Infectious Disease

Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.

Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.

OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled dat...

A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.

Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-...

Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.

The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of re...

Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations.

INTRODUCTION: Despite advances in diagnostic technologies for tuberculosis (TB), global control of this disease requires improved technologies for act...

Systematic collection, annotation, and pattern analysis of viral vaccines in the VIOLIN vaccine knowledgebase.

BACKGROUND: Viral vaccines have been proven significant in protecting us against viral diseases such as COVID-19. To better understand and design vira...

Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety.

While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and depl...

MSCMamba: Prediction of Antimicrobial Peptide Activity Values by Fusing Multiscale Convolution with Mamba Module.

Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been de...

Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .

Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multid...

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.

BACKGROUND: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availabi...

Discovery of TRPV4-Targeting Small Molecules with Anti-Influenza Effects Through Machine Learning and Experimental Validation.

Transient receptor potential vanilloid 4 (TRPV4) is a calcium-permeable cation channel critical for maintaining intracellular Ca homeostasis and is es...

Future prospective of AI in drug discovery.

Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and ...

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsaturated fats and antioxidants, lowering the risk of ca...

Two-stage CNN-based framework for leukocytes classification.

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide path...

Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides.

Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AM...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces t...

Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.

BACKGROUND: The COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbat...

Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.

Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicate...

Machine learning for predicting severe dengue in Puerto Rico.

BACKGROUND: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health ...

The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe.

The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to...