the psychological health of others have not been sufficiently explored in the literature. Examining this is of utmost importance in light of the pandemic's elevated prevalence of depressive symptoms across countries. Using nationally representative data from South Africa, we explore the relationship between depressive symptoms and perceived noncompliance. We examine this relationship using a double machine learning approach while controlling for observable selection. Our result shows that the perception that neighbors are noncompliant is correlated with self-reported depressive symptoms. Therefore, in the context of a highly infectious virus, noncompliance has detrimental effects on the wellbeing of others.

Oyenubi Adeola, Kollamparambil Umakrishnan

2023-Mar

Causal inference, Double machine learning, Mental health, Negative externality, South Africa

General General

Independent regulation of Z-lines and M-linesduring sarcomere assembly in cardiac myocytesrevealed by the automatic image analysis software sarcApp

bioRxiv Preprint

Sarcomeres are the basic contractile units within cardiac myocytes, and the collective shortening of sarcomeres aligned along myofibrils generates the force driving the heartbeat. The alignment of the individual sarcomeres is important for proper force generation, and misaligned sarcomeres are associated with diseases including cardiomyopathies and COVID-19. The actin bundling protein, -actinin-2, localizes to the Z-Bodies of sarcomere precursors and the Z-Lines of sarcomeres, and has been used previously to assess sarcomere assembly and maintenance. Previous measurements of -actinin-2 organization have been largely accomplished manually, which is time-consuming and has hampered research progress. Here, we introduce sarcApp, an image analysis tool that quantifies several components of the cardiac sarcomere and their alignment in muscle cells and tissue. We first developed sarcApp to utilize deep learning- based segmentation and real space quantification to measure -actinin-2 structures and determine the organization of both precursors and sarcomeres/myofibrils. We then expanded sarcApp to analyze M-Lines using the localization of myomesin and a protein that connects the Z-Lines to the M-Line (titin). sarcApp produces 33 distinct measurements per cell and 24 per myofibril that allow for precise quantification of changes in sarcomeres, myofibrils, and their precursors. We validated this system with perturbations to sarcomere assembly. Surprisingly, we found perturbations that affected Z-Lines and M-Lines differently, suggesting that they may be regulated independently during sarcomere assembly.

Neininger-Castro, A. C.; Hayes, J. B.; Sanchez, Z. C.; Taneja, N.; Fenix, A. M.; Moparthi, S.; Vassilopoulos, S.; Burnette, D. T.

2023-01-12

General General

Coronavirus covid-19 detection by means of explainable deep learning.

In Scientific reports ; h5-index 158.0

The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

Mercaldo Francesco, Belfiore Maria Paola, Reginelli Alfonso, Brunese Luca, Santone Antonella

2023-Jan-10

Public Health Public Health

Innovations in Public Health Surveillance for Emerging Infections.

In Annual review of public health

Public health surveillance is defined as the ongoing, systematic collection, analysis, and interpretation of health data and is closely integrated with the timely dissemination of information that the public needs to know and upon which the public should act. Public health surveillance is central to modern public health practice by contributing data and information usually through a national notifiable disease reporting system (NNDRS). Although early identification and prediction of future disease trends may be technically feasible, more work is needed to improve accuracy so that policy makers can use these predictions to guide prevention and control efforts. In this article, we review the advantages and limitations of the current NNDRS in most countries, discuss some lessons learned about prevention and control from the first wave of COVID-19, and describe some technological innovations in public health surveillance, including geographic information systems (GIS), spatial modeling, artificial intelligence, information technology, data science, and the digital twin method. We conclude that the technology-driven innovative public health surveillance systems are expected to further improve the timeliness, completeness, and accuracy of case reporting during outbreaks and also enhance feedback and transparency, whereby all stakeholders should receive actionable information on control and be able to limit disease risk earlier than ever before. Expected final online publication date for the Annual Review of Public Health, Volume 44 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Jia Peng, Liu Shiyong, Yang Shujuan

2022-Jan-10

General General

Big Data in Genomic Research for Big Questions with Examples from Covid-19 and Other Zoonoses.

In Journal of applied microbiology

Omics research inevitably involves the collection and analysis of big data, which can only be handled by automated approaches. Here we point out that analysis of big data in the field of genomics dictates certain requirements, such as specialized software, quality control of input data, and simplification for visualization of the results. The latter results in loss of information, as is exemplified for phylogenetic trees. Clear communication of big data analyses can be enhanced by novel visualization strategies. The interpretation of findings is sometimes hampered when dedicated analytical tools are not fully understood by microbiologists, while the researchers performing these analyses may not have a full overview of the biology of the microbes under study. These issues are illustrated here, using SARS-Cov-2 and Salmonella enterica as zoonotic examples. Whereas in scientific communications jargon should be avoided or explained, nomenclature to group similar organisms and distinguish these from more distant relatives is not only essential, but also influences the interpretation of results. Unfortunately, changes in taxonomically accepted names are now so frequent that they hamper rather than assist research, as is illustrated with difficulties of microbiome studies. Nomenclature to group viral isolates, as is done for SARS-Cov2, is also not without difficulties. Some weaknesses in current omics research stem from poor quality of data or biased databases, and problems can be magnified by machine learning approaches. Moreover, the overall opus of scientific publications can now be considered 'big data', as is illustrated by the avalanche of Covid-19-related publications. The peer-review model of scientific publishing is only barely coping with this novel situation, resulting in retractions and publication of bogus works. The avalanche of scientific publications that originated from the current pandemic can obstruct literature searches and this will unfortunately continue over time.

Wassenaar Trudy M, Ussery David W, Rosel Adriana Cabal

2022-Dec-16

Covid-19, Salmonella, big data, genomics, omics, scientific publishing, zoonoses

General General

Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers.

In Journal of medical virology

The COVID-19 pandemic, caused by rapidly evolving variants of SARS-CoV-2, continues to be a global health threat. SARS-CoV-2 infection symptoms often intersect with other nonsevere respiratory infections, making early diagnosis challenging. There is an urgent need for early diagnostic and prognostic biomarkers to predict severity and reduce mortality when a sudden outbreak occurs. This study implemented a novel approach of integrating bioinformatics and machine learning algorithms over publicly available clinical COVID-19 transcriptome datasets. The robust seven-gene biomarker identified through this analysis can not only discriminate SARS-CoV-2 associated acute respiratory illness (ARI) from other types of ARIs but also can discriminate severe COVID-19 patients from nonsevere COVID-19 patients. Validation of the 7-gene biomarker in an independent blood transcriptome dataset of longitudinal analysis of COVID-19 patients across various stages of the disease showed that the dysregulation of the identified biomarkers during severe disease is restored during recovery, showing their prognostic potential. The blood biomarkers identified in this study can serve as potential diagnostic candidates and help reduce COVID-19-associated mortality. This article is protected by copyright. All rights reserved.

Krishnamoorthy Pandikannan, Raj Athira S, Kumar Himanshu

2023-Jan-10

Blood biomarker, Machine learning, Meta-analysis, SARS-CoV-2, Transcriptome

General General

Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19.

In Financial innovation

This paper aims to probe the influence of innovation spillovers in the artificial intelligence (AI) and financial technology (Fin-tech) industries on the value of the internet of things (IoT) companies. Python was utilized to download public information from Yahoo Finance, and then the GARCH model was used to extract the fluctuations of cross-industry innovation spillovers. Next, the Fama-French three-factor model was used to explore the interactive changes between variables. The panel data regression analysis indicates that the more firms accept innovation spillovers from other industries, the better the excess return; however, this effect differs because of industrial attributes and the environmental changes induced by COVID-19. Additionally, this study finds that investing in large-cap growth stocks of IoT firms is more likely to yield excess returns. Finally, the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation-interactive industries by referring to the practices of Singapore and South Korea.

Ho Chi-Ming

2023

AI, Covid-19, Fin-Tech, Innovation spillover, IoT

General General

Using knowledge of, attitude toward, and daily preventive practices for COVID-19 to predict the level of post-traumatic stress and vaccine acceptance among adults in Hong Kong.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : COVID-19 has been perceived as an event triggering a new type of post-traumatic stress (PTSD) that can live during and after the pandemic itself. However, it remains unclear whether such PTSD is partly related to people's knowledge of, attitude toward and daily behavioral practices (KAP) for COVID-19.

METHODS : Through a telephone survey, we collected responses from 3,011 adult Hong Kong residents. Then using the Catboost machine learning method, we examined whether KAP predicted the participant's PTSD level, vaccine acceptance and participation in voluntary testing.

RESULTS : Results suggested that having good preventative practices for, poor knowledge of, and negative attitude toward COVID-19 were associated with greater susceptibility to PTSD. Having a positive attitude and good compliance with preventative practices significantly predicted willingness to get vaccinated and participate in voluntary testing. Good knowledge of COVID-19 predicted engagement in testing but showed little association with vaccine acceptance.

DISCUSSION : To maintain good mental health and ongoing vaccine acceptance, it is important to foster people's sense of trust and belief in health professionals' and government's ability to control COVID-19, in addition to strengthening people's knowledge of and compliance with preventative measures.

Cao Yuan, Siu Judy Yuen-Man, Choi Kup-Sze, Ho Nick Cho-Lik, Wong Kai Chun, Shum David H K

2022

COVID-19, KAP, PTSD, knowledge – attitude – behavior, vaccine

General General

Drug repositioning based on heterogeneous networks and variational graph autoencoders.

In Frontiers in pharmacology

Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.

Lei Song, Lei Xiujuan, Liu Lian

2022

COVID-19, drug repositioning, graph representation learning, heterogeneous network, variational graph autoencoders

Cardiology Cardiology

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.

In Informatics in medicine unlocked

BACKGROUND : Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.

MATERIAL AND METHOD : We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.

RESULTS : Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.

CONCLUSIONS : Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Tuncer Ilknur, Barua Prabal Datta, Dogan Sengul, Baygin Mehmet, Tuncer Turker, Tan Ru-San, Yeong Chai Hong, Acharya U Rajendra

2023

COVID-19 classification, Computed tomography images, Swin, Swin-textural, Textural feature extraction

General General

Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.

In Journal of Taibah University Medical Sciences

OBJECTIVE : The coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach. The purpose of this study was to demonstrate the effectiveness of our approach to identify drugs for the prevention of COVID-19 through literature review, cluster analysis, drug docking calculations, and clinical trial data.

METHODS : This research was conducted in four main stages. First, the text mining stage was carried out by involving Bidirectional Encoder Representations from Transformers for Biomedical to obtain vector representation of each word in the sentence from texts. The next stage generated the disease-drug associations, which were obtained from the correlation between disease and drug. Next, the clustering stage grouped the rules through the similarity of diseases by utilizing Term Frequency-Inverse Document Frequency as its feature. Finally, the drug candidate extraction stage was processed through leveraging PubChem and DrugBank databases. We further used the drug docking package AUTODOCK VINA in PyRx software to verify the results.

RESULTS : Comparative analyses showed that the percentage of findings using mining with clustering outperformed mining without clustering in all experimental settings. In addition, we suggest that the top three drugs/phytochemicals by drug docking analysis may be effective in preventing COVID-19.

CONCLUSIONS : The proposed method for text mining utilizing the clustering method is quite promising in the discovery of drug candidates for the prevention of COVID-19 through the biomedical literature.

Supianto Ahmad Afif, Nurdiansyah Rizky, Weng Chia-Wei, Zilvan Vicky, Yuwana Raden Sandra, Arisal Andria, Pardede Hilman Ferdinandus, Lee Min-Min, Huang Chien-Hung, Ng Ka-Lok

2023-Jan-04

COVID-19, Coronavirus, Drug docking, Phytochemicals, SARS-CoV-2, Text mining

Surgery Surgery

Literature analysis of artificial intelligence in biomedicine.

In Annals of translational medicine

Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

Hulsen Tim

2022-Dec

Artificial intelligence (AI), Embase, PubMed, biomedicine, deep learning (DL), healthcare, literature, machine learning (ML), medicine, neural networks (NNs)

oncology Oncology

Predictive model for BNT162b2 vaccine response in cancer patients based on blood cytokines and growth factors.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : Patients with cancer, especially hematological cancer, are at increased risk for breakthrough COVID-19 infection. So far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed.

METHODS : We employed machine learning approaches to identify a biomarker signature based on blood cytokines, chemokines, and immune- and non-immune-related growth factors linked to vaccine immunogenicity in 199 cancer patients receiving the BNT162b2 vaccine.

RESULTS : C-reactive protein (general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) correctly classified patients with a diminished vaccine response assessed at day 49 with >80% accuracy. Amongst these, CRP showed the highest predictive value for poor response to vaccine administration. Importantly, this unique signature of vaccine response was present at different studied timepoints both before and after vaccination and was not majorly affected by different anti-cancer treatments.

CONCLUSION : We propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at persistent high risk of COVID-19 despite vaccination with BNT162b2. Our data also suggest that such a signature may reflect the inherent immunological constitution of some cancer patients who are refractive to immunotherapy.

Konnova Angelina, De Winter Fien H R, Gupta Akshita, Verbruggen Lise, Hotterbeekx An, Berkell Matilda, Teuwen Laure-Anne, Vanhoutte Greetje, Peeters Bart, Raats Silke, der Massen Isolde Van, De Keersmaecker Sven, Debie Yana, Huizing Manon, Pannus Pieter, Neven Kristof Y, Ariën Kevin K, Martens Geert A, Bulcke Marc Van Den, Roelant Ella, Desombere Isabelle, Anguille Sébastien, Berneman Zwi, Goossens Maria E, Goossens Herman, Malhotra-Kumar Surbhi, Tacconelli Evelina, Vandamme Timon, Peeters Marc, van Dam Peter, Kumar-Singh Samir

2022

BNT162b2, COVID-19 vaccine, SARS-CoV-2, chemokines, cytokines, growth factors, haematological malignancies, solid cancers

General General

D3SENet: A hybrid deep feature extraction network for Covid-19 classification using chest X-ray images.

In Biomedical signal processing and control

Covid-19 is one of the biggest global epidemics seen in the world in recent years. Because of this, people's daily lifestyles, the economic conditions of countries and individuals, and most importantly, their health status has been adversely affected all over the world. Millions of people around the world have died from this disease. For this reason, rapid and accurate detection of the disease is of great importance in terms of treatment and precautions. In addition, it is especially important to correctly distinguish between Covid-19 and non-Covid-19 pneumonia diseases for correct diagnosis and treatment. These two diseases cause similar symptoms, and the symptoms and the effects of the disease on the body should be carefully examined for their differentiation. Chest X-ray images, chest computerized tomography, and swab tests are commonly used to detect patients infected with COVID-19. This disease affects the lungs the most in the body and causes fatal side effects such as shortness of breath. Therefore, medical images taken from the chest play an important role in the diagnosis of the disease. The fact that X-rays are faster and cheaper than computerized tomography has led to an increase in studies on the detection of disease with X-rays. In recent years, the impressive results of deep learning in the field of computer vision have attracted researchers to this field when working with image data. This study aims to detect these diseases on chest X-ray images collected from Covid-19 patients, pneumonia patients, and healthy individuals. We proposed a hybrid feature extraction network namely D3SENET which consists of DarkNet53, DarkNet19, DenseNet201, SqueezeNet, and EfficientNetb0. After a balanced data set was prepared, feature vectors were obtained from images using deep learning-based CNN models and the size of feature vectors was reduced by feature selection algorithms. Obtained features were classified by traditional machine learning methods such as SVMs. The number of features to be selected was tested by the iterative increment method and the parameters with the highest accuracy rate were obtained. As a result, it was seen that healthy and infected individuals were detected in 3 classes with an accuracy rate of 98.78%. In addition, the confusion matrix, precision, recall values, and F1 score of the obtained model are also given.

Kaya Mustafa, Eris Mustafa

2023-Apr

Covid-19, Deep Ensemble Network, Deep Learning, Machine Learning, Medical Image Processing

General General

Locating frontline workers' position up against COVID-19.

In Journal of family medicine and primary care

History of mankind has been brutal and marred by wars, attacks, invasions, occupying others territory and killing other human beings with their animals in the process. But now with arrival of Industrial Revolutions in last century or so, we gradually realized that for having and maintaining economic prosperity; we need others' cooperation and since then full- scale wars almost disappeared. But when we fight now and support others in the process, we realise that brute force is only occasionally used entity and most of the times technological methods are deployed to injure others. It is this rationale which makes way for people of either gender having capability to use highly advanced weaponry to enter the arena to decide fate of their side. Therefore, now war is not exclusively masculine entity and that analogy may not be appropriate in modern era. When we use masculine notion to explain our war against COVID-19, there are many shortcomings.

Gupta Harish

2022-Oct

Artificial Intelligence, essential service providers, frontline workers, health-care professionals, modern warfare

Public Health Public Health

A demonstration of Modified Treatment Policies to evaluate shifts in mobility and COVID-19 case rates in U.S. counties.

In American journal of epidemiology ; h5-index 65.0

Mixed evidence exists of associations between mobility data and COVID-19 case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall 2020 in the United States and to demonstrate modified treatment policies (MTPs) to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indices on the number of newly reported cases per 100,000 residents two weeks ahead. Primary analyses used targeted minimum loss-based estimation (TMLE) with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indices and subsequent new case rates. However, after confounder adjustment, none of the indices showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health.

Nugent Joshua R, Balzer Laura B

2023-Jan-09

COVID-19 Pandemic, Machine Learning, modified treatment policy, targeted learning

General General

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.

In Computers in biology and medicine

The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

Zhang Jun, Wu Jingyue, Qiu Yiyi, Song Aiguo, Li Weifeng, Li Xin, Liu Yecheng

2023-Jan-05

Automatic speech recognition, Diagnosis, Human-computer interaction, Machine learning, Smart hospital, Transcription

Public Health Public Health

The Use of Digital Technology for COVID-19 Detection, and Response Management in Indonesia: A Mixed Methods Study.

