ArticleScientific data2022
Benchmarking emergency department prediction models with machine learning and public electronic health records.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial.Nature communications · 2025Trial
- Disparate privacy risks from medical AI.Nature · 2026Article
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- Performance fairness of neural network models in early risk assessment of inpatients with varying severity: a retrospective study.BMC medical informatics and decision making · 2026Article
- Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Survival Modeling Using Deep Learning, Machine Learning, and Statistical Methods: A Comparative Analysis for Predicting Mortality After Hospital Admission.Health data science · 2026Article
- Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis.Medical journal of the Islamic Republic of Iran · 2026Review
- Benchmarking Foundation Models with Multimodal Public Electronic Health Records.IEEE journal of biomedical and health informatics · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Benchmarking large language models for personalized, biomarker-based health intervention recommendations.NPJ digital medicine · 2025Article
- Predictive Model for Managing the Clinical Risk of Emergency Department Patients: A Systematic Review.Journal of clinical medicine · 2025Review
- Risk Factors and Development of a Predictive Model for In-Hospital Mortality in Hemodynamically Stable Older Adults with Urinary Tract Infection.Medicina (Kaunas, Lithuania) · 2025Article
- Clinical decision support using pseudo-notes from multiple streams of EHR data.NPJ digital medicine · 2025Article
- Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.BMJ health & care informatics · 2025Article
- Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data.Journal of medical systems · 2025Article
- Article
- Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.BMC emergency medicine · 2025Article
- Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review.Healthcare informatics research · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.