AIMC Topic: COVID-19
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Vaccine development using artificial intelligence and machine learning: A review.
International journal of biological macromolecules
Oct 18, 2024
The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immu...
Clinically Guided Adaptive Machine Learning Update Strategies for Predicting Severe COVID-19 Outcomes.
The American journal of medicine
Oct 17, 2024
BACKGROUND: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to...
Neural parameter calibration and uncertainty quantification for epidemic forecasting.
PloS one
Oct 17, 2024
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such prediction...
Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Oct 14, 2024
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention a...
Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance.
Computers in biology and medicine
Oct 9, 2024
BACKGROUND: Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further res...
Recommendations on the use of artificial intelligence in health promotion.
Progress in cardiovascular diseases
Oct 9, 2024
The purpose of this perspective is to provide recommendations on the use of Artificial Intelligence (AI) in health promotion. To arrive at these recommendations, we followed a 6-step process. The first step was to recruit an international authorship ...
Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.
PloS one
Oct 9, 2024
The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transpare...
TDFFM: Transformer and Deep Forest Fusion Model for Predicting Coronavirus 3C-Like Protease Cleavage Sites.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
COVID-19, caused by the highly contagious SARS-CoV-2 virus, is distinguished by its positive-sense, single-stranded RNA genome. A thorough understanding of SARS-CoV-2 pathogenesis is crucial for halting its proliferation. Notably, the 3C-like proteas...
DeepCOVIDNet-CXR: deep learning strategies for identifying COVID-19 on enhanced chest X-rays.
Biomedizinische Technik. Biomedical engineering
Oct 8, 2024
OBJECTIVES: COVID-19 is one of the recent major epidemics, which accelerates its mortality and prevalence worldwide. Most literature on chest X-ray-based COVID-19 analysis has focused on multi-case classification (COVID-19, pneumonia, and normal) by ...
Perceived Impact of COVID-19 in an Underserved Community: A Natural Language Processing Approach.
Journal of advanced nursing
Oct 7, 2024
AIM: To utilise natural language processing (NLP) to analyse interviews about the impact of COVID-19 in underserved communities and to compare it to traditional thematic analysis in a small subset of interviews.
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