AIMC Topic:
SARS-CoV-2
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Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study.
PloS one
Nov 24, 2020
The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians i...
Adult
Sensitivity and Specificity
Male
Middle Aged
Deep Learning
Data Accuracy
Humans
Reproducibility of Results
Radiography, Thoracic
SARS-CoV-2
Aged
Aged, 80 and over
COVID-19
Reverse Transcriptase Polymerase Chain Reaction
Retrospective Studies
Triage
Radiologists
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Republic of Korea
Female
Application of artificial neural networks to predict the COVID-19 outbreak.
Global health research and policy
Nov 23, 2020
BACKGROUND: Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers.
Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.
Scientific reports
Nov 23, 2020
COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SAR...
Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients.
Journal of healthcare engineering
Nov 23, 2020
Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. ...
Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.
BMC medical informatics and decision making
Nov 19, 2020
BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop progno...
Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model.
Viruses
Nov 18, 2020
Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-ta...
An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.
PloS one
Nov 17, 2020
A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging techn...
Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.
Journal of critical care
Nov 16, 2020
PURPOSE: The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19.
Machine Learning for Mortality Analysis in Patients with COVID-19.
International journal of environmental research and public health
Nov 12, 2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis wher...
Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.
PloS one
Nov 12, 2020
Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapt...
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