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

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 screening hemodi...

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Veröffentlicht in:	PloS one Jg. 15; H. 5; S. e0233491
Hauptverfasser:	Kanda, Eiichiro, Epureanu, Bogdan I., Adachi, Taiji, Tsuruta, Yuki, Kikuchi, Kan, Kashihara, Naoki, Abe, Masanori, Masakane, Ikuto, Nitta, Kosaku
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 29.05.2020 Public Library of Science (PLoS)
Schlagworte:	Age Algorithms Artificial intelligence Body mass index Cluster analysis Clustering Computer and Information Sciences Creatinine Datasets Death Deep learning Diabetes Diabetes mellitus Dialysis Geriatrics Glomerulonephritis Health hazards Hemodialysis Hemodialysis patients Hypertension Japan Laboratories Learning algorithms Machine learning Medical prognosis Medicine and Health Sciences Methods Model accuracy Mortality Nephrology Older people Patient outcomes Patients Peritoneal dialysis Prognosis Regression models Risk analysis Risk assessment Risk factors Statistical models Support vector machines Vector quantization Japan United States > US
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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