Risk prediction of inappropriate implantable cardioverter-defibrillator therapy using machine learning

Risk prediction of inappropriate implantable cardioverter-defibrillator therapy using machine learning

Abstract We aimed to develop machine learning-based predictive models for identifying inappropriate implantable cardioverter-defibrillator (ICD) therapy. Our study included 182 consecutive cases (average age 62.2 ± 4.5 years, 169 men) and employed 14 non-deep learning models for prediction (hold-out...

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Bibliographic Details
Main Authors: Ryo Tateishi, Makoto Suzuki, Masato Shimizu, Hiroshi Shimada, Takahiro Tsunoda, Hiroko Miyazaki, Yoshiki Misu, Yosuke Yamakami, Masao Yamaguchi, Nobutaka Kato, Ami Isshiki, Shigeki Kimura, Hiroyuki Fujii, Mitsuhiro Nishizaki, Tetsuo Sasano
Format: Article
Language:English
Published: Nature Portfolio 2023-11-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-46095-y

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https://doi.org/10.1038/s41598-023-46095-y

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