Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective...

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Detalles Bibliográficos
Autores principales: Kim, Seong-Hwan, Jeon, Eun-Tae, Yu, Sungwook, Oh, Kyungmi, Kim, Chi Kyung, Song, Tae-Jin, Kim, Yong-Jae, Heo, Sung Hyuk, Park, Kwang-Yeol, Kim, Jeong-Min, Park, Jong-Ho, Choi, Jay Chol, Park, Man-Seok, Kim, Joon-Tae, Choi, Kang-Ho, Hwang, Yang Ha, Kim, Bum Joon, Chung, Jong-Won, Bang, Oh Young, Kim, Gyeongmoon, Seo, Woo-Keun, Jung, Jin-Man
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523653/
https://www.ncbi.nlm.nih.gov/pubmed/34663874
http://dx.doi.org/10.1038/s41598-021-99920-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523653/
https://www.ncbi.nlm.nih.gov/pubmed/34663874
http://dx.doi.org/10.1038/s41598-021-99920-7

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