Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model

Jae Yong Yu, Doyeop Kim, Sunyoung Yoon, Taerim Kim, Se Jin Heo, Hansol Chang, Gab Soo Han, Kyung Won Jeong, Rae Woong Park, Jun Myung Gwon, Feng Xie, Marcus Eng Hock Ong, Yih Yng Ng, Hyung Joon Joo, Won Chul Cha

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients’ ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital’s score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858–0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy.

Original languageEnglish (US)
Article number6666
JournalScientific reports
Volume14
Issue number1
DOIs
StatePublished - Mar 2024
Externally publishedYes

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© The Author(s) 2024.

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