ArticleScientific reports2022
An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial.PLoS medicine · 2026Trial
- 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
- Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Acute biological age as a determinant of adverse outcomes requiring hospitalization in Danish emergency department patients.Communications medicine · 2026Article
- Article
- Assessing Risk in Implementing New Artificial Intelligence Triage Tools-How Much Risk is Reasonable in an Already Risky World?Asian bioethics review · 2025Article
- Prospective and External Validation of Machine Learning Models for Short- and Long-Term Mortality in Acutely Admitted Patients Using Blood Tests.Journal of clinical medicine · 2024Article
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7 authors.
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Abstract
Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients' ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department.
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