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

Journal: PloS one
Published Date: May 29, 2020

Abstract

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 screening hemodialysis patients at a high risk of one-year death using the nation-wide database of the Japanese Society for Dialysis Therapy.

Authors

  • Eiichiro Kanda
    Medical Science, Kawasaki Medical School, Okayama, Japan. kms.cds.kanda@gmail.com.
  • Bogdan I Epureanu
    College of Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
  • Taiji Adachi
    Institute for Frontier Life and Medical Sciences, Kyoto University, Sakyo, Kyoto, Japan.
  • Yuki Tsuruta
    Tsuruta Itabashi Clinic, Itabashi, Tokyo, Japan.
  • Kan Kikuchi
    Shimoochiai Clinic, Shinjuku, Tokyo, Japan.
  • Naoki Kashihara
    Department of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.
  • Masanori Abe
    The Committee of Renal Data Registry, Japanese Society for Dialysis Therapy, Tokyo, Japan.
  • Ikuto Masakane
    The Committee of Renal Data Registry, Japanese Society for Dialysis Therapy, Tokyo, Japan.
  • Kosaku Nitta
    The Committee of Renal Data Registry, Japanese Society for Dialysis Therapy, Tokyo, Japan.

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