AIMC Topic: COVID-19

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Applying spectral analysis to the arterial pulse to discriminate cardiovascular side effects following administration of Moderna's mRNA-1273 vaccine.

European journal of pharmacology
Vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have demonstrated strong efficacy in preventing symptomatic disease, but adverse cardiovascular side effects have been reported. This study investigated whether noninvasive...

Artificial intelligence in biology and medicine.

Die Naturwissenschaften
This article explores the role of artificial intelligence (AI) in medicine and biology. Special attention is given to areas of biology such as genomics, proteomics, biotechnology, cell, and synthetic biology. In the field of medicine, the emphasis is...

Lactate/albumin ratio predicts mortality in critically ill COVID-19 patients: a retrospective machine learning study.

Scientific reports
Severe COVID-19 often progresses to critical illness, requiring accurate prognostic biomarkers. Lactate-to-albumin ratio (LAR) has been proposed as a novel indicator to estimate the likelihood of death. Using data from the MIMIC database, this retros...

Enhancing explainability in epidemiological predictions using fuzzy logic integrated with machine and deep learning algorithms.

Scientific reports
Epidemiological data is often analyzed without fully accounting for the uncertainties that are key to understanding the nuances of the dataset. While traditional approaches like the SIR mathematical model provide valuable insights, our study aims to ...

Lung Ultrasound Imaging Dataset for Accurate Detection and Localization of LUS Vertical Artifact.

Scientific data
Lung ultrasound (LUS) vertical artifacts are critical sonographic markers commonly used in evaluating pulmonary conditions such as pulmonary edema, interstitial lung disease, pneumonia, and COVID-19. Accurate detection and localization of these artif...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting.

BMC primary care
BACKGROUND: Establishing risk factors associated with severity and prognosis in the early stages of the disease is important to identify patients who need specialized care. Creating new clinical tools to improve health decisions and outcomes in the p...

Bibliometric Insights Into the Infodemic: Global Research Trends and Policy Responses: Quantitative Research.

JMIR medical informatics
BACKGROUND: Amidst the COVID-19 pandemic, the proliferation of misinformation on social media, termed the "infodemic," has complicated global health responses.

FragOPT: An ML-Driven Computational Workflow for Rational Fragments Optimization Toward Lead Compounds.

Journal of chemical information and modeling
Advances in machine learning (ML) offer significant potential to accelerate drug discovery. Although mathematical modeling and ML have become crucial in predicting drug-target interactions and properties, the complexity of chemical space and the "bla...

Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic.

Vaccine
Understanding vaccine hesitancy is a critical public health challenge, yet traditional statistical methods often fail to capture the complex drivers behind it. This study uses COVID-19 vaccine hesitancy in a rural population as a case study to demons...

Deep phenotyping of patient lived experience in functional bowel disorders using machine learning.

Scientific reports
Contemporary clinical management relies on a diagnostic label as the primary guide to treatment. However, individual patients' lived experiences vary more widely than standard diagnostic categories reflect. This is especially true for functional bowe...