학술논문
Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model.
Document Type
Academic Journal
Author
Yu JY; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.; Kim D; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.; Yoon S; Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea.; Kim T; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, Republic of Korea.; Heo S; Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea.; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, Republic of Korea.; Chang H; Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea.; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, Republic of Korea.; Han GS; Department of Cardiology, Cardiovascular Center, College of Medicine, Korea University, Seoul, Republic of Korea.; Jeong KW; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.; Park RW; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.; Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.; Gwon JM; Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, Incheon, Republic of Korea.; Xie F; Department of Biomedical Data Science, Stanford University, Stanford, USA.; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, USA.; Ong MEH; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore.; Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore.; Ng YY; Digital and Smart Health Office, Tan Tock Seng Hospital, Singapore, Singapore.; Joo HJ; Department of Cardiology, Cardiovascular Center, College of Medicine, Korea University, Seoul, Republic of Korea.; Cha WC; Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea. docchaster@gmail.com.; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, Republic of Korea. docchaster@gmail.com.; Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea. docchaster@gmail.com.
Source
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Subject
Language
English
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.
(© 2024. The Author(s).)
(© 2024. The Author(s).)