In Interactive journal of medical research

BACKGROUND : The COVID-19 pandemic has triggered the greater use of digital technologies as part of the healthcare response in many countries, including in Indonesia.

OBJECTIVE : The objective of our study was to identify and review the use of digital health technologies in COVID-19 detection and response management in Indonesia. It is the world's fourth most populous nation, and Southeast Asia's most populous country, with considerable public health pressures.

METHODS : This paper conducted a literature review of publicly accessible information in technical and scientific journals, as well as news articles between September 2020 to August 2022 to identify the use case examples of digital technologies in COVID-19 detection and response management in Indonesia.

RESULTS : The results are presented into three groups, namely (i) Big Data, Artificial Intelligence and Machine Learning (technologies for the collection and/or processing of data); (ii) Healthcare System Technologies (acting at the public health level); and (iii) Population Treatment (acting at the individual patient level). Some of these technologies are the result of government-academia-private sector collaborations during the pandemic, which represent a novel, multi-sectoral practice in Indonesia within the public healthcare ecosystem. A small number of the identified technologies pre-existed the pandemic, but were upgraded and adapted for the current needs.

CONCLUSIONS : Digital technologies were developed in Indonesia during the pandemic, with a direct impact in supporting the COVID-19 management, detection, response, and treatment. They addressed different areas of the technological spectrum, and with different levels of adoption, ranging from local, regional to national. The indirect impact from this wave of technological creation and use, is to provide a strong foundation for fostering future multi-sectoral collaboration within the national healthcare system of Indonesia.

Nur Aisyah Dewi, Lokopessy Alfiano Fawwaz, Naman Maryan, Diva Haniena, Manikam Logan, Adisasmito Wiku, Kozlakidis Zisis

2023-Jan-09

General General

Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment.

In Procedia computer science

The Covid-19 (coronavirus) pandemic creates a worldwide health crisis. According to the WHO, the effective protection system is wearing a face mask in public places. Many studies proved that carrying a face mask is also one of the precautions to decrease the possibility of viral transmission. Strict monitoring of face mask being worn by people is now enforced in many countries. Manual observation and monitoring is quite tedious. Hence, automated systems have been researched using well-kwown face mask detection methods. However, this research paper, deals with some deep learning models which can be effectively used to detect multiple face masks in a crowded environment when the amount of incoming data from sensors is huge or in otherwise stated to a Big data problem. Hence, standalone face detection models are not quite suited. Deep learning models are required in such Big data scenario which forms the essence of this study.

Rekha V, Manoharan J Samuel, Hemalatha R, Saravanan D

2022

Big Data, Complex Data analytics, Deep Learning models, Face Mask Detection, Multi – Sensor Data Acquisition

General General

A fuzzy fine-tuned model for COVID-19 diagnosis.

In Computers in biology and medicine

The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.

Esmi Nima, Golshan Yasaman, Asadi Sara, Shahbahrami Asadollah, Gaydadjiev Georgi

2023-Jan-04

Blind/Referenceless image spatial quality evaluator, COVID-19, Deep learning, Fuzzy pooling, Weighted multi-class cross-entropy

General General

Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0.

In Health and technology

OBJECTIVE : The objective of this theoretical paper is to identify conceptual solutions for securing, predicting, and improving vaccine production and supply chains.

METHOD : The case study, action research, and review method is used with secondary data - publicly available open access data.

RESULTS : A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event. A new conceptual framework is designed to integrate the emerging solutions in vaccine production and supply chains. The framework is constructed to improve the state-of-the-art by intersecting the previously isolated disciplines of edge computing; cyber-risk analytics; healthcare systems, and AI algorithms.

CONCLUSION : For healthcare systems to cope better during a disease X event than during Covid-19, we need multiple highly specific AI algorithms, targeted for solving specific problems. The proposed framework would reduce production and supply chain risk and complexity in a Disease X event.

Radanliev Petar, De Roure David

2023-Jan-04

Artificial intelligence, Disease X, Healthcare systems, Industry 4.0, Risk assessment, Vaccine production and supply chains

General General

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic.

In New generation computing

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

Sahoo Santosh Kumar, Palai G, Altahan Baraa Riyadh, Ahammad Sk Hasane, Priya P Poorna, Hossain Md Amzad, Rashed Ahmed Nabih Zaki

2023-Jan-02

Deep learning, Detectron2, Intersection over union, Object detection, Social distancing

Public Health Public Health

A measurement method for mental health based on dynamic multimodal feature recognition.

In Frontiers in public health

INTRODUCTION : The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities.

METHODS : Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment.

RESULTS : The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests.

CONCLUSION : Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems.

Xu Haibo, Wu Xiang, Liu Xin

2022

deep learning, emotion recognition, interactive assessment scale, mental health assessment, video feature extraction

Radiology Radiology

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

In Quantitative imaging in medicine and surgery

BACKGROUND : The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification.

METHODS : Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models.

RESULTS : Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images.

CONCLUSIONS : The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Sun Hongfei, Ren Ge, Teng Xinzhi, Song Liming, Li Kang, Yang Jianhua, Hu Xiaofei, Zhan Yuefu, Wan Shiu Bun Nelson, Wong Man Fung Esther, Chan King Kwong, Tsang Hoi Ching Hailey, Xu Lu, Wu Tak Chiu, Kong Feng-Ming Spring, Wang Yi Xiang J, Qin Jing, Chan Wing Chi Lawrence, Ying Michael, Cai Jing

2023-Jan-01

Coronavirus disease 2019 (COVID-19), bone suppression, chest X-ray (CXR), super-resolution

General General

Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests an external P.1 variant origin.

In Frontiers in microbiology

Brazil was the epicenter of worldwide pandemics at the peak of its second wave. The genomic/proteomic perspective of the COVID-19 pandemic in Brazil could provide insights to understand the global pandemics behavior. In this study, we track SARS-CoV-2 molecular information in Brazil using real-time bioinformatics and data science strategies to provide a comparative and evolutive panorama of the lineages in the country. SWeeP vectors represented the Brazilian and worldwide genomic/proteomic data from Global Initiative on Sharing Avian Influenza Data (GISAID) between February 2020 and August 2021. Clusters were analyzed and compared with PANGO lineages. Hierarchical clustering provided phylogenetic and evolutionary analyses of the lineages, and we tracked the P.1 (Gamma) variant origin. The genomic diversity based on Chao's estimation allowed us to compare richness and coverage among Brazilian states and other representative countries. We found that epidemics in Brazil occurred in two moments with different genetic profiles. The P.1 lineages emerged in the second wave, which was more aggressive. We could not trace the origin of P.1 from the variants present in Brazil. Instead, we found evidence pointing to its external source and a possible recombinant event that may relate P.1 to a B.1.1.28 variant subset. We discussed the potential application of the pipeline for emerging variants detection and the PANGO terminology stability over time. The diversity analysis showed that the low coverage and unbalanced sequencing among states in Brazil could have allowed the silent entry and dissemination of P.1 and other dangerous variants. This study may help to understand the development and consequences of variants of concern (VOC) entry.

Perico Camila P, De Pierri Camilla R, Neto Giuseppe Pasqualato, Fernandes Danrley R, Pedrosa Fabio O, de Souza Emanuel M, Raittz Roberto T

2022

SWeeP, big data, diversity, genomics and proteomics, machine learning, virus

General General

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images.

In Journal of bionic engineering

Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur's entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Han Yan, Chen Weibin, Heidari Ali Asghar, Chen Huiling

2023-Jan-04

Bionic algorithm, COVID-19, Kapur’s entropy, Meta-heuristic algorithm, Multi-verse optimizer, Multilevel threshold image segmentation

General General

Integration of improved YOLOv5 for face mask detector and auto-labeling to generate dataset for fighting against COVID-19.

In The Journal of supercomputing

One of the most effective deterrent methods is using face masks to prevent the spread of the virus during the COVID-19 pandemic. Deep learning face mask detection networks have been implemented into COVID-19 monitoring systems to provide effective supervision for public areas. However, previous works have limitations: the challenge of real-time performance (i.e., fast inference and low accuracy) and training datasets. The current study aims to propose a comprehensive solution by creating a new face mask dataset and improving the YOLOv5 baseline to balance accuracy and detection time. Particularly, we improve YOLOv5 by adding coordinate attention (CA) module into the baseline backbone following two different schemes, namely YOLOv5s-CA and YOLOV5s-C3CA. In detail, we train three models with a Kaggle dataset of 853 images consisting of three categories: without a mask "NM," with mask "M," and incorrectly worn mask "IWM" classes. The experimental results show that our modified YOLOv5 with CA module achieves the highest accuracy mAP@0.5 of 93.9% compared with 87% of baseline and detection time per image of 8.0 ms (125 FPS). In addition, we build an integrated system of improved YOLOv5-CA and auto-labeling module to create a new face mask dataset of 7110 images with more than 3500 labels for three categories from YouTube videos. Our proposed YOLOv5-CA and the state-of-the-art detection models (i.e., YOLOX, YOLOv6, and YOLOv7) are trained on our 7110 images dataset. In our dataset, the YOLOv5-CA performance enhances with mAP@0.5 of 96.8%. The results indicate the enhancement of the improved YOLOv5-CA model compared with several state-of-the-art works.

Pham Thi-Ngot, Nguyen Viet-Hoan, Huh Jun-Ho

2023-Jan-03

Auto-labeling, COVID-19, Coordinate attention, Deep learning, Face mask detection, YOLO, YOLOv5, You Only Look One

General General

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.

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

Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.

Attallah Omneya

2023-Feb-15

COVID-19, Convolution neural networks (CNN), Deep learning, Discrete wavelet transform, Dual-tree complex wavelet transform, Texture analysis

General General

MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.

In Expert systems with applications

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.

Ullah Zahid, Usman Muhammad, Gwak Jeonghwan

2023-Apr-15

COVID-19, Deep learning, Multi-task learning, Representation learning, Semi-supervised adversarial learning

General General

Critical review of conformational B-cell epitope prediction methods.

In Briefings in bioinformatics

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Cia Gabriel, Pucci Fabrizio, Rooman Marianne

2023-Jan-05

Antibody-specific epitope prediction, Benchmarking, Conformational B-cell epitope prediction, Immunoinformatics

Ophthalmology Ophthalmology

Can Tele-Neuro-Ophthalmology Be Useful Beyond the Pandemic?

In Current neurology and neuroscience reports

PURPOSE OF THE REVIEW : Neuro-ophthalmologists rapidly adopted telehealth during the COVID-19 pandemic to minimize disruption to patient care. This article reviews recent research on tele-neuro-ophthalmology adoption, current limitations, and potential use beyond the pandemic. The review considers how digital transformation, including machine learning and augmented reality, may be applied to future iterations of tele-neuro-ophthalmology.

RECENT FINDINGS : Telehealth utilization has been sustained among neuro-ophthalmologists throughout the pandemic. Adoption of tele-neuro-ophthalmology may provide solutions to subspecialty workforce shortage, patient access, physician wellness, and trainee educational needs within the field of neuro-ophthalmology. Digital transformation technologies have the potential to augment tele-neuro-ophthalmology care delivery by providing automated workflow solutions, home-based visual testing and therapies, and trainee education via simulators. Tele-neuro-ophthalmology use has and will continue beyond the COVID-19 pandemic. Digital transformation technologies, when applied to telehealth, will drive and revolutionize the next phase of tele-neuro-ophthalmology adoption and use in the years to come.

Lai Kevin E, Ko Melissa W

2023-Jan-07

Artificial intelligence, Augmented reality, Neuro-ophthalmology, Pandemic, Telehealth, Telemedicine

General General

Authentication of Covid-19 Vaccines Using Synchronous Fluorescence Spectroscopy.

In Journal of fluorescence

The present study demonstrates the potential of synchronous fluorescence spectroscopy and multivariate data analysis for authentication of COVID-19 vaccines from various manufacturers. Synchronous scanning fluorescence spectra were recorded for DNA-based and mRNA-based vaccines obtained through the NHS Central Liverpool Primary Care Network. Fluorescence spectra of DNA and DNA-based vaccines as well as RNA and RNA-based vaccines were identical to one another. The application of principal component analysis (PCA), PCA-Gaussian Mixture Models (PCA-GMM)) and Self-Organising Maps (SOM) methods to the fluorescence spectra of vaccines is discussed. The PCA is applied to extract the characteristic variables of fluorescence spectra by analysing the major attributes. The results indicated that the first three principal components (PCs) can account for 99.5% of the total variance in the data. The PC scores plot showed two distinct clusters corresponding to the DNA-based vaccines and mRNA-based vaccines respectively. PCA-GMM clustering complemented the PCA clusters by further classifying the mRNA-based vaccines and the GMM clusters revealed three mRNA-based vaccines that were not clustered with the other vaccines. SOM complemented both PCA and PCA-GMM and proved effective with multivariate data without the need for dimensions reduction. The findings showed that fluorescence spectroscopy combined with machine learning algorithms (PCA, PCA-GMM and SOM) is a useful technique for vaccination verification and has the benefits of simplicity, speed and reliability.

Assi Sulaf, Abbas Ismail, Arafat Basel, Evans Kieran, Al-Jumeily Dhiya

2023-Jan-07

Covid-19, Gaussian mixture models, Principal component analysis, Self organising maps, Synchronous fluorescence, Vaccines

Public Health Public Health

Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

In BMC bioinformatics

BACKGROUND : With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease.

RESULTS : On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development.

CONCLUSIONS : This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Liu Xuehan, Hasan Md Rakibul, Ahmed Khandaker Asif, Hossain Md Zakir

2023-Jan-06

Autoencoder, COVID-19 diagnosis, Machine learning, Multi-omics, Severity

General General

Nurses' Work Concerns and Disenchantment during the COVID-19 Pandemic: Machine Learning Analysis of Online Discussions.

In JMIR nursing

BACKGROUND : Online forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experience and concerns.

OBJECTIVE : The objective of this study is to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic.

METHODS : We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) to identify salient topics in the collected posts. A human-in-the-loop (HITL) analysis complemented the machine learning approach, categorizing topics into themes and sub-themes. We develop insights on nurses' evolving perspective based on temporal changes.

RESULTS : We identified themes for bi-weekly periods and grouped them into 20 major themes based on the work concerns inventory framework. Dominant work concerns varied during the study period. A detailed analysis of patterns in how themes evolve over time enables us to create narratives of work concerns.

CONCLUSIONS : The analysis demonstrates that professional online forums capture nuanced details about nurse work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of online discussions could provide useful data for healthcare organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions, and to design more effective responses and planning during crises.

Jiang Haoqiang, Castellanos Arturo, Castillo Alfred, Gomes Paulo J, Li Juanjuan, VanderMeer Debra

2023-Jan-03

Internal Medicine Internal Medicine

Severe COVID-19 Infection in Type 1 and Type 2 Diabetes During the First Three Waves in Sweden.

In Diabetes care ; h5-index 125.0

OBJECTIVE : Type 2 diabetes is an established risk factor for hospitalization and death in COVID-19 infection, while findings with respect to type 1 diabetes have been diverging.

RESEARCH DESIGN AND METHODS : Using nationwide health registries, we identified all patients aged ≥18 years with type 1 and type 2 diabetes in Sweden. Odds ratios (ORs) describe the general and age-specific risk of being hospitalized, need for intensive care, or dying, adjusted for age, socioeconomic factors, and coexisting conditions, compared with individuals without diabetes. Machine learning models were used to find predictors of outcomes among individuals with diabetes positive for COVID-19.

RESULTS : Until 30 June 2021, we identified 365 (0.71%) and 11,684 (2.31%) hospitalizations in 51,402 and 504,337 patients with type 1 and 2 diabetes, respectively, with 67 (0.13%) and 2,848 (0.56%) requiring intensive care unit (ICU) care and 68 (0.13%) and 4,020 (0.80%) dying (vs 7,824,181 individuals without diabetes [41,810 hospitalizations (0.53%), 8,753 (0.11%) needing ICU care, and 10,160 (0.13%) deaths). Although those with type 1 diabetes had moderately raised odds of being hospitalized (multiple-adjusted OR 1.38 [95% CI 1.24-1.53]), there was no independent effect on ICU care or death (OR of 1.21 [95% CI 0.94-1.52] and 1.13 [95% CI 0.88-1.48], respectively). Age and socioeconomic factors were the dominating features for predicting hospitalization and death in both types of diabetes.

CONCLUSIONS : Type 2 diabetes was associated with increased odds for all outcomes, whereas patients with type 1 diabetes had moderately increased odds of hospitalization but not ICU care and death.

Edqvist Jon, Lundberg Christina, Andreasson Karin, Björck Lena, Dikaiou Pigi, Ludvigsson Johnny, Lind Marcus, Adiels Martin, Rosengren Annika

2023-Jan-06

Internal Medicine Internal Medicine

The 2000HIV study: Design, multi-omics methods and participant characteristics.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : Even during long-term combination antiretroviral therapy (cART), people living with HIV (PLHIV) have a dysregulated immune system, characterized by persistent immune activation, accelerated immune ageing and increased risk of non-AIDS comorbidities. A multi-omics approach is applied to a large cohort of PLHIV to understand pathways underlying these dysregulations in order to identify new biomarkers and novel genetically validated therapeutic drugs targets.

METHODS : The 2000HIV study is a prospective longitudinal cohort study of PLHIV on cART. In addition, untreated HIV spontaneous controllers were recruited. In-depth multi-omics characterization will be performed, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and metagenomics, functional immunological assays and extensive immunophenotyping. Furthermore, the latent viral reservoir will be assessed through cell associated HIV-1 RNA and DNA, and full-length individual proviral sequencing on a subset. Clinical measurements include an ECG, carotid intima-media thickness and plaque measurement, hepatic steatosis and fibrosis measurement as well as psychological symptoms and recreational drug questionnaires. Additionally, considering the developing pandemic, COVID-19 history and vaccination was recorded. Participants return for a two-year follow-up visit. The 2000HIV study consists of a discovery and validation cohort collected at separate sites to immediately validate any finding in an independent cohort.

