Infection Control

Why are rural hospitals closing in the U.S.? Predictors identified using explainable machine learning.

OBJECTIVE: Rural hospital closures in the U.S. reduce access to essential healthcare services and worsen health and economic outcomes in rural communities. This study uses national longitudinal data and explainable machine learning (XML) to identify and interpret risk factors of rural hospital closures. MATERIALS AND METHODS: We conducted a retrospective longitudinal study of U.S. rural hospitals ...

Investigating Infection and Clinical Data to Predict Effective Antibiotic Therapy and Monitor Resistance Trends.

BACKGROUND: Data-driven approaches to effectively select antibiotics are crucial to improving patient outcomes and reducing antibiotic resistance. This study aimed to determine whether routinely collected clinical and microbiological data can be used to train machine learning (ML) models to predict antibiotic resistance in patients with bacterial infections. METHODS: We conducted a retrospective o...

Third-party governance and artificial intelligence for diabetes management in primary health care, China.

PROBLEM: Fragmented primary health care in China fails to tackle the growing burden of noncommunicable diseases. Despite substantial investment, fewer...

Enhancing healthcare information security through VAE-driven anomaly detection in EHR access patterns.

HIPAA breaches and unauthorized access to Electronic Health Records (EHRs) have been growing more likely due to the sudden digitalization of the healt...

An In-Hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting Based on Machine Learning: Cohort Study.

BACKGROUND: Ischemic heart disease remains the leading cause of death worldwide. Coronary artery bypass grafting (CABG) remains the primary surgical t...

Optimizing heart failure care: A machine learning-based prediction of hospital length of stay for heart failure patients.

BACKGROUND: Heart failure represents a significant global health burden, with prolonged length of stay (LoS) tied to increased mortality and costs. Ac...

Application of AI-assisted PBL-CBL combined teaching in the diagnosis and treatment of post-herpetic neuralgia: a randomised controlled trial.

BACKGROUND: This study aimed to explore the efficacy of a teaching model integrating artificial intelligence-assisted problem-based learning (PBL) wit...

Environmental Antibiotic Contamination and AMR: Integrating Pathways, Impacts, and AI-Driven Mitigation.

The widespread contamination of the environment with antibiotic residues is a significant factor contributing to the global crisis of antimicrobial re...

Prediction of 30-Day All-Cause Hospital Readmissions Using Limited Structured Electronic Health Record Data: Retrospective Comparative Study.

BACKGROUND: Unplanned hospital readmissions represent a critical operational and financial challenge for health care systems in the United States, wit...

Integrating ARIMA and Deep Learning Models for Counterfactual Evaluation of Nirsevimab's Early Impact on RSV Infant Admissions.

This study presents a hybrid modelling framework that integrates classical statistical methods (ARIMA) with deep learning architectures to enhance lon...

Machine Learning Models for Predicting Mortality Risk and Survival Time in Lung Cancer Patients Treated with EGFR-TKIs.

This study develops machine learning models to predict patient mortality and estimate survival time using electronic health record (EHR) data from thr...

Evaluation of Graph-Based Algorithms for Early Detection of In-Hospital Mortality.

Enhancing patient care quality depends on preventing health-related adverse events (HAEs), including in-hospital mortality, right from the start of ho...

Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.

BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromi...

Postangiography Prediction of Renal Replacement Therapy in Acute Myocardial Infarction-Related Cardiogenic Shock: Least Absolute Shrinkage and Selection Operator Nomogram Development and Validation.

BACKGROUND: Acute kidney injury critically impacts outcomes in cardiogenic shock secondary to acute myocardial infarction (CS-AMI). Acute kidney injur...

Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study.

BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a sh...

Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand.

BACKGROUND: The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in emergency dep...

Diagnostic value of cytokine detection in children with Mycoplasma pneumoniae pneumonia complicated by bacterial or viral co-infections.

BACKGROUND: To evaluate the diagnostic value of cytokine levels in paediatric patients with Mycoplasma pneumoniae pneumonia (MPP) complicated by bacte...

Predicting dynamic individual out-of-hospital cardiac arrest risks using explainable machine learning: a multicenter study in China.

Out-of-hospital cardiac arrest (OHCA) poses a significant public health challenge, with limited tools available for dynamic risk estimation under chan...

Machine learning using PROFUND components for 30-day readmission prediction in multimorbid patients: a prospective multicentre study.

Early hospital readmission in multimorbid patients remains a major clinical challenge. Although risk stratification tools are widely used, predictive ...

Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals' perspectives.

Explainable Artificial Intelligence (XAI) has the potential to enhance clinical decision support (CDS) systems however, it remains unclear how XAI sys...