ArticlePloS one2020
Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 5 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Technological Integration in Triage With AI and Machine Learning Compared With Conventional Triage and Narrative Synthesis of Telehealth for Patient Prioritization in Emergency and Acute Care Settings: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled 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
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- The Use of Machine Learning in Emergency Care Units: A Systematic Review.Journal of primary care & community healthPooled it
- Integrating the interpretable machine learning Score for Emergency Risk Prediction (SERP) with emergency department triage to better predict 30-day mortality.Clinical and experimental emergency medicine · 2026Article
- Deep learning-based natural language processing for critical care identification in pediatric emergency department.BMC emergency medicine · 2026Article
- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- Article
- Machine learning and SHAP-based risk assessment of PICC-related bloodstream infections in premature infants at the time of clinical suspicion.Pediatric research · 2026Article
- Does Provider Identity at Triage Improve Machine Learning Prediction of Hospital Admission? A Comparative Analysis of Ten Supervised Classifiers with SHAP Explainability.Journal of personalized medicine · 2026Article
- Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study.JMIR AI · 2026Article
- AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery.NPJ digital medicine · 2025Article
- Development and internal validation of an AI-based emergency triage model for predicting critical outcomes in emergency department.Scientific reports · 2025Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Primer on large language models: an educational overview for intensivists.Critical care (London, England) · 2025Review
- Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study.Journal of medical Internet research · 2025Observational
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis.Health science reports · 2025Article
- Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.Current cardiology reports · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model-using only triage priorities-with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission.
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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.