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Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

Authors
 Arom Choi  ;  So Yeon Choi  ;  Kyungsoo Chung  ;  Hyun Soo Chung  ;  Taeyoung Song  ;  Byunghun Choi  ;  Ji Hoon Kim 
Citation
 SCIENTIFIC REPORTS, Vol.13(1) : 8561, 2023-05 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2023-05
MeSH
Clinical Deterioration* ; Decision Support Systems, Clinical* ; Emergency Service, Hospital ; Heart Arrest* / diagnosis ; Humans ; Machine Learning ; Retrospective Studies
Abstract
This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care. © 2023, The Author(s).
Files in This Item:
T202400715.pdf Download
DOI
10.1038/s41598-023-35617-3
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Emergency Medicine (응급의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Ji Hoon(김지훈) ORCID logo https://orcid.org/0000-0002-0070-9568
Jung, Kyung Soo(정경수) ORCID logo https://orcid.org/0000-0003-1604-8730
Chung, Hyun Soo(정현수) ORCID logo https://orcid.org/0000-0001-6110-1495
Choi, So Yeon(최소연)
Choi, Arom(최아롬)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/197990
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