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COVID-19

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AI-powered mechanisms as judges: Breaking ties in chess.

PloS one
Recently, Artificial Intelligence (AI) technology use has been rising in sports to reach decisions of various complexity. At a relatively low complexity level, for example, major tennis tournaments replaced human line judges with Hawk-Eye Live techno...

Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.

IET systems biology
Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elde...

A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis.

IEEE transactions on neural networks and learning systems
The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mut...

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG.

Biosensors
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples t...

The power of artificial intelligence for managing pandemics: A primer for public health professionals.

The International journal of health planning and management
Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the ke...

Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.

Pathogens (Basel, Switzerland)
Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed wi...

Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.

International journal of environmental research and public health
Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that individuals who smoke are more susceptible to COVID-19. In this study, we aimed to investigate the influe...

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders.

Methods (San Diego, Calif.)
HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limite...

Vaccine development using artificial intelligence and machine learning: A review.

International journal of biological macromolecules
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
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...