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

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 method)....

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Detalles Bibliográficos
Autores principales: Tateishi, Ryo, Suzuki, Makoto, Shimizu, Masato, Shimada, Hiroshi, Tsunoda, Takahiro, Miyazaki, Hiroko, Misu, Yoshiki, Yamakami, Yosuke, Yamaguchi, Masao, Kato, Nobutaka, Isshiki, Ami, Kimura, Shigeki, Fujii, Hiroyuki, Nishizaki, Mitsuhiro, Sasano, Tetsuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638417/
https://www.ncbi.nlm.nih.gov/pubmed/37949876
http://dx.doi.org/10.1038/s41598-023-46095-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638417/
https://www.ncbi.nlm.nih.gov/pubmed/37949876
http://dx.doi.org/10.1038/s41598-023-46095-y

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