Hospital-Based Medicine

Diagnostic utility of the platelet-to-thrombin-antithrombin complex ratio in heatstroke: a single-center retrospective analysis.

PURPOSE: To assess the discriminatory performance of the platelet-to-thrombin-antithrombin complex ratio (PLT/TAT) for differentiating heatstroke from heat exhaustion and to explore its association with 28-day prognosis and disease severity. METHODS: This retrospective study analyzed 100 heat illness patients from 2018 to 2025, including 45 with heat exhaustion and 55 with heatstroke. Baseline and...

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. Accurate prediction of hospital LoS is crucial for improving resource allocation, lowering mortality and readmission rates, and enhancing patient care. OBJECTIVES: This study leverages machine learning (ML) models to predict LoS categories (Short: 1-3 ...

Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance.

Augmented renal clearance (ARC) frequently occurs in critically ill septic patients and is known to impact survival outcomes. To address this, we aime...

Microbial systems for azo dye biodegradation: enzymatic mechanisms, microbial consortia, and emerging biotechnological strategies.

Azo dyes are the most widely used class of synthetic colorants in textile and related industries; however, their discharge into natural ecosystems pos...

Beyond readability: Advancing patient-centered evaluation of AI-generated clinical reports.

Large language models offer promising opportunities to simplify clinical documentation and improve the accessibility of medical information for patien...

Interpretable machine learning to predict postoperative adverse outcomes in cardiac surgery.

BACKGROUND: Cardiac surgery is associated with significant mortality and complication risks. This study aims to develop an interpretable machine learn...

Influencing factors and mediating effects of advanced medical care plan readiness in young and middle-aged patients with advanced cancer based on machine learning.

OBJECTIVE: To examine the factors influencing advance care planning (ACP) readiness and its correlation with disease benefit perception and social sup...

Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI.

Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification i...

Integrating AI into hospital workflows: implementation strategies and real-world barriers.

Artificial intelligence (AI) is increasingly being integrated into hospital systems with the potential to transform clinical workflows, operational ef...

Automated deep learning model for predicting pathological complete response in rectal cancer: A tool to organ-preserving strategies.

BACKGROUND: Accurate identification of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal canc...

Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study.

UNLABELLED: Several severity scores have been developed to assess disease severity in infants with bronchiolitis, but they often lack objectivity and ...

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...

Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

BACKGROUND: Infections are a leading cause of hospitalizations and emergency department (ED) visits in home care. Existing prediction tools often unde...

Understanding Blood-Injection-Injury Phobia: Do Not Miss the Point.

Needle and blood-injection-injury phobia is commonly encountered in the perioperative setting. It can significantly disrupt operating room throughput,...

Automated Machine Learning Approaches for Surgery Duration Prediction in Orthopaedics.

Accurate prediction of surgical case duration is essential for reducing operating room overruns and maximising theatre utilisation. Traditional estima...

Harnessing Clinical Data Streams for Nursing Workload Prediction Using Artificial Intelligence.

The increasing workload in inpatient care and the ongoing shortage of skilled workers require new approaches to demand-oriented personnel planning. A ...

Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review.

This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proac...

Evaluating the Potential of Machine Learning for Discharge Management on Routine Health Insurance Data.

Machine learning (ML) has great potential in healthcare, especially with large structured data. Routine health insurance claims (HIC) data are a valua...

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...

Predicting Pediatric Mortality Across Five Intensive Care Units: Toward an Early Warning Using Machine Learning.

Despite the advances in critical care and innovations of medical technology, earlier identification of children at high mortality risk remains challen...