Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan

BACKGROUND: Although dialysis patients are at a high risk of death, it is difficult for medical practitioners to simultaneously evaluate many inter-related risk factors. In this study, we evaluated the characteristics of hemodialysis patients using machine learning model, and its usefulness for scre...

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Detalles Bibliográficos
Autores principales: Kanda, Eiichiro, Epureanu, Bogdan I., Adachi, Taiji, Tsuruta, Yuki, Kikuchi, Kan, Kashihara, Naoki, Abe, Masanori, Masakane, Ikuto, Nitta, Kosaku
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7259704/
https://www.ncbi.nlm.nih.gov/pubmed/32469924
http://dx.doi.org/10.1371/journal.pone.0233491

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7259704/
https://www.ncbi.nlm.nih.gov/pubmed/32469924
http://dx.doi.org/10.1371/journal.pone.0233491

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