RESULTS : Overall, 1895 PLHIV from four sites were included for analysis, 1559 in the discovery and 336 in the validation cohort. The study population was representative of a Western European HIV population, including 288 (15.2%) cis-women, 463 (24.4%) non-whites, and 1360 (71.8%) MSM (Men who have Sex with Men). Extreme phenotypes included 114 spontaneous controllers, 81 rapid progressors and 162 immunological non-responders. According to the Framingham score 321 (16.9%) had a cardiovascular risk of >20% in the next 10 years. COVID-19 infection was documented in 234 (12.3%) participants and 474 (25.0%) individuals had received a COVID-19 vaccine.

CONCLUSION : The 2000HIV study established a cohort of 1895 PLHIV that employs multi-omics to discover new biological pathways and biomarkers to unravel non-AIDS comorbidities, extreme phenotypes and the latent viral reservoir that impact the health of PLHIV. The ultimate goal is to contribute to a more personalized approach to the best standard of care and a potential cure for PLHIV.

Vos Wilhelm A J W, Groenendijk Albert L, Blaauw Marc J T, van Eekeren Louise E, Navas Adriana, Cleophas Maartje C P, Vadaq Nadira, Matzaraki Vasiliki, Dos Santos Jéssica C, Meeder Elise M G, Fröberg Janeri, Weijers Gert, Zhang Yue, Fu Jingyuan, Ter Horst Rob, Bock Christoph, Knoll Rainer, Aschenbrenner Anna C, Schultze Joachim, Vanderkerckhove Linos, Hwandih Talent, Wonderlich Elizabeth R, Vemula Sai V, van der Kolk Mike, de Vet Sterre C P, Blok Willem L, Brinkman Kees, Rokx Casper, Schellekens Arnt F A, de Mast Quirijn, Joosten Leo A B, Berrevoets Marvin A H, Stalenhoef Janneke E, Verbon Annelies, van Lunzen Jan, Netea Mihai G, van der Ven Andre J A M

2022

COVID-19, HIV extreme phenotype, HIV reservoir, HIV-1, cardiovascular disease, hepatic disease, multi-omics, non-AIDS comorbidities

oncology Oncology

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes.

In Cell systems

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.

Gfeller David, Schmidt Julien, Croce Giancarlo, Guillaume Philippe, Bobisse Sara, Genolet Raphael, Queiroz Lise, Cesbron Julien, Racle Julien, Harari Alexandre

2022-Dec-23

CD8(+) T cell epitopes, HLA-I peptidomics, antigen presentation, computational biology, epitope predictions, immunology, machine learning

Public Health Public Health

Analysis and forecasting of air quality index based on satellite data.

In Inhalation toxicology

OBJECTIVE : The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI.

MATERIALS AND METHODS : Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla).

RESULTS : AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores.

CONCLUSIONS : Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

Singh Tinku, Sharma Nikhil, Satakshi Kumar

2023-Jan-05

Google Earth Engine (GEE), beta distribution, pollutants, remote sensing, satellite data

Radiology Radiology

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

In Journal of medical virology

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns heart rate variability patterns in pre-symptom by tracking relationships in sequential HR data. In the cross-validation results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and AUROC of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the cross-validation: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared to the vaccinated patients. The last finding is that the model trained in a certain period of times may provide degraded diagnosis performances as the virus continues to mutate. This article is protected by copyright. All rights reserved.

Chung Heewon, Ko Hoon, Lee Hooseok, Yon Dong Keon, Lee Won Hee, Kim Tae-Seong, Kim Kyung Won, Lee Jinseok

2023-Jan-05

COVID-19, deep learning, early diagnosis, heart rate, heart rate variability, smartwatch, transformer model

General General

Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

In BMC bioinformatics

BACKGROUND : Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss.

RESULTS : By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research.

CONCLUSIONS : MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Liu Chaozhong, Wang Linhua, Liu Zhandong

2023-Jan-04

COVID-19, Data integration, Deep learning, Single-cell sequencing analysis

General General

Neurophenotypes of COVID-19: risk factors and recovery trajectories.

In Research square

Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed "long COVID". It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery trajectories. We examined post-acute outcomes following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective neuropsychological measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits ("normal cognition" neurophenotype), although mild subjective attention and memory complaints were reported. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processed speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery trajectories at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing stability, the dysexecutive group showing improvement, and the memory-speed impaired group showing persistent processing speed deficits and fatigue, as well as worse functional outcomes. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery trajectories. This information may inform phenotype-specific approaches to treatment.

Prabhakaran Divya, Day Gregory, Munipalli Bala, Rush Beth, Pudalov Lauren, Niazi Shehzad, Brennan Emily, Powers Harry, Durvasula Ravi, Athreya Arjun, Blackmon Karen

2022-Dec-21

General General

A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program.

In medRxiv : the preprint server for health sciences

BACKGROUND : As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data.

METHODS AND FINDINGS : In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values.

CONCLUSIONS : The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

FUNDING SOURCE : This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.

DISCLAIMER : The content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.

Lorman Vitaly, Razzaghi Hanieh, Song Xing, Morse Keith, Utidjian Levon, Allen Andrea J, Rao Suchitra, Rogerson Colin, Bennett Tellen D, Morizono Hiroki, Eckrich Daniel, Jhaveri Ravi, Huang Yungui, Ranade Daksha, Pajor Nathan, Lee Grace M, Forrest Christopher B, Bailey L Charles

2022-Dec-26

General General

Diagnostic performance of artificial intelligence algorithms for detection of pulmonary involvement by COVID-19 based on portable radiography.

In Medicina clinica (English ed.)

INTRODUCTION AND OBJECTIVES : To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX).

MATERIAL AND METHODS : Prospective observational study that included patients admitted for suspected COVID-19 infection in a university hospital between July and November 2020. The reference standard of pulmonary involvement by SARS-CoV-2 comprised a positive PCR test and low-tract respiratory symptoms.

RESULTS : 493 patients were included, 140 (28%) with positive PCR and 32 (7%) with SARS-CoV-2 pneumonia. The AI-B algorithm had the best diagnostic performance (areas under the ROC curve AI-B 0.73, vs. AI-A 0.51, vs. AI-C 0.57). Using a detection threshold greater than 55%, AI-B had greater diagnostic performance than the specialist [(area under the curve of 0.68 (95% CI 0.64-0.72), vs. 0.54 (95% CI 0.49-0.59)].

CONCLUSION : AI algorithms based on portable RX enabled a diagnostic performance comparable to human assessment for the detection of SARS-CoV-2 lung involvement.

Cobeñas Ricardo Luis, de Vedia María, Florez Juan, Jaramillo Daniela, Ferrari Luciana, Re Ricardo

2022-Dec-30

Artificial intelligence, COVID-19, Lung, Machine learning, Pneumonia, Thoracic RX

General General

Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition.

In Machine vision and applications

Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Castro Eduardo, Ferreira Pedro M, Rebelo Ana, Rio-Torto Isabel, Capozzi Leonardo, Ferreira Mafalda Falcão, Gonçalves Tiago, Albuquerque Tomé, Silva Wilson, Afonso Carolina, Gamelas Sousa Ricardo, Cimarelli Claudio, Daoudi Nadia, Moreira Gabriel, Yang Hsiu-Yu, Hrga Ingrid, Ahmad Javed, Keswani Monish, Beco Sofia

2023

Computer vision, Deep learning, Fashion intelligence, Image retrieval, Summer school competition

General General

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

In Journal of digital imaging

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Yin Minyue, Liang Xiaolong, Wang Zilan, Zhou Yijia, He Yu, Xue Yuhan, Gao Jingwen, Lin Jiaxi, Yu Chenyan, Liu Lu, Liu Xiaolin, Xu Chao, Zhu Jinzhou

2023-Jan-03

Asymptomatic coronavirus-disease-2019 patients, Chest CT images, Convolutional neural networks, Deep learning, Transfer learning, Transformer

Public Health Public Health

Cluster analysis of adults unvaccinated for COVID-19 based on behavioral and social factors, National Immunization Survey-Adult COVID Module, United States.

In Preventive medicine ; h5-index 62.0

By the end of 2021, approximately 15% of U.S. adults remained unvaccinated against COVID-19, and vaccination initiation rates had stagnated. We used unsupervised machine learning (K-means clustering) to identify clusters of unvaccinated respondents based on Behavioral and Social Drivers (BeSD) of COVID-19 vaccination and compared these clusters to vaccinated participants to better understand social/behavioral factors of non-vaccination. The National Immunization Survey Adult COVID Module collects data on U.S. adults from September 26-December 31,2021 (n = 187,756). Among all participants, 51.6% were male, with a mean age of 61 years, and the majority were non-Hispanic White (62.2%), followed by Hispanic (17.2%), Black (11.9%), and others (8.7%). K-means clustering procedure was used to classify unvaccinated participants into three clusters based on 9 survey BeSD items, including items assessing COVID-19 risk perception, social norms, vaccine confidence, and practical issues. Among unvaccinated adults (N = 23,397), 3 clusters were identified: the "Reachable" (23%), "Less reachable" (27%), and the "Least reachable" (50%). The least reachable cluster reported the lowest concern about COVID-19, mask-wearing behavior, perceived vaccine confidence, and were more likely to be male, non-Hispanic White, with no health conditions, from rural counties, have previously had COVID-19, and have not received a COVID-19 vaccine recommendation from a healthcare provider. This study identified, described, and compared the characteristics of the three unvaccinated subgroups. Public health practitioners, healthcare providers and community leaders can use these characteristics to better tailor messaging for each sub-population. Our findings may also help inform decisionmakers exploring possible policy interventions.

Meng Lu, Masters Nina B, Lu Peng-Jun, Singleton James A, Kriss Jennifer L, Zhou Tianyi, Weiss Debora, Black Carla L

2022-Dec-31

COVID-19 vaccines, Cluster analysis, Health communication, Health policy, SARS-CoV-2, Vaccine hesitancy

General General

Investigating mental health outcomes of undergraduates and graduate students in Taiwan during the COVID-19 pandemic.

In Journal of American college health : J of ACH

Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.

Lin Ching-Hui, Lin Szu-Yin, Hu Bo-Hsien, Lo C Owen

2023-Jan-03

College students, Covid-19, mental health, postsecondary education

General General

Users' Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Within a few months, the COVID-19 pandemic has spread to many countries and has been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates, and have faced lack of confidence before marketing in France.

OBJECTIVE : This study aims to identify and investigate the opinion of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.

METHODS : This study was conducted in two phases. First, we filtered a collection of tweets related to COVID-19 available on twitter from February to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand labeled subset of 4,548 tweets.

RESULTS : A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focus mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled to extract nearly 350k tweets in French. The sentiment analysis model achieved a 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets and 43% of neutral tweets. This allowed to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose, and that COVID-19 is a commercial argument for the pharmaceutical companies.

CONCLUSIONS : Twitter might be a useful tool to investigate the arguments of vaccine mistrust as it unveils a political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

Dupuy-Zini Alexandre, Audeh Bissan, Gérardin Christel, Duclos Catherine, Gagneux-Brunon Amandine, Bousquet Cedric

2022-Aug-09

General General

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.

In PloS one ; h5-index 176.0

During the course of this research, we came up with a brand new distribution that is superior; we then presented and analysed the mathematical properties of this distribution; finally, we assessed its fuzzy reliability function. Because the novel distribution provides a number of advantages, like the reality that its cumulative distribution function and probability density function both have a closed form, it is very useful in a wide range of disciplines that are related to data science. One of these fields is machine learning, which is a sub field of data science. We used both traditional methods and Bayesian methodologies in order to generate a large number of different estimates. A test setup might have been carried out to assess the effectiveness of both the classical and the Bayesian estimators. At last, three different sets of Covid-19 death analysis were done so that the effectiveness of the new model could be demonstrated.

Almuqrin Muqrin A, Almutlak Salemah A, Gemeay Ahmed M, Almetwally Ehab M, Karakaya Kadir, Makumi Nicholas, Hussam Eslam, Aldallal Ramy

2023

General General

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans.

In Cognitive computation

This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.

Bhatele Kirti Raj, Jha Anand, Tiwari Devanshu, Bhatele Mukta, Sharma Sneha, Mithora Muktasha R, Singhal Stuti

2022-Dec-29

COVID-19, CT scan, Chest X-ray, Deep transfer learning, Machine learning

General General

Screening of COVID-19 Based on GLCM Features from CT Images Using Machine Learning Classifiers.

In SN computer science

In healthcare, the decision-making process is crucial, including COVID-19 prevention methods should include fast diagnostic methods. Computed tomography (CT) is used to diagnose COVID patients' conditions. There is inherent variation in the texture of a CT image of COVID, much like the texture of a CT image of pneumonia. The process of diagnosing COVID images manually is difficult and challenging. Using low-resolution images and a small COVID dataset, the extraction of discriminant characteristics and fine-tuning of hyperparameters in classifiers provide challenges for computer-assisted diagnosis. In radiomics, quantitative image analysis is frequently used to evaluate the prognosis and diagnose diseases. This research tests an ML model built on GLCM features collected from chest CT images to screen for COVID-19. In this study, Support Vector Machines, K-nearest neighbors, Random Forest, and XGBoost classifiers are used together with LBGM. Tuning tests were used to regulate the hyperparameters of the model. With cross-validation, tenfold results were obtained. Random Forest and SVM were the best classification methods for GLCM features with an overall accuracy of 99.94%. The network's performance was assessed in terms of sensitivity, accuracy, and specificity.

Godbin A Beena, Jasmine S Graceline

2023

COVID-19, Feature extraction, GLCM, LGBM, Machine learning, SVM

General General

Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.

In Computer methods and programs in biomedicine update

BACKGROUND : In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic.

METHODS : This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity.

RESULTS : We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system's sensitivity while producing more stable predictions.

CONCLUSIONS : We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data. .

Villegas Marta, Gonzalez-Agirre Aitor, Gutiérrez-Fandiño Asier, Armengol-Estapé Jordi, Carrino Casimiro Pio, Fernández David Pérez, Soares Felipe, Serrano Pablo, Pedrera Miguel, García Noelia, Valencia Alfonso

2022-Dec-29

COVID-19, Mortality prediction, Recurrent Neural Network, Time series

Public Health Public Health

Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.

In BMJ health & care informatics

OBJECTIVES : The objective of this study is the implementation of an automatic procedure to weekly detect new SARS-CoV-2 variants and non-neutral variants (variants of concern (VOC) and variants of interest (VOI)).

METHODS : We downloaded spike protein primary sequences from the public resource GISAID and we represented each sequence as k-mer counts. For each week since 1 July 2020, we evaluate if each sequence represents an anomaly based on a One Class support vector machine (SVM) classification algorithm trained on neutral protein sequences collected from February to June 2020.

RESULTS : We assess the ability of the One Class classifier to detect known VOC and VOI, such as Alpha, Delta or Omicron, ahead of their official classification by health authorities. In median, the classifier predicts a non-neutral variant as outlier 10 weeks before the official date of designation as VOC/VOI.

DISCUSSION : The identification of non-neutral variants during a pandemic usually relies on indicators available during time, such as changing population size of a variant. Automatic variant surveillance systems based on protein sequences can enhance the fast identification of variants of potential concern.

CONCLUSION : Machine learning, and in particular One Class SVM classification, can support the detection of potentially VOC/VOI variants during an evolving pandemics.

Nicora Giovanna, Salemi Marco, Marini Simone, Bellazzi Riccardo

2022-Dec

COVID-19, data science, machine learning, public health informatics

General General

Fitness Dependent Optimizer with Neural Networks for COVID-19 patients.

In Computer methods and programs in biomedicine update

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

Abdulkhaleq Maryam T, Rashid Tarik A, Hassan Bryar A, Alsadoon Abeer, Bacanin Nebojsa, Chhabra Amit, Vimal S

2022-Dec-27

COVID 19, FDO, Fitness Dependent Optimizer, Machine Learning, Swarm Intelligence

General General

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images.

In Annals of operations research

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.

Gülmez Burak

2022-Dec-25

COVID-19, Convolutional neural network, Deep learning, Genetic algorithm, Xception

General General

Multi-Objective deep learning framework for COVID-19 dataset problems.

In Journal of King Saud University. Science

BACKGROUND : It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes.

METHODS : This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN).

RESULTS : The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%.

CONCLUSIONS : The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.

Mohammedqasem Roa’a, Mohammedqasim Hayder, Asad Ali Biabani Sardar, Ata Oguz, Alomary Mohammad N, Almehmadi Mazen, Amer Alsairi Ahad, Azam Ansari Mohammad

2022-Dec-28

Artificial intelligence, COVID-19, Deep learning, Hyperparameter optimization, Missing value

General General

Development of CNN-LSTM combinational architecture for COVID-19 detection.

In Journal of ambient intelligence and humanized computing

The world has been under extreme pressure due to the spread of the coronavirus. The urgency to eradicate the virus has caused distress amongst civilians and medical agencies to an equal extent. Due to anomalies observed in the results from reverse transcription-polymerase chain reaction (RTPCR) tests, more reliable options like computed tomography (CT) scan-based tests are being researched upon. In this paper, a novel combinational architecture is built upon the principles of Convolution Neural Networks (CNN) and Long Short Term Memory (LSTM) Networks to detect COVID-19 virus. This method uses chest X-ray images as inputs to combinational architecture for the classification of samples. The CNN part of the network will be used to extract features that help in the classification, and the LSTM part will be used for classification based on the extracted features. A total of 8 convolutional layers and 4 pooling layers are used for CNN and 4 LSTM layers of 64 and 128 cells respectively. Instead of the sigmoid function, a rectified linear unit function is used as an activation function. This provides non-linearity to the CNN and better accuracies in comparison. The proposed model employs a padding layer to prevent the loss of information. Accuracy, loss, F1 score, and Matthew's Correlation Coefficient (MCC) are calculated to analyse the effectiveness of the proposed architecture. The proposed model is validated using a relatively larger dataset of 7292 images. The combinational architecture provides a more informative and truthful result in the evaluation of classification as it caters to both the size of positive elements and negative elements in the dataset. The proposed CNN-LSTM model gives an accuracy of 98.91% and an MCC value of 97.84% respectively. The model is also compared with models employing transfer learning methods for similar applications.

