AIMC Topic: Emergency Service, Hospital
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Can AI match emergency physicians in managing common emergency cases? A comparative performance evaluation.
BMC emergency medicine
Jul 31, 2025
BACKGROUND: Large language models (LLMs) such as ChatGPT are increasingly explored for clinical decision support. However, their performance in high-stakes emergency scenarios remains underexamined. This study aimed to evaluate ChatGPT's diagnostic a...
Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.
BMC medical informatics and decision making
Jul 28, 2025
INTRODUCTION: Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages...
Evaluating GPT-4's role in critical patient management in emergency departments.
PloS one
Jul 24, 2025
INTRODUCTION: Recent advancements in artificial intelligence (AI) have introduced tools like ChatGPT-4, capable of interpreting visual data, including ECGs. In our study,we aimed to investigate the effectiveness of GPT-4 in interpreting ECGs and mana...
Artificial intelligence platform to predict children's hospital care for respiratory disease using clinical, pollution, and climatic factors.
Journal of global health
Jul 21, 2025
BACKGROUND: Hospitals and health care systems may benefit from artificial intelligence (AI) and big data to analyse clinical information combined with external sources. Machine learning, a subset of AI, uses algorithms trained on data to generate pre...
Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department.
BMC emergency medicine
Jul 15, 2025
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...
Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.
Scientific reports
Jul 2, 2025
This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. ...
Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.
Scientific reports
Jul 1, 2025
Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 data in Colorado from October 2020 to March 2024 to reconstruct seven real-time surveillance indicators...
A machine learning model using clinical notes to identify physician fatigue.
Nature communications
Jul 1, 2025
Clinical notes should capture important information from a physician-patient encounter, but they may also contain signals indicative of physician fatigue. Using data from 129,228 emergency department (ED) visits, we train a model to identify notes wr...
Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.
BMJ health & care informatics
Jun 26, 2025
OBJECTIVE: Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different site...
Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine learning approach.
BMJ open
Jun 17, 2025
OBJECTIVES: Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Gi...
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