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

 Min Kim  ;  Younghyun Kang  ;  Seng Chan You  ;  Hyung-Deuk Park  ;  Sang-Soo Lee  ;  Tae-Hoon Kim  ;  Hee Tae Yu  ;  Eue-Keun Choi  ;  Hyoung-Seob Park  ;  Junbeom Park  ;  Young Soo Lee  ;  Ki-Woon Kang  ;  Jaemin Shim  ;  Jung-Hoon Sung  ;  Il-Young Oh  ;  Jong Sung Park  ;  Boyoung Joung 
 SCIENTIFIC REPORTS, Vol.12(1) : 37, 2022-01 
Journal Title
Issue Date
Aged ; Aged, 80 and over ; Artificial Intelligence* ; Atrial Fibrillation / diagnosis* ; Atrial Fibrillation / therapy ; Clinical Decision Rules ; Cohort Studies ; Female ; Humans ; Machine Learning ; Male ; Models, Statistical ; Pacemaker, Artificial ; Prospective Studies ; ROC Curve ; Registries ; Republic of Korea
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.
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1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Min(김민) ORCID logo https://orcid.org/0000-0002-8132-9873
Kim, Tae-Hoon(김태훈) ORCID logo https://orcid.org/0000-0003-4200-3456
You, Seng Chan(유승찬) ORCID logo https://orcid.org/0000-0002-5052-6399
Yu, Hee Tae(유희태) ORCID logo https://orcid.org/0000-0002-6835-4759
Joung, Bo Young(정보영) ORCID logo https://orcid.org/0000-0001-9036-7225
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