Infection Control

Development and validation of an interpretable machine learning model for predicting in-hospital mortality among critically ill patients with liver cirrhosis.

This study aimed to develop and validate an interpretable machine learning model for early prediction of in-hospital mortality in critically ill patients with liver cirrhosis to optimize risk stratification and individualized clinical intervention. A total of 5358 cirrhotic ICU patients were retrospectively selected from the MIMIC-IV database and randomly divided into training and internal validat...

Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.

BACKGROUND: Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to prolonged hospitalization, increased costs, and increased mortality. However, simple and reliable preoperative predictors remain limited. Therefore, this study aimed to develop and validate a hematology-based machine learning model for the early predi...

Machine Learning-Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study.

BACKGROUND: Effective risk stratification in sepsis remains a critical clinical challenge. Serum lactate is a cornerstone biomarker of metabolic dysfu...

Enhancing out-of-hospital emergency care via lexical machine learning modeling of chief complaints.

Emergency medical services (EMS) professionals make high-stakes decisions in austere environments. To support out-of-hospital emergency care, we devel...

A covalently adaptive polymer welding strategy enabling integrated electronics for pre-hospital monitoring of myocardial infarction.

Myocardial infarction (MI) is a major contributor to cardiovascular diseases (CVDs), creating an urgent demand for wireless, real-time, and continuous...

Optimizing stroke mortality prediction: a comparative study of sampling, cost-sensitive learning, and threshold adjustment.

BACKGROUND: Stroke is a leading cause of mortality and disability worldwide, creating a critical need for accurate prediction tools to support early c...

Use of an AI Image Recognition-Based Smartphone App in Personalized Bowel Preparation Before Colonoscopy: Prospective Randomized Controlled Trial.

BACKGROUND: Sufficient bowel preparation is critical for increasing the quality of colonoscopy. However, current bowel preparation guidelines have lim...

An interpretable machine learning model for early prediction of ESBL-producing bacteraemia in the emergency department.

Empirical antibiotic therapy in the emergency department (ED) is often initiated before susceptibility results are available, risking treatment failur...

Deep learning-based segmentation and subtype prediction of renal cell carcinoma on contrast-enhanced CT.

Accurate preoperative discrimination of renal cell carcinoma (RCC) subtypes is critical for treatment stratification. We aimed to develop and validate...

Enhancing mortality prediction in septic ICU patients with malignancies through ensemble machine learning: a retrospective cohort study.

Sepsis remains a leading cause of mortality in the intensive care unit (ICU), and patients with underlying malignancies are disproportionately affecte...

Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.

Multiomics, next-generation, and long-read sequencing approaches have transformed the practice of medical genetics. Complex cases often require severa...

Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage.

Machine learning models that predict hospital admission at triage may support patient flow forecasting, yet the effects of covariate drift, concept dr...

Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.

BACKGROUND: Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most exis...

Trigger tool methodologies in hospital settings: reappraising their role in pharmacovigilance and digital safety surveillance.

INTRODUCTION: Trigger tool methodologies have become important approaches for detecting adverse events in hospital care because they identify more har...

Using Encounter-Level Data for Risk-Adjustment of Antimicrobial Use Comparisons: Feasibility and Variable Selection.

BACKGROUND: External comparisons of hospital antimicrobial use (AU), risk-adjusted using encounter characteristics, may better inform antimicrobial st...

Using machine learning to discriminate non-classical 21-hydroxylase deficiency from polycystic ovary syndrome: an external validation study.

INTRODUCTION: Nonclassical 21-hydroxylase deficiency (NC21OHD) is a rare autosomal recessive disorder that is frequently misdiagnosed as polycystic ov...

Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data.

BACKGROUND: Limited data availability and privacy constraints hinder the development of robust survival prediction models for personalized treatment. ...

Hospital system resilience in the face of armed conflicts; a cross sectional study from two regions in Lebanon.

BACKGROUND: Since 2020, Lebanon has faced a succession of financial, health, and security crises that have severely weakened its hospital system. In t...

Identifying high-risk patient clusters for falls using unsupervised machine learning on linked primary and secondary care electronic health record data.

Falls risk is multifactorial, involving a combination of clinical and sociodemographic factors. Although guidelines acknowledge this complexity, most ...

Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.

Ischemic heart disease remains a major contributor to mortality in Malaysia, with non-elective percutaneous coronary intervention (PCI) frequently per...