Narula Abhinav, Vaegae Naveen Kumar

2022-Dec-24

COVID-19, Chest X-ray, Convolution neural network, Deep learning, Image processing, LSTM

General General

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies.

In Complex & intelligent systems

Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook's Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

Ghosh Indranil, Dragan Pamucar

2022-Dec-26

Ensemble empirical mode decomposition, Explainable artificial intelligence, Facebook’s prophet algorithm, Financial stress, Long short-term memory network, Technical indicators

General General

Cough Audio Analysis for COVID-19 Diagnosis.

In SN computer science

Humanity has suffered catastrophically due to the COVID-19 pandemic. One of the most reliable diagnoses of COVID-19 is RT-PCR (reverse-transcription polymer chain reaction) testing. This method, however, has its limitations. It is time consuming and requires scalability. This research work carries out a preliminary prognosis of COVID-19, which is scalable and less time consuming. The research carried out a competitive analysis of four machine-learning models namely, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory, and VGG-19 with Support Vector Machines. Out of these models, Multilayer Perceptron outperformed with higher specificity of 94.5% and accuracy of 96.8%. The results show that Multilayer Perceptron was able to distinguish between positive and negative COVID-19 coughs by a robust feature embedding technique.

Kapoor Teghdeep, Pandhi Tanya, Gupta Bharat

2023

CNN, COVID-19, COVID-19 preliminary diagnosis, Cough diagnosis, Deep learning, LSTM, MLP, Machine learning, RNN, SVM

General General

Think Twice: First for Tech, Then for Ed.

In SN computer science

The embodiment of technology in education can make learning easier, more enjoyable, and more accessible. From Learning Machines to artificial intelligence (AI), educational technology has repeatedly tested its strength as an aider or a substitute to in-person teaching. During the COVID-19 pandemic international organisations promoted the idea of the transformation of education using technology. Comparison of their texts published in 2020 with texts published in 2021 indicates that much of the early enthusiasm concerning the transition from in-person to remote learning gave its position to more thoughtful accounts after considering the learning losses and students' disappointment from the disruption of in-person relationships. This publication highlights aspects of education technology usually overlooked in futuristic accounts of education. Adopting a non-deterministic view of technology attempts to contribute to the more human-centred incorporation of technologies in education.

Photopoulos Panos, Triantis Dimos

2023

Blended learning, COVID-19, Face-to-face education, Online learning, Technological determinism, Technology driven change

General General

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.

In Journal of scientific computing

We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

Silva Vinicius L S, Heaney Claire E, Li Yaqi, Pain Christopher C

2023

COVID-19, Compartmental model, Data assimilation, Deep learning, Epidemiology, Generative adversarial networks, Reduced-order model, Spatio-temporal prediction

General General

Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.

In International journal of biomedical imaging

This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.

Islam Rumana, Tarique Mohammed

2022

General General

De novo design of site-specific protein interactions with learned surface fingerprints

bioRxiv Preprint

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Gainza, P.; Wehrle, S.; Van Hall-Beauvais, A.; Marchand, A.; Scheck, A.; Harteveld, Z.; Ni, D.; Tan, S.; Sverrisson, F.; Goverde, C.; Turelli, P.; Raclot, C.; Teslenko, A.; Pacesa, M.; Rosset, S.; Buckley, S.; Georgeon, S.; Marsden, J.; Petruzzella, A.; Liu, K.; Xu, Z.; Chai, Y.; Han, P.; Gao, G. F.; Oricchio, E.; Fierz, B.; Trono, D.; Stahlberg, H.; Bronstein, M.; Correia, B. E.

2023-01-03

General General

Biosensors - A Miraculous Detecting Tool in Combating the War against COVID-19.

In Current pharmaceutical biotechnology

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly known as COVID-19, created rack and ruin and erupted as a global epidemic. Nearly 482.3 million cases and approximately 6.1 million deaths have been reported. The World Health Organization (WHO) designated it an international medical emergency on January 30, 2020; shortly in March 2020, it was declared a pandemic. To address this situation, governments and scientists around the globe were urged to combat and prevent its spread, mainly when no treatment was available. Presently, quantitative real-time polymerase chain reaction (qRT-PCR) is the most widely utilized technique for diagnosing SARS-CoV-2. But this method is cumbersome, tedious, and might not be quickly accessible in isolated areas with a circumscribed budget. Therefore, there is a quest for novel diagnostic techniques which can diagnose the disease in a lesser time in an economical way. This paper outlines the potential of biosensors in the diagnosis of SARS-CoV-2. This review highlights the current state of presently available detection techniques, expected potential limits, and the benefits of biosensor-implicated tests against SARS-Cov-2 diagnosis. CRISPR-Cas9 implanted paper strip, field-effect transistor (FET) implanted sensor, nucleic-acid centric, aptamers-implanted biosensor, antigen-Au/Ag nanoparticles-based electrochemical biosensor, surface-enhanced Raman scattering (SERS)-based biosensor, Surface Plasmon Resonance, potential electrochemical biosensor, optical biosensor, as well as artificial intelligence (AI) are some of the novel biosensing devices that are being utilized in the prognosis of coronaviruses.

Deshmukh Rohitas, Mishra Sakshi, Singh Rajesh

2023-Jan-02

Biosensors, COVID-19, CRISPR-Cas9., SARS-CoV-2, Virus, respiratory, syndrome

Cardiology Cardiology

Did Australia's COVID-19 restrictions impact statin incidence, prevalence or adherence?

In Current problems in cardiology

OBJECTIVE : COVID-19 restrictions may have an unintended consequence of limiting access to cardiovascular care. Australia implemented adaptive interventions (e.g. telehealth consultations, digital image prescriptions, continued dispensing, medication delivery) to maintain medication access. This study investigated whether COVID-19 restrictions in different jurisdictions coincided with changes in statin incidence, prevalence and adherence.

METHODS : Analysis of a 10% random sample of national medication claims data from January 2018 to December 2020 was conducted across three Australian jurisdictions. Weekly incidence and prevalence were estimated by dividing the number statin initiations and any statin dispensing by the Australian population aged 18-99 years. Statin adherence was analysed across the jurisdictions and years, with adherence categorised as <40%, 40-79% and ≥80% based on dispensings per calendar year.

RESULTS : Overall, 309,123, 315,703 and 324,906 people were dispensed and 39029, 39816, and 44979 initiated statins in 2018, 2019 and 2020 respectively. Two waves of COVID-19 restrictions in 2020 coincided with no meaningful change in statin incidence or prevalence per week when compared to 2018 and 2019. Incidence increased 0.3% from 23.7 to 26.2 per 1000 people across jurisdictions in 2020 compared to 2019. Prevalence increased 0.14% from 158.5 to 159.9 per 1000 people across jurisdictions in 2020 compared to 2019. The proportion of adults with ≥80% adherence increased by 3.3% in Victoria, 1.4% in NSW and 1.8% in other states and territories between 2019 and 2020.

CONCLUSIONS : COVID-19 restrictions did not coincide with meaningful changes in the incidence, prevalence or adherence to statins suggesting adaptive interventions succeeded in maintaining access to cardiovascular medications.

Livori Adam C, Lukose Dickson, Bell J Simon, Webb Geoffrey I, Ilomäki Jenni

2022-Dec-28

Statin, cardiology, cardiovascular, drug utilisation, medication adherence

General General

Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns.

METHODS : Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD.

RESULTS : A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02).

LIMITATIONS : The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data.

CONCLUSIONS : The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Wasserzug Yael, Degani Yoav, Bar-Shaked Mili, Binyamin Milana, Klein Amit, Hershko Shani, Levkovitch Yechiel

2022-Dec-28

Depression screening, Machine learning, Predictive analytics, Remote patient monitoring, Speech prosody, Voice analysis

Internal Medicine Internal Medicine

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices.

In Clinical immunology (Orlando, Fla.)

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Asteris Panagiotis G, Kokoris Styliani, Gavriilaki Eleni, Tsoukalas Markos Z, Houpas Panagiotis, Paneta Maria, Koutzas Andreas, Argyropoulos Theodoros, Alkayem Nizar Faisal, Armaghani Danial J, Bardhan Abidhan, Cavaleri Liborio, Cao Maosen, Mansouri Iman, Mohammed Ahmed Salih, Samui Pijush, Gerber Gloria, Boumpas Dimitrios T, Tsantes Argyrios, Terpos Evangelos, Dimopoulos Meletios A

2022-Dec-28

Artificial intelligence, Artificial neural networks, COVID-19, Laboratory indices, SARS-CoV2

Public Health Public Health

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

In BMC medical research methodology

BACKGROUND : The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients.

METHODS : This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models.

RESULTS : Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features.

CONCLUSIONS : Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

Moslehi Samad, Mahjub Hossein, Farhadian Maryam, Soltanian Ali Reza, Mamani Mojgan

2022-Dec-31

COVID-19, Feature selection, Generalized neural additive, Laboratory markers, Machine learning, Prediction

General General

Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach.

In PloS one ; h5-index 176.0

The COVID-19 pandemic has presented unprecedented challenges for university students, creating uncertainties for their academic careers, social lives, and mental health. Our study utilized a machine learning approach to examine the degree to which students' college adjustment and coping styles impacted their adjustment to COVID-19 disruptions. More specifically, we developed predictive models to distinguish between well-adjusted and not well-adjusted students in each of five psychological domains: academic adjustment, emotionality adjustment, social support adjustment, general COVID-19 regulations response, and discriminatory impact. The predictive features used for these models are students' individual characteristics in three psychological domains, i.e., Ways of Coping (WAYS), Adaptation to College (SACQ), and Perceived Stress Scale (PSS), assessed using established commercial and open-access questionnaires. We based our study on a proprietary survey dataset collected from 517 U.S. students during the initial peak of the pandemic. Our models achieved an average of 0.91 AUC score over the five domains. Using the SHAP method, we further identified the most relevant risk factors associated with each classification task. The findings reveal the relationship of students' general adaptation to college and coping in relation to their adjustment during COVID-19. Our results could help universities identify systemic and individualized strategies to support their students in coping with stress and to facilitate students' college adjustment in this era of challenges and uncertainties.

Zhao Yijun, Ding Yi, Chekired Hayet, Wu Ying

2022

General General

Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States.

In Quality management in health care

BACKGROUND AND OBJECTIVES : Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States.

METHODS : A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool.

RESULTS : Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing.

CONCLUSION : Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

Schilling Josh, Moeller F Gerard, Peterson Rachele, Beltz Brandon, Joshi Deepti, Gartner Danielle, Vang Jee, Jain Praduman

Radiology Radiology

COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence.

In Cureus

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs.

MATERIALS AND METHODS : Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values.

RESULTS : A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78).

CONCLUSION : Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

Baruah Dhiraj, Runge Louis, Jones Richard H, Collins Heather R, Kabakus Ismail M, McBee Morgan P

2022-Nov

artificial intelligence, chest radiography, covid, receiver operating characteristic (roc) analysis, rt-pcr

General General

The role of artificial intelligence technology in the care of diabetic foot ulcers: the past, the present, and the future.

In World journal of diabetes ; h5-index 53.0

Foot ulcers are common complications of diabetes mellitus and substantially increase the morbidity and mortality due to this disease. Wound care by regular monitoring of the progress of healing with clinical review of the ulcers, dressing changes, appropriate antibiotic therapy for infection and proper offloading of the ulcer are the cornerstones of the management of foot ulcers. Assessing the progress of foot ulcers can be a challenge for the clinician and patient due to logistic issues such as regular attendance in the clinic. Foot clinics are often busy and because of manpower issues, ulcer reviews can be delayed with detrimental effects on the healing as a result of a lack of appropriate and timely changes in management. Wound photographs have been historically useful to assess the progress of diabetic foot ulcers over the past few decades. Mobile phones with digital cameras have recently revolutionized the capture of foot ulcer images. Patients can send ulcer photographs to diabetes care professionals electronically for remote monitoring, largely avoiding the logistics of patient transport to clinics with a reduction on clinic pressures. Artificial intelligence-based technologies have been developed in recent years to improve this remote monitoring of diabetic foot ulcers with the use of mobile apps. This is expected to make a huge impact on diabetic foot ulcer care with further research and development of more accurate and scientific technologies in future. This clinical update review aims to compile evidence on this hot topic to empower clinicians with the latest developments in the field.

Pappachan Joseph M, Cassidy Bill, Fernandez Cornelius James, Chandrabalan Vishnu, Yap Moi Hoon

2022-Dec-15

Artificial intelligence technology, COVID-19 pandemic, Diabetic foot ulcers, Digital photography, Mobile app, Photographic monitoring

General General

What factors can support students' deep learning in the online environment: The mediating role of learning self-efficacy and positive academic emotions?

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : In 2020, COVID-19 forced higher education institutions in many countries to turn to online distance learning. The trend of using online education has accelerated across the world. However, this change in the teaching mode has led to the decline of students' online learning quality and resulted in students being unable to do deep learning. Therefore, the current research, aimed at promoting deep learning in the online environment, constructed a theoretical model with learning self-efficacy and positive academic emotions as mediators, deep learning as the dependent variable, perceived TPACK support, peer support, technical usefulness, and ease of use as independent variables.

METHODS : The theoretical model was verified by SPSS26.0 and smartPLS3.0, and to assess the measurement and structural models, the PLS approach to structural equation modeling (SEM) was performed.

RESULTS : The study found that (a) positive academic emotions play a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning; (b) learning self-efficacy plays a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning.

DISCUSSION : The findings of this study fill the gaps in the research on the theoretical models of deep learning in the online environment and provide a theoretical basis for online teaching, learning quality, and practical improvement strategies.

Zhao Jingxian, Liu Enyun

2022

deep learning, learning self-efficacy, perceived TPACK support, perceived peer support, perceived technical usefulness and ease of use, positive academic emotions

General General

A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2.

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

BACKGROUND : This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data.

METHODS : This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models.

RESULTS : We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation.

CONCLUSIONS : The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.

Bottrighi Alessio, Pennisi Marzio, Roveta Annalisa, Massarino Costanza, Cassinari Antonella, Betti Marta, Bolgeo Tatiana, Bertolotti Marinella, Rava Emanuele, Maconi Antonio

2022-Dec-28

COVID-19, Explainability, Machine learning, Patient risk prediction

General General

Emerging Dominant SARS-CoV-2 Variants.

In Journal of chemical information and modeling

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants, Omicron (BA.1), BA.2, and BA.4/BA.5, were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. On the basis of newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BP.1, BL*, BA.2.75*, BQ.1*, and particularly BN.1* have a high potential to become the new dominant variants to drive the next surge. Our key projection about these variants dominance made on Oct. 18, 2022 (see arXiv:2210.09485) became reality in late November 2022.

Chen Jiahui, Wang Rui, Hozumi Yuta, Liu Gengzhuo, Qiu Yuchi, Wei Xiaoqi, Wei Guo-Wei

2022-Dec-28

General General

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

In GigaScience

BACKGROUND : Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade.

RESULTS : In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants.

CONCLUSIONS : By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants.

AVAILABILITY : The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

Avila Cartes Jorge, Anand Santosh, Ciccolella Simone, Bonizzoni Paola, Della Vedova Gianluca

2022-Dec-28

, GISAID clades, SARS-CoV-2, chaos game representation, classification of genome sequences, convolutional neural networks, deep learning

General General

Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation.

MATERIAL AND METHODS : We included COVID-19 patients admitted to intensive care units for >24 hours from March 2020 to October 2021, divided into training and testing development and testing only holdout cohorts. We developed ECMO-deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0-48 hours, compared to PaO2/FiO2 (PF) ratio, Sequential Organ Failure Assessment (SOFA) score, PREdiction of Survival on ECMO Therapy-Score (PRESET) score, logistic regression (LR), and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics.

RESULTS : ECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-hour prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO had the highest AUROC (0.94 & 0.95) and AUPRC (0.54 & 0.37) in development and holdout cohorts in identifying ECMO patients without data 18-hours prior to ECMO.

DISCUSSION AND CONCLUSION : We developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multi-center validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.

Xue Bing, Shah Neel, Yang Hanqing, Kannampallil Thomas, Payne Philip Richard Orrin, Lu Chenyang, Said Ahmed Sameh

2022-Dec-28

COVID-19, ECMO, early alert, machine learning, prediction, resource allocation

General General

Towards a soft three-level voting model (Soft T-LVM) for fake news detection.

In Journal of intelligent information systems

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

Jlifi Boutheina, Sakrani Chayma, Duvallet Claude

2022-Dec-23

COVID-19., Ensemble learning models, Fake news detection, Machine learning algorithms, Natural Language Processing (NLP), Social media

General General

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19.

In Neural processing letters

COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is fine-tuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets.

Kour Harnain, Gupta Manoj K

2022-Dec-23

BERT, COVID-19, LSTM, Sentiment analysis, Transfer learning, Tweets

General General

Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space.

