Hospitalists

Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.

BACKGROUND: Multicenter electronic health records (EHRs) can support quality improvement and comparative effectiveness research in critical care. However, limitations of EHR-based research include challenges in abstracting key clinical variables, including a patient's level of consciousness. OBJECTIVE: This study aimed to develop a natural language processing model to predict Glasgow Coma Scale (G...

Effects of Telerehabilitation Based on Motion Recognition Technology on Exercise Endurance of Patients With Non-Small Cell Lung Cancer After Surgery: Single-Center, Prospective, Open-Label, Randomized Controlled Trial.

BACKGROUND: Non-small cell lung cancer (NSCLC) accounts for approximately 85% of primary pulmonary neoplasms. Complete surgical removal remains the cornerstone of curative therapy, yet it frequently diminishes residual lung function and exercise tolerance. Structured, center-based rehabilitation hastens physiological recovery, but conventional schemes rarely deliver continuous, patient-specific mo...

Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.

BACKGROUND: Ovarian cancer patients requiring intensive care unit (ICU) admission face particularly grave prognosis, yet current prognostic models rel...

Identifying subgroups of ICU patients with high mortality rates using machine learning: A nationwide, population-based study.

PURPOSE: Identifying subgroups of intensive care unit (ICU) patients with high mortality rates can provide directions in policy making about appropria...

White cell - platelet ratio: A strong indicator for early mortality in liver cirrhosis patients with esophagogastric varices.

Esophagogastric varices (EGV) in liver cirrhosis patients within the intensive care unit (ICU) is a significant medical concern. This study aims to de...

Predicting the flood susceptibility under land use and climate change scenarios using deep learning algorithms.

Flood risk in semi-arid, snow-fed basins is increasingly influenced by both land-use and climate change, yet their combined future effects remain poor...

Development and external validation of an interpretable machine learning model for predicting prolonged postoperative ICU length of stay in coronary artery bypass grafting patients using MIMIC-IV 3.1 and eICU-CRD 2.0.

BACKGROUND: Prolonged postoperative intensive care unit (ICU) length of stay (LOS) after coronary artery bypass grafting (CABG) drives resource use ye...

Machine learning models to predict ICU admission of patients with COPD and depression: A retrospective cohort study.

This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (...

Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.

Accurate prognostication in the intensive care unit (ICU) is essential for delivering personalized and ethically sound care, yet it remains a challeng...

Development and validation of machine-learning diagnostic models for identifying frailty in older adults with tuberculosis: a multicentre observational study protocol.

BACKGROUND: The Frailty in Tuberculosis (FIT) study aims to assess frailty in older adults with tuberculosis (TB) using machine learning (ML) to devel...

CT Quantification of Intraventricular Hemorrhage Volume in Poor-Grade Aneurysmal Subarachnoid Hemorrhage: Impact on Mortality and Long-Term Disability.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with high morbidity and mortality, particularly in poor-grade pa...

Predictors of same-day discharge following bariatric surgery using machine learning.

BACKGROUND: Same-day discharge (SDD) following bariatric surgery is becoming increasingly more common to reduce healthcare utilization. However, predi...

Association between dynamic digital radiography findings and post-extubation respiratory deterioration: A retrospective exploratory analysis of a prospectively collected ICU cohort.

INTRODUCTION: Dynamic Digital Radiography (DDR) is a novel bedside imaging modality that enables real-time visualization of pulmonary motion with mini...

Multimedia ecological risk deciphering and sediment source apportionment of potentially toxic elements in a typical coastal bay: A combined PMF-SOM-GIS approach.

As transitional zones of landsea interactions, bays are facing increasingly severe anthropogenic pollution pressures. To systematically assess these i...

Integrating 2.5D multi-modal CT imaging with serum triglycerides for early risk stratification in hypertriglyceridemia-induced acute pancreatitis: A multicenter study.

OBJECTIVES: To develop and externally validate a clinical-radiological framework that fuses 2.5D deep learning features from dual-phase computed tomog...

Interpretable Machine Learning for Early Risk Stratification of Carbapenem Resistance Among ICU Patients with Sterile-Site Pseudomonas aeruginosa Isolates: Development and Internal Validation Using MIMIC-IV.

OBJECTIVES: We developed and internally validated an interpretable machine learning model to stratify carbapenem-resistance probability among ICU pati...

Deep learning-based RUL and SOH prediction of lithium-ion batteries using LOCO.

Degradation in lithium-ion batteries negatively affects the reliability, safety, and cost-effectiveness of modern energy storage systems, making accur...

Towards autonomous medical artificial intelligence agents.

Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather t...

Spatial distribution of per- and polyfluoroalkyl substances in the Yellow River basin: Role of suspended sediment on the PFAS partition and accumulation.

The widespread occurrence of per- and polyfluoroalkyl substances (PFAS) and emerging substitutes in riverine environments has raised growing concern, ...

Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.

BACKGROUND: Heart failure (HF) is a leading cause of hospitalization and readmission. Cardiac implantable electronic devices (CIEDs) continuously capt...