Infectious Disease

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications.

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development o...

Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review.

Antimicrobial stewardship programs (ASPs) are essential in optimizing the use of antibiotics to address the global concern of antimicrobial resistance (AMR). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for enhancing ASPs efficiency by improving antibiotic prescription accuracy, resistance prediction, and dosage optimization. This systematic review evaluat...

The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.

BACKGROUND: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods ofte...

Current state and future prospects of Horizontal Gene Transfer detection.

Artificial intelligence (AI) has been shown to be beneficial in a wide range of bioinformatics applications. Horizontal Gene Transfer (HGT) is a drivi...

Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI.

BACKGROUND: The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 mi...

Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.

BACKGROUND: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models...

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial...

Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability.

Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical resp...

Machine Learning Methods Based on Chest CT for Predicting the Risk of COVID-19-Associated Pulmonary Aspergillosis.

RATIONALE AND OBJECTIVES: To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillu...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. ...

Deep Learning for Antimicrobial Peptides: Computational Models and Databases.

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is b...

Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study.

BACKGROUND: Salmonella typhimurium infection is a considerable global health concern, particularly in children, where it often leads to persistent dia...

Understanding Providers' Attitude Toward AI in India's Informal Health Care Sector: Survey Study.

BACKGROUND: Tuberculosis (TB) is a major global health concern, causing 1.5 million deaths in 2020. Diagnostic tests for TB are often inaccurate, expe...

Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch.

Semi-supervised learning effectively mitigates the lack of labeled data by introducing extensive unlabeled data. Despite achieving success in respirat...

Forecasting Epidemic Spread With Recurrent Graph Gate Fusion Transformers.

Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as g...

RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS).

Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction associated with significant mortality and morbidity among critically ill pati...

TFinder App: Artificial intelligence to diagnose tick fever agents and assess parasitemia/bacteremia in bovine blood smears.

Due to the importance of diagnosing tick fever (TF) agents and their parasitemia in the field to provide appropriate treatment, the objective of this ...

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to th...

A numerical treatment through Bayesian regularization neural network for the chickenpox disease model.

OBJECTIVES: The current research investigations designates the numerical solutions of the chickenpox disease model by applying a proficient optimizati...

ResGloTBNet: An interpretable deep residual network with global long-range dependency for tuberculosis screening of sputum smear microscopy images.

Tuberculosis is a high-mortality infectious disease. Manual sputum smear microscopy is a common and effective method for screening tuberculosis. Howev...