In Earth science informatics

Spectroscopy is a methodology for gaining knowledge of particles, especially biomolecules, by quantifying the interactions between matter and light. By examining the level of light absorbed, reflected or released by a specimen, its constituents, properties, and volume can be determined. Spectra obtained through spectroscopy procedures are quick, harmless and contactless; hence nowadays preferred in chemometrics. Due to the high dimensional nature of the spectra, it is challenging to build a robust classifier with good performance metrics. Many linear and nonlinear dimensionality reduction-based classification models have been previously implemented to overcome this issue. However, they lack in capturing the subtle details of the spectra into the low dimension space or cannot efficiently handle the nonlinearity present in the spectral data. We propose a graph-based neural network embedding approach to extract appropriate features into latent space and circumvent the spectrums' nonlinearity problem. Our approach performs dimensionality reduction into two phases: constructing a nearest neighbor graph and producing almost linear embedding using a fully connected neural network. Further, the low dimensional embedding is subjected to classification using the Random Forest algorithm. In this paper, we have implemented and compared our technique with four nonlinear dimensionality techniques widely used for spectral data analysis. In this study, we have considered five different spectral datasets belonging to specific applications. The various classification performance metrics of all the techniques are evaluated. The proposed approach is able to perform competitively well on six different low-dimensional spaces for each dataset with an accuracy score above 95% and Matthew's correlation coefficient value close to 1. The trustworthiness score of almost 1 show that the presented dimensionality reduction approach preserves the closest neighbor structure of high dimensional spectral inputs into latent space.

Yousuff Mohamed, Babu Rajasekhara

2022-Dec-23

COVID-19, Chemometrics, Dimensionality reduction, Machine learning, Random Forest, Spectroscopy

General General

A robust deep learning platform to predict CD8+ T-cell epitopes

bioRxiv Preprint

T-cells play a crucial role in the adaptive immune system by inducing an anti-tumour response, defending against pathogens, and maintaining tolerance against self-antigens, which has sparked interest in the development of T-cell-based vaccines and immunotherapies. Because screening antigens driving the T-cell response is currently low-throughput and laborious, computational methods for predicting CD8+ T-cell epitopes have emerged. However, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8+ T-cell epitopes. Therefore, we developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning platform for predicting CD8+ T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8+ T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to dissimilarity to self from cancer studies. We used TRAP to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. Thus, this study presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

Lee, C. H.-J.; Huh, J.; Buckley, P. R.; Jang, M. J.; Pereira Pinho, M.; Fernandes, R.; Antanaviciute, A.; Simmons, A.; Koohy, H.

2022-12-29

Public Health Public Health

Prevalence of Asymptomatic SARS-CoV-2 Infection in Japan.

In JAMA network open

IMPORTANCE : Real-world evidence of SARS-CoV-2 transmission is needed to understand the prevalence of infection in the Japanese population.

OBJECTIVE : To conduct sentinel screening of the Japanese population to determine the prevalence of SARS-CoV-2 infection in asymptomatic individuals, with complementary analysis for symptomatic patients as reported by active epidemiologic surveillance used by the government.

DESIGN, SETTING, AND PARTICIPANTS : This cross-sectional study of a sentinel screening program investigated approximately 1 million asymptomatic individuals with polymerase chain reaction (PCR) testing for SARS-CoV-2 infection between February 22 and December 8, 2021. Participants included children, students, employed adults, and older individuals, as well as volunteers to broadly reflect the general Japanese population in the 14 prefectures of Japan that declared a state of emergency. Saliva samples and a cycle threshold (Ct) value of approximately 40 as standard in Japan were used. Polymerase chain reaction testing for symptomatic patients was separately done by public health authorities, and the results were obtained from the Ministry of Health, Labour, and Welfare of Japan to complement data on asymptomatic infections from the present study.

MAIN OUTCOMES AND MEASURES : Temporal trends in positivity and prevalence (including surges of different variants) and demographic associations (eg, age, geographic location, and vaccination status) were assessed.

RESULTS : The positive rate of SARS-CoV-2 infection in 1 082 976 asymptomatic individuals (52.08% males; mean [SD] age 39.4 [15.7] years) was 0.03% (95% CI, 0.02%-0.05%) during periods without surges and a maximum of 0.33% (95% CI, 0.25%-0.43%) during peak surges at the Japanese standard Ct value of approximately 40; however, the positive rate would have been 10-fold less at a Ct value of 25 as used elsewhere in the world (eg, UK). There was an increase in patients with a positive PCR test result with a Ct value of 25 or 30 preceding surges in infection and hotspots of asymptomatic infections.

CONCLUSIONS AND RELEVANCE : In this cross-sectional study of asymptomatic SARS-CoV-2 infection in the general population of Japan in 2021, as investigated by sentinel surveillance, a low rate of infection was seen in the Japanese population compared with reported levels elsewhere in the world. This finding provides real-world data on the state of infection in Japan.

Suzuki Toru, Aizawa Kenichi, Shibuya Kenji, Yamanaka Shinya, Anzai Yuichiro, Kurokawa Kiyoshi, Nagai Ryozo

2022-Dec-01

Public Health Public Health

Depression and anxiety on Twitter during the COVID-19 stay-at-home period in seven major US cities.

In AJPM focus

INTRODUCTION : While surveys are a well-established instrument to capture population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders due to COVID-19, and anxiety and depression in seven major US cities utilizing Twitter data.

METHODS : We collected 18 million Tweets from January to September 2019 (baseline), and 2020 from seven US cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine-learning-based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google trends data using search query frequencies. A qualitative evaluation of trends is presented.

RESULTS : Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all seven locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer, and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual States. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results.

CONCLUSIONS : Our study demonstrates feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

Levanti Danielle, Monastero Rebecca N, Zamani Mohammadzaman, Eichstaedt Johannes C, Giorgi Salvatore, Schwartz H Andrew, Meliker Jaymie R

2022-Dec-22

coronavirus, mental health, social media, stay-at-home order

Radiology Radiology

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

In IEEE transactions on technology and society

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.

Allahabadi Himanshi, Amann Julia, Balot Isabelle, Beretta Andrea, Binkley Charles, Bozenhard Jonas, Bruneault Frederick, Brusseau James, Candemir Sema, Cappellini Luca Alessandro, Chakraborty Subrata, Cherciu Nicoleta, Cociancig Christina, Coffee Megan, Ek Irene, Espinosa-Leal Leonardo, Farina Davide, Fieux-Castagnet Genevieve, Frauenfelder Thomas, Gallucci Alessio, Giuliani Guya, Golda Adam, van Halem Irmhild, Hildt Elisabeth, Holm Sune, Kararigas Georgios, Krier Sebastien A, Kuhne Ulrich, Lizzi Francesca, Madai Vince I, Markus Aniek F, Masis Serg, Mathez Emilie Wiinblad, Mureddu Francesco, Neri Emanuele, Osika Walter, Ozols Matiss, Panigutti Cecilia, Parent Brendan, Pratesi Francesca, Moreno-Sanchez Pedro A, Sartor Giovanni, Savardi Mattia, Signoroni Alberto, Sormunen Hanna-Maria, Spezzatti Andy, Srivastava Adarsh, Stephansen Annette F, Theng Lau Bee, Tithi Jesmin Jahan, Tuominen Jarno, Umbrello Steven, Vaccher Filippo, Vetter Dennis, Westerlund Magnus, Wurth Renee, Zicari Roberto V

2022-Dec

Artificial intelligence, COVID-19, Z-Inspection®, case study, ethical tradeoff, ethics, explainable AI, healthcare, pandemic, radiology, trust, trustworthy AI

General General

Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression.

In Biomedical signal processing and control

Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data.

You Mingyu, Wang Weihao, Li You, Liu Jiaming, Xu Xianghuai, Qiu Zhongmin

2022-Feb

BiLSTM, Boundary regression, C-BiLSTM, Cough detection, Deep learning

General General

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach.

In Energy (Oxford, England)

The COVID-19 pandemic broke the balance of oil supply and demand. Meeting these oil market challenges induced by the pandemic required a more accurate assessment of the impact of the pandemic on oil consumption. The existing measurement of the impact of the pandemic on oil consumption was based on year-over-year calculation. In this work, a new measurement approach based on a comparison of simulated and actual oil consumption was proposed. In this proposed measurement model, the actual oil consumption was from the official statistics, whereas the simulated oil demand came from business-as-usual (without pandemic) scenario simulation. In order to reduce the simulation error, three hybrid simulation approaches were developed by combining the simulation technique and machine learning technique. The mean relative errors of the proposed simulation approaches were between 1.08% and 2.51%, within the high precision level. An empirical research on the US oil consumption was conducted by running the proposed measurement model. Through analyzing the difference between the simulated and real US oil consumption, we found the impact of the epidemic on U.S. oil consumption was obvious in April-May 2020 and January-February 2021. At its worst, the oil decline in the United States reached 973 trillion British thermal units, compared to the state without the epidemic. During the entire survey period (January 2020-March 2021), the US oil consumption under the epidemic was about 18.14% lower than that under the normal epidemic-free situation, which was 5% higher than the 13% inter-annual decline rate reported. This work contributed to understand the impact of the pandemic on oil consumption more comprehensively, and also provided a new approach for analyzing the impact of the pandemic on energy consumption.

Wang Qiang, Li Shuyu, Zhang Min, Li Rongrong

2022-Jan-15

COVID-19, Pandemic-free scenario, Simulation, U.S. petroleum

General General

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance.

In Journal of air transport management

One of the purposes of Artificial Intelligence tools is to ease the analysis of large amounts of data. In order to support the strategic decision-making process of the airlines, this paper proposes a Data Mining approach (focused on visualization) with the objective of extracting market knowledge from any database of industry players or competitors. The method combines two clustering techniques (Self-Organizing Maps, SOMs, and K-means) via unsupervised learning with promising dynamic applications in different sectors. As a case study, 30-year data from 18 diverse US passenger airlines is used to showcase the capabilities of this tool including the identification and assessment of market trends, M&A events or the COVID-19 consequences.

Pérez-Campuzano Darío, Rubio Andrada Luis, Morcillo Ortega Patricio, López-Lázaro Antonio

2022-Jun

Airlines, COVID-19, Data mining (DM), K-means, Self-organizing map (SOM), Unsupervised learning

General General

Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.

In Ecological informatics

In this study, mean monthly and diurnal variations in fine particulate matters (PM2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NOx, O3, nitrate (NO3 -), and sulfate (SO4 2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NOx concentration due to a decrease in traffic flow under the NOx-saturated regime was observed to enhance the secondary NO3 - and O3 formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NOx, O3, NO3 -, and SO4 2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH3, HNO3, and H2SO4, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Lin Guan-Yu, Chen Wei-Yea, Chieh Shao-Heng, Yang Yi-Tsung

2022-Jul

And SO42− prediction, Artificial neural network, Level 3 COVID-19 alert, Meteorological effect factors, NO3−, NOx, O3

General General

Covid-19 Diagnosis by WE-SAJ.

In Systems science & control engineering

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

Wang Wei, Zhang Xin, Wang Shui-Hua, Zhang Yu-Dong

2022-Dec-31

COVID-19, Deep Learning, Diagnosis, Jaya, Self-adaptive Jaya, Wavelet Entropy

General General

Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread.

In Computers & industrial engineering

Deep Neural Networks (DNN) form a powerful deep learning model that can process unprecedented volumes of data. The hyperparameters of DNN have a significant influence on its prediction performance. Evolutionary algorithms (EAs) form a heuristic-based approach that provides an opportunity to optimize deep learning models to obtain good performance. Therefore, we propose an evolutionary deep learning model called IPSO-DNN based on DNN for prediction and an improved Particle Swarm Optimization (IPSO) algorithm to optimize the kernel hyperparameters of DNN in a self-adaptive evolutionary way. In the IPSO algorithm, a micro population size setting is introduced to improve the search efficiency of the algorithm, and the generalized opposition-based learning strategy is used to guide the population evolution. In addition, the IPSO algorithm employs a self-adaptive update strategy to prevent premature convergence and then improves the exploitation and exploration parameter optimization performance of DNN. In this paper, we show that the IPSO algorithm provides an efficient approach for tuning the hyperparameters of DNN with saving valuable computational resources. We explore the proposed IPSO-DNN model to predict the effect of social distancing on the spread of COVID-19 based on the social distancing metrics. The preliminary experimental results reveal that the proposed IPSO-DNN model has the least computation cost and yields better prediction accuracy results when compared to the other models. The experiments of the IPSO-DNN model also illustrate that aggressive and extensive social distancing interventions are crucial to help flatten the COVID-19 epidemic curve in the United States.

Liu Dixizi, Ding Weiping, Dong Zhijie Sasha, Pedrycz Witold

2022-Apr

COVID-19 Pandemic, Deep Neural Network, Evolutionary Algorithm, Generalized Opposition-Based Learning, Particle Swarm Optimization, Social Distancing

General General

Modeling the social influence of COVID-19 via personalized propagation with deep learning.

In World wide web

Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to analyze the proposed algorithm's efficiency and effectiveness. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

Liu Yufei, Cao Jie, Wu Jia, Pi Dechang

2022-Dec-17

COVID-19, Deep learning, Personalized propagation, Social influence

General General

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods.

In Applied energy ; h5-index 131.0

Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%-25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector.

Şahin Utkucan, Ballı Serkan, Chen Yan

2021-Nov-15

Covid-19, Electricity generation, Forecasting, Fractional grey model, Machine learning, Seasonal fluctuations

General General

A physiological signal compression approach using optimized Spindle Convolutional Auto-encoder in mHealth applications.

In Biomedical signal processing and control

BACKGROUND AND OBJECTIVES : The COVID-19 pandemic manifested the need of developing robust digital platforms for facilitating healthcare services such as consultancy, clinical therapies, real time remote monitoring, early diagnosis and future predictions. Innovations made using technologies such as Internet of Things (IoT), edge computing, cloud computing and artificial intelligence are helping address this crisis. The urge for remote monitoring, symptom analysis and early detection of diseases lead to tremendous increase in the deployment of wearable sensor devices. They facilitate seamless gathering of physiological data such as electrocardiogram (ECG) signals, respiration traces (RESP), galvanic skin response (GSR), pulse rate, body temperature, photoplethysmograms (PPG), oxygen saturation (SpO2) etc. For diagnosis and analysis purpose, the gathered data needs to be stored. Wearable devices operate on batteries and have a memory constraint. In mHealth application architectures, this gathered data is hence stored on cloud based servers. While transmitting data from wearable devices to cloud servers via edge devices, a lot of energy is consumed. This paper proposes a deep learning based compression model SCAElite that reduces the data volume, enabling energy efficient transmission.

RESULTS : Stress Recognition in Automobile Drivers dataset and MIT-BIH dataset from PhysioNet are used for validation of algorithm performance. The model achieves a compression ratio of up to 300 fold with reconstruction errors within 8% over the stress recognition dataset and 106.34-fold with reconstruction errors within 8% over the MIT-BIH dataset. The computational complexity of SCAElite is 51.65% less compared to state-of-the-art deep compressive model.

CONCLUSION : It is experimentally validated that SCAElite guarantees a high compression ratio with good quality restoration capabilities for physiological signal compression in mHealth applications. It has a compact architecture and is computationally more efficient compared to state-of-the-art deep compressive model.

Barot Vishal, Patel Dr Ritesh

2022-Mar

Data compression, Energy efficiency, Physiological signal compression, Spindle Convolutional Auto-encoder, mHealth applications

General General

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

bioRxiv Preprint

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. Here we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction. The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1,046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Chen, J.; Kuhn, L. A.; Raschka, S.

2022-12-27

General General

A comparative bibliometric analysis of Omicron and Delta variants during the COVID-19 pandemic.

In Annals of palliative medicine ; h5-index 20.0

BACKGROUND : To compare the research hotspots of infections with the Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic and to identify future research trends.

METHODS : Studies about Delta and Omicron variant infections published over the last 3 years were retrieved from the Web of Science (WoS) database. A comparative bibliometric analysis was conducted through machine learning and visualization tools, including VOSviewer, Bibliographic Item Co-Occurrence Matrix Builder, and Graphical Clustering Toolkit. Research hotspots and trends in the field were analyzed, and the contributions and collaborations of countries, institutions, and authors were documented. A cross-sectional analysis of the relevant studies registered at ClinicalTrials.gov was also performed to clarify the direction of future research.

RESULTS : A total of 1,787 articles distributed in 107 countries and 374 publications from 77 countries focused on the Delta and Omicron variants were included in our bibliometric analysis. The top five productive countries in both variants were the USA, China, the UK, India, and Germany. In 5,999 and 1,107 keywords identified from articles on the Delta and Omicron, the top two frequent keywords were the same: "COVID-19" (occurrence: 713, total link strength: 1,525 in Delta; occurrence: 137, total link strength: 354 in Omicron), followed by "SARS-CoV-2" (occurrence: 553, total link strength: 1,478 in Delta; occurrences 132, total link strength: 395 in Omicron). Five theme clusters from articles on Delta variant were identified: transmission, molecular structure, activation mode, epidemiology, and co-infection. While other three theme clusters were recognized for the Omicron variant: vaccine, human immune response, and infection control. Meanwhile, 21 interventional studies had been registered up to April 2022, most of which aimed to evaluate the immunogenicity and safety of different kinds of vaccines in various populations.

CONCLUSIONS : Publications and clinical trials related to COVID-19 increased annually. As the first comparative bibliometric analysis for Delta and Omicron variants, we noticed that the relevant research trends have shifted from vaccine development to infection control and management of complications. The ongoing clinical studies will verify the safety and efficacy of promising drugs.

Liu Yang-Xi, Wang Li-Hui, Zhu Cheng, Zha Qiong-Fang, Yu Yue-Tian

2022-Dec-14

Bibliometric analysis, Delta variant, Omicron variant, coronavirus disease (COVID), research trends

General General

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.

In Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

Wang Wei, Pei Yanrong, Wang Shui-Hua, Gorrz Juan Manuel, Zhang Yu-Dong

2023

COVID-19, Convolutional Neural Network, Hyperparameters Tuning, Particle Swarm Optimisation, SARS-CoV-2

General General

Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images.

