계명대학교 의학도서관 Repository

Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices

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Author(s)
Min KimYounghyun KangSeng Chan YouHyung-Deuk ParkSang-Soo LeeTae-Hoon KimHee Tae YuEue-Keun ChoiHyoung-Seob ParkJunbeom ParkYoung Soo LeeKi-Woon KangJaemin ShimJung-Hoon SungIl-Young OhJong Sung ParkBoyoung Joung
Keimyung Author(s)
Park, Hyoung Seob
Department
Dept. of Internal Medicine (내과학)
Journal Title
Sci Rep
Issued Date
2022
Volume
12
Issue
1
Abstract
To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the clinical parameters of patients with implanted pacemakers in comparison to logistic regression (LR). We included 721 patients without known atrial fibrillation or atrial flutter from a prospective multicenter (11 tertiary hospitals) registry comprising all geographical regions of Korea from September 2017 to July 2020. Predictive models of clinically relevant AHREs were developed using the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm. Model prediction training was conducted by seven hospitals, and model performance was evaluated using data from four hospitals. During a median follow-up of 18 months, clinically relevant AHREs were noted in 104 patients (14.4%). The three ML-based models improved the discrimination of the AHREs (area under the receiver operating characteristic curve: RF: 0.742, SVM: 0.675, and XGB: 0.745 vs. LR: 0.669). The XGB model had a greater resolution in the Brier score (RF: 0.008, SVM: 0.008, and XGB: 0.021 vs. LR: 0.013) than the other models. The use of the ML-based models in patient classification was associated with improved prediction of clinically relevant AHREs after pacemaker implantation.
Keimyung Author(s)(Kor)
박형섭
Publisher
School of Medicine (의과대학)
Citation
Min Kim et al. (2022). Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices. Sci Rep, 12(1), 37. doi: 10.1038/s41598-021-03914-4
Type
Article
ISSN
2045-2322
Source
https://www.nature.com/articles/s41598-021-03914-4
DOI
10.1038/s41598-021-03914-4
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/44220
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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