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Machine learning with clinical and intraoperative biosignal data for predicting postoperative delirium after cardiac surgery

Authors
 Changho Han  ;  Hyun Il Kim  ;  Sarah Soh  ;  Ja Woo Choi  ;  Jong Wook Song  ;  Dukyong Yoon 
Citation
 ISCIENCE, Vol.27(6) : 109932, 2024-06 
Journal Title
ISCIENCE
Issue Date
2024-06
Keywords
Bioinformatics ; Machine learning
Abstract
Early identification of patients at high risk of delirium is crucial for its prevention. Our study aimed to develop machine learning models to predict delirium after cardiac surgery using intraoperative biosignals and clinical data. We introduced a novel approach to extract relevant features from continuously measured intraoperative biosignals. These features reflect the patient's overall or baseline status, the extent of unfavorable conditions encountered intraoperatively, and beat-to-beat variability within the data. We developed a soft voting ensemble machine learning model using retrospective data from 1,912 patients. The model was then prospectively validated with data from 202 additional patients, achieving a high performance with an area under the receiver operating characteristic curve of 0.887 and an accuracy of 0.881. According to the SHapley Additive exPlanation method, several intraoperative biosignal features had high feature importance, suggesting that intraoperative patient management plays a crucial role in preventing delirium after cardiac surgery.
DOI
10.1016/j.isci.2024.109932
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyun IL(김현일) ORCID logo https://orcid.org/0000-0003-2665-8620
Soh, Sa Rah(소사라) ORCID logo https://orcid.org/0000-0001-5022-4617
Song, Jong Wook(송종욱) ORCID logo https://orcid.org/0000-0001-7518-2070
Yoon, Dukyong(윤덕용)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/200821
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