In Expert systems with applications

The COVID-19 pandemic has been affecting the world since December 2019, and nowadays, the number of infected is increasing rapidly. Chest X-ray images are clinical adjuncts that can be used in the diagnosis of COVID-19 disease. Because of the rapid spread of COVID-19 disease worldwide and the limited number of expert radiologists, the proposed method uses the automatic diagnosis method rather than a manual diagnosis method. In the paper, COVID-19 Positive/Negative (2275 Positive, 4626 Negative) and Normal/Pneumonia (2313 Normal, 2313 Pneumonia) are diagnosed using chest X-ray images. Herein, 80 % and 20 % of the images are used in the training and validation set, respectively. In the proposed method, six different classifiers are trained using chest X-ray images, and the five most successful classifiers are used in both phases. In Phase-1 and Phase-2, image features are extracted using the Bag of Features method for Cosine K-Nearest Neighbor (KNN), Linear Discriminant, Logistic Regression, Bagged Trees Ensemble, Medium Gaussian Support Vector Machine (SVM), excluding SqueezeNet Deep Learning (K = 2000 and K = 1500 for Phase-1 and Phase-2, respectively). In both phases, the five most successful classifiers are determined, and images classify with the help of the Majority Voting (Mathematical Evaluation) method. The application of the proposed method is designed for users to diagnose COVID-19 Positive, Normal, and Pneumonia. The results show that accuracy values obtained by Majority Voting (Mathematical Evaluation) method for Phase-1 and Phase-2 are equal to 99.86 % and 99.28 %, respectively. Thus, it indicates that the accuracy of the whole system is 99.63 %. When we analyze the classification performance metrics for Phase-1 and Phase-2, Specificity (%), Precision (%), Recall (%), F1 Score (%), Area Under Curve (AUC), and Matthews Correlation Coefficient (MCC) are equal to 99.98-99.83-99.07-99.51-0.9974-0.9855 and 99.73-99.69-98.63-99.23-0.9928-0.9518, respectively. Moreover, if the classification performance metrics of the whole system are examined, it is seen that Specificity (%), Precision (%), Recall (%), F1 Score (%), AUC, and MCC are 99.88, 99.78, 98.90, 99.40, 0.9956, and 0.9720, respectively. When the studies in the literature are examined, the results show that the proposed model is better than its counterparts. Because the best performance metrics for the dataset used were obtained in this study. In addition, since the biphasic majority voting technique is used in the study, it is seen that the proposed model is more reliable. On the other hand, although there are tens of thousands of studies on this subject, the usability of these models is debatable since most of them do not have graphical user interface applications. Already, in artificial intelligence technologies, besides the performance of the developed models, their usability is also important. Because the developed models can generally be used by people who are less knowledgeable about artificial intelligence.

Sunnetci Kubilay Muhammed, Alkan Ahmet

2023-Apr-15

Bag of features, COVID-19, Deep learning, Machine learning, Majority voting

General General

Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation.

In Applied soft computing

With the widespread deployment of COVID-19 vaccines all around the world, billions of people have benefited from the vaccination and thereby avoiding infection. However, huge amount of clinical cases revealed diverse side effects of COVID-19 vaccines, among which cervical lymphadenopathy is one of the most frequent local reactions. Therefore, rapid detection of cervical lymph node (LN) is essential in terms of vaccine recipients' healthcare and avoidance of misdiagnosis in the post-pandemic era. This paper focuses on a novel deep learning-based framework for the rapid diagnosis of cervical lymphadenopathy towards COVID-19 vaccine recipients. Existing deep learning-based computer-aided diagnosis (CAD) methods for cervical LN enlargement mostly only depend on single modal images, e.g., grayscale ultrasound (US), color Doppler ultrasound, and CT, while failing to effectively integrate information from the multi-source medical images. Meanwhile, both the surrounding tissue objects of the cervical LNs and different regions inside the cervical LNs may imply valuable diagnostic knowledge which is pending for mining. In this paper, we propose an Tissue-Aware Cervical Lymph Node Diagnosis method (TACLND) via multi-modal ultrasound semantic segmentation. The method effectively integrates grayscale and color Doppler US images and realizes a pixel-level localization of different tissue objects, i.e., lymph, muscle, and blood vessels. With inter-tissue and intra-tissue attention mechanisms applied, our proposed method can enhance the implicit tissue-level diagnostic knowledge in both spatial and channel dimension, and realize diagnosis of cervical LN with normal, benign or malignant state. Extensive experiments conducted on our collected cervical LN US dataset demonstrate the effectiveness of our methods on both tissue detection and cervical lymphadenopathy diagnosis. Therefore, our proposed framework can guarantee efficient diagnosis for the vaccine recipients' cervical LN, and assist doctors to discriminate between COVID-related reactive lymphadenopathy and metastatic lymphadenopathy.

Gao Yue, Fu Xiangling, Chen Yuepeng, Guo Chenyi, Wu Ji

2023-Jan

Attention mechanism, COVID-19, Cervical lymphadenopathy, Computer-aided diagnosis, Healthcare administration, Human-in-the-loop, Image classification, Multi-modal, Semantic segmentation

General General

On the improvement of heart rate prediction using the combination of singular spectrum analysis and copula-based analysis approach.

In PeerJ

In recent years, many people have been working from home due to the exceptional circumstances concerning the coronavirus disease 2019 (COVID-19) pandemic. It has also negatively influenced general health and quality of life. Therefore, physical activity has been gaining much attention in preventing the spread of Severe Acute Respiratory Syndrome Coronavirus. For planning an effective physical activity for different clients, physical activity intensity and load degree needs to be appropriately adjusted depending on the individual's physical/health conditions. Heart rate (HR) is one of the most critical health indicators for monitoring exercise intensity and load degree because it is closely related to the heart rate. Heart rate prediction estimates the heart rate at the next moment based on now and other influencing factors. Therefore, an accurate short-term HR prediction technique can deliver efficient early warning for human health and decrease the happening of harmful events. The work described in this article aims to introduce a novel hybrid approach to model and predict the heart rate dynamics for different exercises. The results indicate that the combination of singular spectrum analysis (SSA) and the Clayton Copula model can accurately predict HR for the short term.

Namazi Asieh

2022

Artificial intelligence, Heart rate, Machine learning, Prediction, Wearable sensors

General General

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.

In Journal of biomedical physics & engineering

BACKGROUND : Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA).

OBJECTIVE : This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality.

MATERIAL AND METHODS : In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models.

RESULTS : A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively.

CONCLUSION : The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.

Afrash Mohammad Reza, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi

2022-Dec

** Artificial Intelligence, Coronavirus (COVID-19), Data Mining, Machine Learning, Mortality**

Internal Medicine Internal Medicine

Prediction of COVID-19 Patients' Survival by Deep Learning Approaches.

In Medical journal of the Islamic Republic of Iran

Background: Despite many studies done to predict severe coronavirus 2019 (COVID-19) patients, there is no applicable clinical prediction model to predict and distinguish severe patients early. Based on laboratory and demographic data, we have developed and validated a deep learning model to predict survival and assist in the triage of COVID-19 patients in the early stages. Methods: This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data. The database consisted of data from 487 patients with COVID-19 diagnosed by the reverse transcription-polymerase chain reaction test and admitted to Imam Khomeini hospital affiliated to Tehran University of Medical Sciences from February 21, 2020, to June 24, 2020. Results: The developed model achieved an area under the curve (AUC) of 0.96 for survival prediction. The results demonstrated the developed model provided high precision (0.95, 0.93), recall (0.90,0.97), and F1-score (0.93,0.95) for low- and high-risk groups. Conclusion: The developed model is a deep learning-based, data-driven prediction tool that can predict the survival of COVID-19 patients with an AUC of 0.96. This model helps classify admitted patients into low-risk and high-risk groups and helps triage patients in the early stages.

Taheriyan Moloud, Ayyoubzadeh Seyed Mehdi, Ebrahimi Mehdi, R Niakan Kalhori Sharareh, Abooei Amir Hossien, Gholamzadeh Marsa, Ayyoubzadeh Seyed Mohammad

2022

COVID-19, Deep Learning, Prediction, Survival Analysis, Triage

General General

On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19.

In European journal of operational research

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with low accuracy, and it may be too complex to understand and explain. This paper proposes and studies a novel Mathematical Optimization model to build a sparse ensemble, which trades off the accuracy of the ensemble and the number of base regressors used. The latter is controlled by means of a regularization term that penalizes regressors with a poor individual performance. Our approach is flexible to incorporate desirable properties one may have on the ensemble, such as controlling the performance of the ensemble in critical groups of records, or the costs associated with the base regressors involved in the ensemble. We illustrate our approach with real data sets arising in the COVID-19 context.

Benítez-Peña Sandra, Carrizosa Emilio, Guerrero Vanesa, Jiménez-Gamero M Dolores, Martín-Barragán Belén, Molero-Río Cristina, Ramírez-Cobo Pepa, Romero Morales Dolores, Sillero-Denamiel M Remedios

2021-Dec-01

COVID-19, Ensemble Method, Machine Learning, Mathematical Optimization, Selective Sparsity

General General

Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations.

In Information processing & management

This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.

Blanco Guillermo, Lourenço Anália

2022-May

Conversation, Covid-19 pandemic, Emotion classification, Emotion shift, Sociome

General General

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system.

In Journal of cloud computing (Heidelberg, Germany)

In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the "Ad-hoc Cloud System" idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

Mawgoud Ahmed A, Taha Mohamed Hamed N, Abu-Talleb Amr, Kotb Amira

2022

Ad-hoc system, Cloud computing, Cloud security, Deep learning, Encryption, Steganography

General General

Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire.

In Briefings in bioinformatics

Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.

Zhao Yu, He Bing, Xu Zhimeng, Zhang Yidan, Zhao Xuan, Huang Zhi-An, Yang Fan, Wang Liang, Duan Lei, Song Jiangning, Yao Jianhua

2022-Dec-24

COVID-19, TCR repertoire, artificial intelligence, diagnosis, prognosis

General General

New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.

In Archives of gynecology and obstetrics ; h5-index 44.0

Preeclampsia, a multisystem disorder in pregnancy, is still one of the main causes of maternal morbidity and mortality. Due to a lack of a causative therapy, an accurate prediction of women at risk for the disease and its associated adverse outcomes is of utmost importance to tailor care. In the past two decades, there have been successful improvements in screening as well as in the prediction of the disease in high-risk women. This is due to, among other things, the introduction of biomarkers such as the sFlt-1/PlGF ratio. Recently, the traditional definition of preeclampsia has been expanded based on new insights into the pathophysiology and conclusive evidence on the ability of angiogenic biomarkers to improve detection of preeclampsia-associated maternal and fetal adverse events.However, with the widespread availability of digital solutions, such as decision support algorithms and remote monitoring devices, a chance for a further improvement of care arises. Two lines of research and application are promising: First, on the patient side, home monitoring has the potential to transform the traditional care pathway. The importance of the ability to input and access data remotely is a key learning from the COVID-19 pandemic. Second, on the physician side, machine-learning-based decision support algorithms have been shown to improve precision in clinical decision-making. The integration of signals from patient-side remote monitoring devices into predictive algorithms that power physician-side decision support tools offers a chance to further improve care.The purpose of this review is to summarize the recent advances in prediction, diagnosis and monitoring of preeclampsia and its associated adverse outcomes. We will review the potential impact of the ability to access to clinical data via remote monitoring. In the combination of advanced, machine learning-based risk calculation and remote monitoring lies an unused potential that allows for a truly patient-centered care.

Hackelöer Max, Schmidt Leon, Verlohren Stefan

2022-Dec-25

Angiogenic factors, Artificial intelligence, Decision trees, Hypertensive pregnancy disorders, Machine learning, Multivariable modeling, Preeclampsia, Remote monitoring

General General

Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak.

In Bulletin of mathematical biology

Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions' predictions.

Assad Daniel Bouzon Nagem, Cara Javier, Ortega-Mier Miguel

2022-Dec-24

COVID-19, Forecasting, Multivariate approach, Outbreak disease, Univariate approach

General General

Measuring daily-life fear perception change: A computational study in the context of COVID-19.

In PloS one ; h5-index 176.0

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

Chai Yuchen, Palacios Juan, Wang Jianghao, Fan Yichun, Zheng Siqi

2022

General General

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.

In PloS one ; h5-index 176.0

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

Sailunaz Kashfia, Bestepe Deniz, Özyer Tansel, Rokne Jon, Alhajj Reda

2022

General General

The effects of department of Veterans Affairs medical centers on socio-economic outcomes: Evidence from the Paycheck Protection Program.

In PloS one ; h5-index 176.0

Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck Protection Program (PPP) between April and June as a source of variation. First, we find that Veterans received 3.5% more loans and 6.8% larger loans than their counterparts (p < 0.01), controlling for a wide array of zipcode characteristics. Second, we develop models to predict the number of PPP loans awarded to Veterans, finding that the inclusion of local VA medical center characteristics adds almost as much explanatory power as the industry and occupational composition in an area and even more than the education, race, and age distribution combined. Our results suggest that VA medical centers can play an important role in helping Veterans thrive even beyond addressing their direct medical needs.

Makridis Christos A, Kelly J D, Alterovitz Gil

2022

General General

Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model.

In Journal of imaging

The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.

Phung Kim Anh, Nguyen Thuan Trong, Wangad Nileshkumar, Baraheem Samah, Vo Nguyen D, Nguyen Khang

2022-Dec-05

disease recognition, doctor consultation-inspired, medical image processing

General General

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms.

In Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion

BACKGROUND : The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals.

OBJECTIVES : To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university.

METHODS : A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19.

RESULTS : The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables.

CONCLUSIONS : ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Villagrana-Bañuelos Karen E, Maeda-Gutiérrez Valeria, Alcalá-Rmz Vanessa, Oropeza-Valdez Juan J, Herrera-Van Oostdam Ana S, Castañeda-Delgado Julio E, López Jesús Adrián, Borrego Moreno Juan C, Galván-Tejada Carlos E, Galván-Tejeda Jorge I, Gamboa-Rosales Hamurabi, Luna-García Huizilopoztli, Celaya-Padilla José M, López-Hernández Yamilé

2022

Biomarker, COVID-19, Genetic algorithm, LC-MS, Machine learning, Metabolomics, Random forest

General General

The role of advanced technologies against COVID-19: Prevention, Detection, and Treatments.

In Current stem cell research & therapy

Concurrent with the global outbreak of COVID-19, the race began among scientists to generate effective therapeutics for the treatment of COVID-19. In this regard, advanced technology such as nanotechnology, cell-based therapies, tissue engineering and regenerative medicine, nerve stimulation and artificial intelligence (AI) are attractive because they can offer new solutions for the prevention, diagnosis and treatment of COVID-19. Nanotechnology can design rapid and specific tests with high sensitivity for detecting infection and synthases new drugs and vaccines based on nanomaterials to directly deliver the intended antiviral agent to the desired site in the body and also provide new surfaces that do not allow virus adhesion. Mesenchymal stem cells and exosomes secreted from them apply in regenerative medicine and regulate inflammatory responses. Cell therapy and tissue engineering are combined to repair or substitute damaged tissues or cells. Tissue engineering using biomaterials, cells, and signaling molecules can develop new therapeutic and diagnostic platforms and help scientists fight viral diseases. Nerve stimulation technology can augment body's natural ability to modulate the inflammatory response and inhibit pro-inflammatory cytokines and consequently suppress cytokine storm. People can access free online health counseling services through AI and it helps very fast for screening and diagnosis of COVID-19 patients. This study is aimed first to give brief information about COVID-19 and the epidemiology of the disease. After that, we highlight important developments in the field of advanced technologies relevant to the prevention, detection, and treatment of the current pandemic.

Hasanzadeh Elham, Rafati Adele, Tamijani Seyedeh Masoumeh Seyed Hosseini, Rafaiee Raheleh, Golchin Ali, Abasi Mozhgan

2022-Dec-21

Artificial intelligence, COVID-19, Nanotechnology, Nerve stimulation, Stem cell therapy, Tissue engineering

General General

Physical fitness changes in adolescents due to social distancing during the coronavirus disease pandemic in Korea.

In PeerJ

BACKGROUND : At least 60 min of moderate-intensity physical activity per day is recommended for physical and mental health of adolescents. Schools are one of the most suitable places for promoting students' health as it is a place where vigorous physical activity occurs. However, the physical activity of students is threatened because schools are closed worldwide owing to the coronavirus disease (COVID-19) outbreak in 2019. Therefore, this study aimed to analyze the physical fitness changes in 27,782 Korean adolescents during the pandemic and present alternative education and health policies to the school.

METHODS : We included 29,882 middle school students (age: 13-15 years; males: 14,941, females: 12,841) in Korea from 2019 to 2021 . Participants' physical fitness at school was measured using the physical activity promotion system (PAPS) manual developed to measure students' physical fitness. Physical fitness variables included body mass index (BMI), 20 m shuttle run, handgrip strength, sit-and-reach, and 50 m run.

RESULTS : The COVID-19 pandemic has had a negative impact on the BMI and cardiorespiratory endurance of Korean middle school students. Specifically, male students' BMI increased, while body composition, cardiorespiratory endurance, and grip strength decreased significantly. Female students showed significant decreases in BMI and sit-and-reach test scores. It is clear that the physical fitness level of adolescents decreased by a greater degree after the COVID-19 pandemic than before, and the decrease in the physical fitness level of male students was noticeable. Therefore, a lesson strategy should be prepared that considers the contents and methods of physical education classes to improve the physical fitness level of male and female adolescents.

CONCLUSIONS : Fitness-based classes suitable for online methods should be urgently added as alternative physical education classes to prepare for the second COVID-19 outbreak. In addition, it is necessary to create an environment in which physical activity is a possibility in physical education classes, in any situation using artificial intelligence and virtual reality.

Lee Kwang-Jin, Seon Se-Young, Noh Byungjoo, An Keun-Ok

2022

Middle school students, Physical activity, Physical fitness level

Public Health Public Health

Mediating Role of Fine Particles Abatement on Pediatric Respiratory Health During COVID-19 Stay-at-Home Order in San Diego County, California.

In GeoHealth

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID-19 stay-at-home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay-at-home order, we quantified the specific contribution of fine-scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n = 72,333) at the ZIP-code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015-2020) and ZIP-code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay-at-home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay-at-home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4-year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay-at-home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits.

