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Yu JY, Kim D, Yoon S, Kim T, Heo S, Chang H, Han GS, Jeong KW, Park RW, Gwon JM, Xie F, Ong MEH, Ng YY, Joo HJ, Cha WC. Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model. Sci Rep 2024; 14:6666. [PMID: 38509133 PMCID: PMC10954621 DOI: 10.1038/s41598-024-54364-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/12/2024] [Indexed: 03/22/2024] Open
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.
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Affiliation(s)
- Jae Yong Yu
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Doyeop Kim
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Sunyoung Yoon
- Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Taerim Kim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, Republic of Korea
| | - SeJin Heo
- 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
| | - Hansol Chang
- 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
| | - Gab Soo Han
- Department of Cardiology, Cardiovascular Center, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Kyung Won Jeong
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Rae Woong Park
- 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
| | - Jun Myung Gwon
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, Incheon, Republic of Korea
| | - Feng Xie
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, USA
| | - Marcus Eng Hock Ong
- 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
| | - Yih Yng Ng
- Digital and Smart Health Office, Tan Tock Seng Hospital, Singapore, Singapore
| | - Hyung Joon Joo
- Department of Cardiology, Cardiovascular Center, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Won Chul Cha
- 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.
- Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea.
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