KCI등재
SCI
SCIE
SCOPUS
Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
저자
Park Sang Won (Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea.Institute of Medical Science, School of Medicine, Kangwon National University, Chuncheon, Korea.) ; Yeo Na Young (Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea.) ; Kang Seonguk (Department of Convergence Security, Kangwon National University, Chuncheon, Korea.) ; Ha Taejun (Department of Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, Korea.) ; Kim Tae-Hoon (University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea.) ; Lee DooHee (Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea.) ; Kim Dowon (Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea.) ; Choi Seheon (Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea.) ; Kim Minkyu (Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea.) ; Lee DongHoon (Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea.) ; Kim DoHyeon (Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea.) ; Kim Woo Jin (Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea.Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.Department of Internal Medicine, School of Medicine, Kangwon Nati) ; Lee Seung-Joon (Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.) ; Heo Yeon-Jeong (Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.) ; Moon Da Hye (Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.) ; Han Seon-Sook (Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.) ; Kim Yoon (University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea.Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Korea.) ; Choi Hyun-Soo (University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea.Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea.) ; Oh Dong Kyu (Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.) ; Lee Su Yeon (Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.) ; Park MiHyeon (Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.) ; Lim Chae-Man (Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.) ; Heo Jeongwon (Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.)
발행기관
학술지명
Journal of Korean medical science(Journal of Korean Medical Science)
권호사항
발행연도
2024
작성언어
English
주제어
등재정보
KCI등재,SCI,SCIE,SCOPUS
자료형태
학술저널
수록면
1-19(19쪽)
DOI식별코드
제공처
Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department.
Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).
The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP).
Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results.
Conclusion: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.
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