Aguilera Rosana, Leibel Sydney, Corringham Thomas, Bialostozky Mario, Nguyen Margaret B, Gershunov Alexander, Benmarhnia Tarik

2022-Sep

General General

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization.

In Frontiers in microbiology

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

Wang Yibai, Xiang Ju, Liu Cuicui, Tang Min, Hou Rui, Bao Meihua, Tian Geng, He Jianjun, He Binsheng

2022

SARS-CoV-2, bilinear matrix factorization, drug repositioning, machine learning, molecular docking

General General

Artificial Neural Networks for the Prediction of Monkeypox Outbreak.

In Tropical medicine and infectious disease

While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg-Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors' knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries.

Manohar Balakrishnama, Das Raja

2022-Dec-08

COVID-19, Hessian matrix, K-fold cross-validation, Levenberg–Marquardt model, machine learning, regression analysis, sigmoid function

General General

Artificial Neural Networks for the Prediction of Monkeypox Outbreak.

In Tropical medicine and infectious disease

While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg-Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors' knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries.

Manohar Balakrishnama, Das Raja

2022-Dec-08

COVID-19, Hessian matrix, K-fold cross-validation, Levenberg–Marquardt model, machine learning, regression analysis, sigmoid function

General General

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach.

In Environmental monitoring and assessment

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

Dutta Debashree, Pal Sankar K

2022-Dec-22

Air quality, Bidirectional LSTM, COVID-19, Deep learning, Lockdown, PM10, PM2.5, Random forest, Transfer learning

General General

Maimuna (Maia) Majumder.

In Cell reports. Medicine

Maimuna Majumder (she/they) is an assistant professor in the Computational Health Informatics Program at Harvard Medical School and Boston Children's Hospital. Her team has been engaged in COVID-19 response efforts since January 2020. Here, she discusses the role of artificial intelligence in pandemic-related research and computational epidemiology as a field.

**

2022-Dec-20

General General

Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation.

In Computers in biology and medicine

The COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improve the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. The achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset. Source code is available at https://github.com/YujiaKCL/COVID-CT-Similarity-Regularization.

Xu Yujia, Lam Hak-Keung, Jia Guangyu, Jiang Jian, Liao Junkai, Bao Xinqi

2022-Dec-15

CNNs, COVID-19, Computed tomography, Deep learning, Similarity regularization

General General

Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis.

In Applied animal behaviour science ; h5-index 32.0

Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10-23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8 am to 8 pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2 = 17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.

Byosiere Sarah-Elizabeth, Feighelstein Marcelo, Wilson Kristiina, Abrams Jennifer, Elad Guy, Farhat Nareed, van der Linden Dirk, Kaplun Dmitrii, Sinitca Aleksandr, Zamansky Anna

2022-May

Applied behavior, COVID-19, Computer vision, Dog behavior, Machine learning, Shelter research

General General

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening.

In Scientific reports ; h5-index 158.0

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

Xiong Hao, Berkovsky Shlomo, Kâafar Mohamed Ali, Jaffe Adam, Coiera Enrico, Sharan Roneel V

2022-Dec-20

General General

Dementia Analytics Research User Group (DARUG) - A Model for meaningful stakeholder engagement in dementia research.

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

BACKGROUND : The importance of involving stakeholders in research is widely recognised but few studies provide details to implementation in practice. The use of real-time technology involving patients, carers and professionals in project design, monitoring, delivery and reporting could maximise contribution. Stakeholder engagement was included as part of a Dementia Analytics Research User Group project which applied machine learning to the Trinity-Ulster-Department of Agriculture (TUDA) data set, identifying clinical and lifestyle factors associated with cognitive health in 5000 community-dwelling older Irish adults.

METHOD : An innovative model for engagement (ENGAGE) was used1 - a methodological and technology platform, that gains insight into group thinking and consensus. Developed by Ulster University, this produces a report in real time for sharing to all stakeholders, thus ensuring active involvement in defining the research question. Using a Personal and Public Involvement (PPI) approach, representatives from patient and carer groups (including TUDA participants ), charities (e.g., Alzheimer's UK), as well as professionals, were invited to attend one of three scoping events. Each event commenced with an overview by the project team of the value of data analytics and initial data analysis. The PPI groups were then invited to answer specific questions relating to risk factors for dementia and were asked to articulate their expectations on the potential outcomes from the project. These responses were analysed using ENGAGE and word clouds generated for discussion to help refine the project going forward.

RESULTS : Participants (n=87) Lifestyle, Genetics, Stress and were the dominant emerging themes for risk factors of dementia. Prevention, Help and Information/ Research emerged as strong themes, with the mind maps showing stimulus, understanding and awareness as key outputs of this project. The outcomes of this engagement model were utilised to successfully inform the subsequent data analytics portion of the study2 .

CONCLUSION : The model performed well, capturing discussions in real time. Feedback was positive and helped to focus and inform the research team's thinking. What was not so successful was the longer-term inclusion in the project, with engagement through remote channels tending to drift over time, somewhat exacerbated by COVID 19 restrictions. The team aim to follow up on this aspect.

Carlin Paul, Wallace Jonathan, Moore Adrian, Hughes Catherine, Black Michaela, Rankin Deborah, Hoey Leane, McNulty Helene

2022-Dec

General General

Drug development process and COVID-19 pandemic: Flourishing era of outsourcing.

In Indian journal of pharmacology

Traditional drug development is a tedious process with involvement of enormous cost and a high attrition rate. Outsourcing drug development services to contract research organizations (CROs) has become an important strategy for cost and risk reduction, capacity building, and data generation. The therapeutic and operational expertise of these CROs has allowed pharmaceutical industry to reduce in-house infrastructure as well as research capacity. Working with specialized CROs has not only increased the rate of success but also the speed of drug discovery process. Small firms with promising molecules but limited resources and large firms interested in diversifying their dimensions are utilizing the services of efficient CROs. Globally, approximately one-third of the drug development processes are now being outsourced and the data generated by the independent third party are well appreciated during regulatory submissions. In this article, we discuss the international and national trends, outsourcing services and models, key considerations while selecting CRO, and benefits and challenges of outsourcing. Further, we discuss how the technical expertise of competent CROs was utilized when traditional ways of conducting clinical trials were disrupted by the COVID-19 pandemic. Taken together, the increasing health-care demands, COVID-19 pandemic or any other such upcoming health crisis, and recent advances in advanced technologies (machine learning and artificial intelligence, etc.) are likely to fuel global CRO market in the coming years.

Wasan Himika, Singh Devendra, Reeta K H, Gupta Pooja, Gupta Yogendra Kumar

2022

Artificial intelligence, COVID-19, contract research organizations, drug development, outsourcing

General General

D-Cov19Net: A DNN based COVID-19 detection system using lung sound.

In Journal of computational science

The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the deterministic input for our approach. 'D-Cov19Net' has been trained with 23,592 recordings, begetting an AUC of 0.972 and sensitivity of 0.983 after 100 epochs. The model can be of immense utility in biomedical technology due to its significant accuracy, simplicity, user convenience, feasibility, and faster detection while maintaining social distancing.

Chatterjee Sukanya, Roychowdhury Jishnu, Dey Anilesh

2022-Dec-15

Auto-diagnosis system, COVID-19 Detection, Convolution Neural Network (CNN), Deep Learning, Lung/Respiratory sound

General General

A residual network-based framework for COVID-19 detection from CXR images.

In Neural computing & applications

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a large population. Polymerase chain reaction is currently being utilized to diagnose COVID-19 in suspected patients; however, its sensitivity is quite low. The researchers also developed automated approaches for reliably and timely identifying COVID-19 from X-ray images. However, traditional machine learning-based image classification algorithms necessitate manual image segmentation and feature extraction, which is a time-consuming task. Due to promising results and robust performance, Convolutional Neural Network (CNN)-based techniques are being used widely to classify COVID-19 from Chest X-rays (CXR). This study explores CNN-based COVID-19 classification methods. A series of experiments aimed at COVID-19 detection and classification validates the viability of our proposed framework. Initially, the dataset is preprocessed and then fed into two Residual Network (ResNet) architectures for deep feature extraction, such as ResNet18 and ResNet50, whereas support vector machines with its multiple kernels, including Quadratic, Linear, Gaussian and Cubic, are used to classify these features. The experimental results suggest that the proposed framework efficiently detects COVID-19 from CXR images. The proposed framework obtained the best accuracy of 97.3% using ResNet50.

Kibriya Hareem, Amin Rashid

2022-Dec-15

COVID-19, CXR images, Machine learning, ResNet50, SVM

General General

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks.

In Computer networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE (Transferable Traffic Prediction in MUlti-Service Edge Networks), a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning.

Xu Luyang, Liu Haoyu, Song Junping, Li Rui, Hu Yahui, Zhou Xu, Patras Paul

2023-Feb

Edge model transfer, Multi-service traffic prediction, Service clustering

General General

A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction.

In Pattern recognition

With the outbreak and wide spread of novel coronavirus (COVID-19), contactless fingerprint recognition has attracted more attention for personal recognition because it can provide significantly higher user convenience and hygiene than the traditional contact-based fingerprint recognition. However, it is still challenging to achieve a highly accurate recognition due to the low ridge-valley contrast and pose variances of contactless fingerprints. Minutiae points are a kind of ridge flow discontinuities, and robust and accurate extraction is an important step for most automatic fingerprint recognition algorithms. Most of existing methods are based on two stages which locate the minutiae points first and then compute their directions. The two-stage method cannot make full use of location and direction information. In this paper, we propose a multi-task fully deep convolutional neural network for jointly learning the minutiae location detection and its corresponding direction computation which operates directly on the whole gray scale contactless fingerprints. The proposed method consists of offline training and online testing stages. In the training stage, a fully deep convolutional neural network is built for the tasks of minutiae detection and its direction regression, with an attention mechanism to make the direction regression branch concentrate on the minutiae points. A new loss function is proposed to jointly learn the tasks of minutiae detection and its direction regression from the whole fingerprints. In the testing stage, the trained network is applied on the whole contactless fingerprint to generate the minutiae location and direction maps. The proposed multi-task leaning method performs better than the individual single task and it operates directly on the raw gray-scale contactless fingerprints without preprocessing. The results on three contactless fingerprint datasets show the proposed algorithm performs better than other minutiae extraction algorithms and the commercial software.

Zhang Zhao, Liu Shuxin, Liu Manhua

2021-Dec

00-01, 99-00, Contactless fingerprint, Deep convolutional neural network, Minutiae extraction, Multi-task learning

General General

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

bioRxiv Preprint

The success of the human body in fighting SARS-CoV-2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV-2 compared with uninfected controls. In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

Safra, M.; Tamari, Z.; Polak, P.; Shiber, S.; Matan, M.; Karameh, H.; Helviz, Y.; Levy-Barda, A.; Yahalom, V.; Peretz, A.; Ben-Chetrit, E.; Brenner, B.; Tuller, T.; Gal-Tanamy, M.; Yaari, G.

2022-12-21

Internal Medicine Internal Medicine

Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data.

In Frontiers in medicine

BACKGROUND : When facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19.

MATERIALS AND METHODS : We enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission.

RESULTS : The model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care.

CONCLUSION : In clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.

Muto Reiko, Fukuta Shigeki, Watanabe Tetsuo, Shindo Yuichiro, Kanemitsu Yoshihiro, Kajikawa Shigehisa, Yonezawa Toshiyuki, Inoue Takahiro, Ichihashi Takuji, Shiratori Yoshimune, Maruyama Shoichi

2022

COVID-19, artificial intelligence-human collaboration, clinical practice, oxygen needs, sodium chloride difference

Public Health Public Health

An NLP tool for data extraction from electronic health records: COVID-19 mortalities and comorbidities.

In Frontiers in public health

BACKGROUND : The high infection rate, severe symptoms, and evolving aspects of the COVID-19 pandemic provide challenges for a variety of medical systems around the world. Automatic information retrieval from unstructured text is greatly aided by Natural Language Processing (NLP), the primary approach taken in this field. This study addresses COVID-19 mortality data from the intensive care unit (ICU) in Kuwait during the first 18 months of the pandemic. A key goal is to extract and classify the primary and intermediate causes of death from electronic health records (EHRs) in a timely way. In addition, comorbid conditions or concurrent diseases were retrieved and analyzed in relation to a variety of causes of mortality.

METHOD : An NLP system using the Python programming language is constructed to automate the process of extracting primary and secondary causes of death, as well as comorbidities. The system is capable of handling inaccurate and messy data, this includes inadequate formats, spelling mistakes and mispositioned information. A machine learning decision trees method is used to classify the causes of death.

RESULTS : For 54.8% of the 1691 ICU patients we studied, septic shock or sepsis-related multiorgan failure was the leading cause of mortality. About three-quarters of patients die from acute respiratory distress syndrome (ARDS), a common intermediate cause of death. An arrhythmia (AF) disorder was determined to be the strongest predictor of intermediate cause of death, whether caused by ARDS or other causes.

CONCLUSION : We created an NLP system to automate the extraction of causes of death and comorbidities from EHRs. Our method processes messy and erroneous data and classifies the primary and intermediate causes of death of COVID-19 patients. We advocate arranging the EHR with well-defined sections and menu-driven options to reduce incorrect forms.

BuHamra Sana S, Almutairi Abdullah N, Buhamrah Abdullah K, Almadani Sabah H, Alibrahim Yusuf A

2022

SARS-CoV-2, decision tree, information extraction, mortality, natural language processing, prediction, text mining

Public Health Public Health

Can a chatbot enhance hazard awareness in the construction industry?

In Frontiers in public health

Safety training enhances hazard awareness in the construction industry. Its effectiveness is a component of occupational safety and health. While face-to-face safety training has dominated in the past, the frequent lockdowns during COVID-19 have led us to rethink new solutions. A chatbot is messaging software that allows people to interact, obtain answers, and handle sales and inquiries through a computer algorithm. While chatbots have been used for language education, no study has investigated their usefulness for hazard awareness enhancement after chatbot training. In this regard, we developed four Telegram chatbots for construction safety training and designed the experiment as the treatment factor. Previous researchers utilized eye-tracking in the laboratory for construction safety research; most have adopted it for qualitative analyses such as heat maps or gaze plots to study visual paths or search strategies via eye-trackers, which only studied the impact of one factor. Our research has utilized an artificial intelligence-based eye-tracking tool. As hazard awareness can be affected by several factors, we filled this research void using 2-way interaction terms using the design of experiment (DOE) model. We designed an eye-tracking experiment to study the impact of site experience, Telegram chatbot safety training, and task complexity on hazard awareness, which is the first of its kind. The results showed that Telegram chatbot training enhanced the hazard awareness of participants with less onsite experience and in less complex scenarios. Low-cost chatbot safety training could improve site workers' danger awareness, but the design needs to be adjusted according to participants' experience. Our results offer insights to construction safety managers in safety knowledge sharing and safety training.

Zhu Xiaoe, Li Rita Yi Man, Crabbe M James C, Sukpascharoen Khunanan

2022

chatbot safety training, construction hazard awareness, construction practitioners, design of experiment, eye-tracking

General General

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients.

In PeerJ

BACKGROUND : The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients.

METHODS : The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings.

RESULTS : We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained.

CONCLUSIONS : In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

Martinez Gustavo, Garduno Alexis, Mahmud-Al-Rafat Abdullah, Ostadgavahi Ali Toloue, Avery Ann, de Avila E Silva Scheila, Cusack Rachael, Cameron Cheryl, Cameron Mark, Martin-Loeches Ignacio, Kelvin David

2022

Artificial Neural Networks, Biomarkers, Classification, Deep learning, Immunology

General General

COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.

In Chaos, solitons, and fractals

Many severe epidemics and pandemics have hit human civilizations throughout history. The recent Sever Actuate Respiratory disease SARS-CoV-2 known as COVID-19 became a global disease and is still growing around the globe. It has severely affected the world's economy and ways of life. It necessitates predicting the spread in advance and considering various control policies to avoid the country's complete closure. In this paper, we propose deep learning-based stacked Bi-directional long short-term memory (Stacked Bi-LSTM) network that forecasts COVID-19 more accurately for the country of South Korea. The paper's main objectives are to present a lightweight, accurate, and optimized model to predict the spread considering restriction policies such as school closure, workspace closing, and the canceling of public events. Based on the fourteen parameters (including control policies), we predict and forecast the future value of the number of positive, dead, recovered, and quarantined cases. In this paper, we use the dataset of South Korea comprised of several control policies implemented for minimizing the spread of COVID-19. We compare the performance of the stacked Bi-LSTM with the traditional time-series models and LSTM model using the performance metrics mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, we study the impact of control policies on forecasting accuracy. We further study the impact of changing the Bi-LSTM default activation functions Tanh with ReLU on forecasting accuracy. The research provides insight to policymakers to optimize the pooling of resources more optimally on the correct date and time prior to the event and to control the spread by employing various strategies in the meantime.

Ali Furqan, Ullah Farman, Khan Junaid Iqbal, Khan Jebran, Sardar Abdul Wasay, Lee Sungchang

2022-Dec-13

COVID-19, Deep Learning, Forecasting, Long short-term memory, Pandemic, Stacked Bi-LSTM, Time series

Public Health Public Health

Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death.

