ArticleScientific reports2021
Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 4 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
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Pooled it
- Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2024Pooled it
- The Use of Machine Learning in Emergency Care Units: A Systematic Review.Journal of primary care & community healthPooled it
- Review
- Beyond diagnosis: deep-learning-based analysis of hospitalization using abdominal radiographs in the emergency department.Abdominal radiology (New York) · 2026Article
- Review
- Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.European heart journal. Digital health · 2026Article
- Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study.JMIR AI · 2026Article
- Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine.Internal and emergency medicine · 2026Article
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- Considerations for emergency department virtual triage.Healthcare management forum · 2025Article
- Prioritizing Trust in Podiatrists' Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study.JMIR human factors · 2025Article
- A pediatric emergency prediction model using natural language process in the pediatric emergency department.Scientific reports · 2025Article
- Early identification of high-risk patients admitted to emergency departments using vital signs and machine learning.World journal of emergency medicine · 2025Article
- Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review.Healthcare informatics research · 2025Article
- Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.Journal of medical Internet research · 2024Article
- Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.Biomimetics (Basel, Switzerland) · 2024Article
- Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review.Cureus · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963-0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624-0.6818), and the specificity was 0.7814 (95% CI 0.7777-0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586-0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244-0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199-0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.
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