In Frontiers in artificial intelligence

The rapid spread of COVID-19 and its variants have devastated communities worldwide, and as the highly transmissible Omicron variant becomes the dominant strain of the virus in late 2021, the need to characterize and understand the difference between the new variant and its predecessors has been an increasing priority for public health authorities. Artificial Intelligence has played a significant role in the analysis of various facets of COVID-19 since the early stages of the pandemic. This study proposes the use of AI, specifically an XGBoost model, to quantify the impact of various medical risk factors (or "population features") on the possibility of a patient outcome resulting in hospitalization, ICU admission, or death. The results are compared between the Delta and Omicron COVID-19 variants. Results indicated that older age and an unvaccinated patient status most consistently correspond as the most significant population features contributing to all three scenarios (hospitalization, ICU, death). The top 15 features for each variant-outcome scenario were determined, which most frequently included diabetes, cardiovascular disease, chronic kidney disease, and complications of pneumonia as highly significant population features contributing to serious illness outcomes. The Delta/Hospitalization model returned the highest performance metric scores for the area under the receiver operating characteristic (AUROC), F1, and Recall, while Omicron/ICU and Omicron/Hospitalization had the highest accuracy and precision values, respectively. The recall was found to be above 0.60 in most cases (with only two exceptions), indicating that the total number of false positives was generally minimized (accounting for more of the people who would theoretically require medical care).

Hilal Waleed, Chislett Michael G, Snider Brett, McBean Edward A, Yawney John, Gadsden S Andrew

2022

COVID-19, Delta variant, Omicron variant, XGBoost, healthcare, hospitalization, medical risk factors

General General

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.

In Frontiers in artificial intelligence

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Shen John, Ghatti Siddharth, Levkov Nate Ryan, Shen Haiying, Sen Tanmoy, Rheuban Karen, Enfield Kyle, Facteau Nikki Reyer, Engel Gina, Dowdell Kim

2022

AI, COVID-19 detection, IoT, mobile devices, telemedicine

General General

COVID-19 and human development: An approach for classification of HDI with deep CNN.

In Biomedical signal processing and control

The measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Convolutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).

Kavuran Gürkan, Gökhan Şeyma, Yeroğlu Celaleddin

2023-Mar

Artificial intelligence, COVID-19, Classification, Continuous wavelet transform, Deep learning, Human Development Index

General General

The design of compounds with desirable properties - The anti-HIV case study.

In Journal of computational chemistry

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.

Novak Jurica, Pathak Prateek, Grishina Maria A, Potemkin Vladimir A

2022-Dec-19

3CLpro, HIV-1 protease, QSAR, cytotoxicity, drug repurposing

General General

Does media sentiment affect stock prices? Evidence from China's STAR market.

In Frontiers in psychology ; h5-index 92.0

OBJECTIVE : This paper explores the impact of media sentiment on stock prices on the Shanghai Stock Exchange Science and Technology Innovation Board (hereinafter the STAR market) from a behavioral finance perspective.

METHODS : We collect Baidu News coverage of STAR-listed firms as the text, and measure text sentiment using a machine learning-based text analysis technique. We then empirically examine the impact of media sentiment on STAR market stock prices from two aspects: IPO pricing efficiency and IPO first-day stock performance.

RESULTS : (1) Media sentiment has no significant impact on IPO pricing efficiency, thus suggesting that institutional investors participating in such offerings are generally not affected by media sentiment. (2) Optimistic media sentiment has a positive impact on IPO first-day returns, which indicates that individual investors are more easily influenced by media sentiment and therefore likely to abandon their rational judgment. (3) Media sentiment had a greater impact on IPO first-day returns during the COVID-19 pandemic than those before it, which suggests that individual investors are more influenced by media sentiment during pandemics.

DISCUSSION : Our findings deepen the understanding of stock price formation on the STAR market, which provide a statistical basis for formulating policy directions and investment strategies.

Dong Xiuliang, Xu Shiying, Liu Jianing, Tsai Fu-Sheng

2022

IPO first-day stock performance, IPO pricing efficiency, machine learning, media sentiment, stock price, the STAR market

General General

Research on the state of blended learning among college students - A mixed-method approach.

In Frontiers in psychology ; h5-index 92.0

In the wake of the COVID-19 pandemic in 2019, China's education leaders began to focus on and promote blended learning. The process is still in its infancy in Chinese colleges and universities, and its development remains a problem to be solved. By combining technology acceptance and student participation, this article proposes an analysis model for assessing the factors influencing blended learning. A questionnaire was designed and distributed, and 796 valid responses were collected. The mean and variance were used to examine the status of students' technology acceptance and satisfaction with blended learning. The t-test method was employed to analyze the gender differences between students in regard to the topic. The results show that: (1) students majoring in computer science view the factors as having a high level of influence in blended learning. (2) There are major variances regarding the perception of service quality between male and female computer science major students. There is no significant difference between them in terms of perceived usefulness, perceived ease of use, or computer self-efficacy. (3) There are considerable disparities in the skill involvement and participation of computer science major college students. The results show that the technology acceptance and participation of students determine the effect of blended learning. Based on these findings, this article provides theoretical and practical suggestions for the implementation of blended learning to improve its effect.

Deng Chao, Peng Jiao, Li ShuFei

2022

TAM 3, blended learning, learner engagement theory, student engagement, technology acceptance

General General

Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.

In Information sciences

Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.

Mahbub Md Kawsher, Biswas Milon, Gaur Loveleen, Alenezi Fayadh, Santosh K C

2022-May

ACC, Accuracy, AI, Artificial Intelligence, AUC, Area Under the Curve, CADx, Computer-Aided Diagnosis, CNN, Convolutional Neural Network, CT, Computed Tomography, CXR, Chest X-ray, Chest X-ray, Covid-19, DL, Deep Learning, DNN, DNN, Deep Neural Network, Infectious DiseaseX, ML, Machine Learning, MTB, Mycobacterium Tuberculosis, Medical imaging, NN, Neural Network, Pneumonia, SEN, Sensitivity, SPEC, Specificity, TB, Tuberculosis, Tuberculosis, WHO, World Health Organization

Public Health Public Health

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests.

In PeerJ. Computer science

Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.

Nguyen Hieu, Gokhale Swapna

2022

Anti-government, COVID-19, Lockdown protests, Machine Learning, Social media

General General

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations.

In PeerJ. Computer science

Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for instance, is crucial for the success of the global vaccination programs and thus achieving herd immunity. To combat the proliferation of COVID-19-related misinformation, considerable research efforts have been and are still being dedicated to building and sharing COVID-19 misinformation detection datasets and models for Arabic and other languages. However, most of these datasets provide binary (true/false) misinformation classifications. Besides, the few studies that support multi-class misinformation classification deal with a small set of misinformation classes or mix them with situational information classes. False news stories about COVID-19 are not equal; some tend to have more sinister effects than others (e.g., fake cures and false vaccine info). This suggests that identifying the sub-type of misinformation is critical for choosing the suitable action based on their level of seriousness, ranging from assigning warning labels to the susceptible post to removing the misleading post instantly. We develop comprehensive annotation guidelines in this work that define 19 fine-grained misinformation classes. Then, we release the first Arabic COVID-19-related misinformation dataset comprising about 6.7K tweets with multi-class and multi-label misinformation annotations. In addition, we release a version of the dataset to be the first Twitter Arabic dataset annotated exclusively with six different situational information classes. Identifying situational information (e.g., caution, help-seeking) helps authorities or individuals understand the situation during emergencies. To confirm the validity of the collected data, we define three classification tasks and experiment with various machine learning and transformer-based classifiers to offer baseline results for future research. The experimental results indicate the quality and validity of the data and its suitability for constructing misinformation and situational information classification models. The results also demonstrate the superiority of AraBERT-COV19, a transformer-based model pretrained on COVID-19-related tweets, with micro-averaged F-scores of 81.6% and 78.8% for the multi-class misinformation and situational information classification tasks, respectively. Label Powerset with linear SVC achieved the best performance among the presented methods for multi-label misinformation classification with micro-averaged F-scores of 76.69%.

Obeidat Rasha, Gharaibeh Maram, Abdullah Malak, Alharahsheh Yara

2022

BERT, COVID-19, Data annotation, Data collection, Deep learning, Fake news, Machine learning, Misinformation detection, Situational information, Transformers

General General

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In SN computer science

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

Kumar Yogesh, Koul Apeksha, Kaur Sukhpreet, Hu Yu-Chen

2023

COVID-19, Facebook Prophet, Holt model, Prediction, RANSAC regressor, Random forest regressor, Stacked gated recurrent units, Stacked long short-term memory, XG Boost

General General

A Precise Method to Detect Post-COVID-19 Pulmonary Fibrosis Through Extreme Gradient Boosting.

In SN computer science

The association of pulmonary fibrosis with COVID-19 patients has now been adequately acknowledged and caused a significant number of mortalities around the world. As automatic disease detection has now become a crucial assistant to clinicians to obtain fast and precise results, this study proposes an architecture based on an ensemble machine learning approach to detect COVID-19-associated pulmonary fibrosis. The paper discusses Extreme Gradient Boosting (XGBoost) and its tuned hyper-parameters to optimize the performance for the prediction of severe COVID-19 patients who developed pulmonary fibrosis after 90 days of hospital discharge. A dataset comprising Electronic Health Record (EHR) and corresponding High-resolution computed tomography (HRCT) images of chest of 1175 COVID-19 patients has been considered, which involves 725 pulmonary fibrosis cases and 450 normal lung cases. The experimental results achieved an accuracy of 98%, precision of 99% and sensitivity of 99%. The proposed model is the first in literature to help clinicians in keeping a record of severe COVID-19 cases for analyzing the risk of pulmonary fibrosis through EHRs and HRCT scans, leading to less chance of life-threatening conditions.

Jha Manika, Gupta Richa, Saxena Rajiv

2023

COVID-19, Clinical decision support, Extreme gradient boosting, Machine learning, Medical diagnosis, Pulmonary fibrosis, Tree boosting

General General

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.

In Multimedia tools and applications

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

Lasker Asifuzzaman, Ghosh Mridul, Obaidullah Sk Md, Chakraborty Chandan, Roy Kaushik

2022-Dec-03

Chest radiography, Covid-19, Deep neural network, Lung diseases, Pneumonia, Stack ensemble technique

General General

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning.

In Biomedical signal processing and control

Blood Oxygen ( SpO 2 ), a key indicator of respiratory function, has received increasing attention during the COVID-19 pandemic. Clinical results show that patients with COVID-19 likely have distinct lower SpO 2 before the onset of significant symptoms. Aiming at the shortcomings of current methods for monitoring SpO 2 by face videos, this paper proposes a novel multi-model fusion method based on deep learning for SpO 2 estimation. The method includes the feature extraction network named Residuals and Coordinate Attention (RCA) and the multi-model fusion SpO 2 estimation module. The RCA network uses the residual block cascade and coordinate attention mechanism to focus on the correlation between feature channels and the location information of feature space. The multi-model fusion module includes the Color Channel Model (CCM) and the Network-Based Model(NBM). To fully use the color feature information in face videos, an image generator is constructed in the CCM to calculate SpO 2 by reconstructing the red and blue channel signals. Besides, to reduce the disturbance of other physiological signals, a novel two-part loss function is designed in the NBM. Given the complementarity of the features and models that CCM and NBM focus on, a Multi-Model Fusion Model(MMFM) is constructed. The experimental results on the PURE and VIPL-HR datasets show that three models meet the clinical requirement(the mean absolute error ⩽ 2%) and demonstrate that the multi-model fusion can fully exploit the SpO 2 features of face videos and improve the SpO 2 estimation performance. Our research achievements will facilitate applications in remote medicine and home health.

Hu Min, Wu Xia, Wang Xiaohua, Xing Yan, An Ning, Shi Piao

2023-Mar

Coordinate attention, Deep learning, Estimation, Multi-model fusion, Remote photo-plethysmography, Residual network

General General

MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.

In Information sciences

The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%.

Ding Weiping, Abdel-Basset Mohamed, Hawash Hossam, Pedrycz Witold

2023-Apr

COVID-19, Data Heterogeneity, Deep Learning, Fog Computing, Internet of Medical Things

Internal Medicine Internal Medicine

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

In Applied soft computing

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

Custode Leonardo Lucio, Mento Federico, Tursi Francesco, Smargiassi Andrea, Inchingolo Riccardo, Perrone Tiziano, Demi Libertario, Iacca Giovanni

2023-Jan

COVID-19, Decision trees, Evolutionary algorithms, Grammatical evolution, Lung ultrasound, Neuro-symbolic artificial intelligence

General General

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals.

In Frontiers in immunology ; h5-index 100.0

INTRODUCTION : Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome.

METHODS : By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals.

RESULTS : Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1β, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors.

DISCUSSION : In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Chattopadhyay Partha, Khare Kriti, Kumar Manish, Mishra Pallavi, Anand Alok, Maurya Ranjeet, Gupta Rohit, Sahni Shweta, Gupta Ayushi, Wadhwa Saruchi, Yadav Aanchal, Devi Priti, Tardalkar Kishore, Joshi Meghnad, Sethi Tavpritesh, Pandey Rajesh

2022

COVID-19, T-cell activation, bayesian network model, immune response, recovered COVID-19 individuals, single cell multi-omics

Public Health Public Health

SARS-CoV-2 induces "cytokine storm" hyperinflammatory responses in RA patients through pyroptosis.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : The coronavirus disease (COVID-19) is a pandemic disease that threatens worldwide public health, and rheumatoid arthritis (RA) is the most common autoimmune disease. COVID-19 and RA are each strong risk factors for the other, but their molecular mechanisms are unclear. This study aims to investigate the biomarkers between COVID-19 and RA from the mechanism of pyroptosis and find effective disease-targeting drugs.

METHODS : We obtained the common gene shared by COVID-19, RA (GSE55235), and pyroptosis using bioinformatics analysis and then did the principal component analysis(PCA). The Co-genes were evaluated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for functional enrichment, the protein-protein interaction (PPI) network was built by STRING, and the k-means machine learning algorithm was employed for cluster analysis. Modular analysis utilizing Cytoscape to identify hub genes, functional enrichment analysis with Metascape and GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network pharmacology analysis was performed to identify target drug-related genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub genes and construct transcription factor (TF)-hub genes and miRNA-hub genes networks by NetworkAnalyst. The Co-hub genes were validated using GSE55457 and GSE93272 to acquire the Key gene, and their efficacy was assessed using receiver operating curves (ROC); SPEED2 was then used to determine the upstream pathway. Immune cell infiltration was analyzed using CIBERSORT and validated by the HPA database. Molecular docking, molecular dynamics simulation, and molecular mechanics-generalized born surface area (MM-GBSA) were used to explore and validate drug-gene relationships through computer-aided drug design.

RESULTS : COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways(the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex, death-inducing signaling complex, regulation of interleukin production), natural immune pathways (Network map of SARS-CoV-2 signaling pathway, activation of NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm pathways (IL, nuclear factor-kappa B (NF-κB), TNF signaling pathway and regulation of cytokine-mediated signaling). Of these, CASP1 is the most involved pathway and is closely related to minocycline. YY1, hsa-mir-429, and hsa-mir-34a-5p play an important role in the expression of CASP1. Monocytes are high-caspase-1-expressing sentinel cells. Minocycline can generate a highly stable state for biochemical activity by docking closely with the active region of caspase-1.

CONCLUSIONS : Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and it may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline may counteract cytokine storm inflammation in patients with COVID-19 combined with RA by inhibiting caspase-1 expression.

Zheng Qingcong, Lin Rongjie, Chen Yuchao, Lv Qi, Zhang Jin, Zhai Jingbo, Xu Weihong, Wang Wanming

2022

COVID-19, SARS-CoV-2, caspase-1, minocycline, pyroptosis, rheumatoid arthritis

Dermatology Dermatology

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : A lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways.

METHODS : The RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification.

RESULTS : We recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible.

CONCLUSIONS : To sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.

Zhang Teng, Feng Hao, Zou Xiaoyan, Peng Shixiong

2022

COVID-19, bioinformatics, biomarker, chronic Urticaria (CU), immunology

General General

COVID vaccine stigma: detecting stigma across social media platforms with computational model based on deep learning.

In Applied intelligence (Dordrecht, Netherlands)

The study presents the first computational model of COVID vaccine stigma that can identify stigmatised sentiment with a high level of accuracy and generalises well across a number of social media platforms. The aim of the study is to understand the lexical features that are prevalent in COVID vaccine discourse and disputes between anti-vaccine and pro-vaccine groups. This should provide better insight for healthcare authorities, enabling them to better navigate those discussions. The study collected posts and their comments related to COVID vaccine sentiment in English, from Reddit, Twitter, and YouTube, for the period from April 2020 to March 2021. The labels used in the model, "stigma", "not stigma", and "undefined", were collected from a smaller Facebook (Meta) dataset and successfully propagated into a larger dataset from Reddit, Twitter, and YouTube. The success of the propagation task and consequent classification is a result of state-of-the-art annotation scheme and annotated dataset. Deep learning and pre-trained word vector embedding significantly outperformed traditional algorithms, according to two-tailed P(T≤t) test and achieved F1 score of 0.794 on the classification task with three classes. Stigmatised text in COVID anti-vaccine discourse is characterised by high levels of subjectivity, negative sentiment, anxiety, anger, risk, and healthcare references. After the first half of 2020, anti-vaccination stigma sentiment appears often in comments to posts attempting to disprove COVID vaccine conspiracy theories. This is inconsonant with previous research findings, where anti-vaccine people stayed primarily within their own in-group discussions. This shift in the behaviour of the anti-vaccine movement from affirming climates to ones with opposing opinions will be discussed and elaborated further in the study.

Straton Nadiya

2022-Dec-07

COVID-19, Deep learning, Social media, Stigma, Vaccine

General General

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control.

In Computer communications

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyra- mid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fu- sion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense align- ment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network's performance. In addition, to achieve high real-time performance, our net- work is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.

Kong Weiyi, You Zhisheng, Lv Xuebin

2022-Dec-13

3D face recognition, Deep learning, Epidemic control, Face reconstruction, Multimodal fusion

General General

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach.

In Computer communications

COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.

Dhasarathan Chandramohan, Hasan Mohammad Kamrul, Islam Shayla, Abdullah Salwani, Mokhtar Umi Asma, Javed Abdul Rehman, Goundar Sam

2022-Dec-14

Deep learning system, Healthcare, Homomorphic, Privacy metrics, Privacy preserving, Security