Machine learning using serial changes in proteinuria during initial steroid therapy to predict treatment response and immunosuppressant use in pediatric idiopathic nephrotic syndrome.

Abstract

Authors

• Takaya Iida
  Centre for Critical Illness Research, Lawson Health Research Institute, London, ON N6A 5W9, Canada.
• Kenichiro Miura
  Department of Pediatric Nephrology, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.
• Takayuki Okamoto
  Department of Pediatrics, Hokkaido University Hospital, Sapporo, Japan.
• Shuichiro Fujinaga
  Division of Nephrology, Saitama Children's Medical Center, Saitama, Japan.
• Yuko Akioka
  Department of Pediatrics, Saitama Medical University, Saitama, Japan.
• Yasuhiro Takeshima
  Department of Pediatrics, Hyogo Medical University, Nishinomiya, Hyogo, Japan.
• Maki Urushihara
  Department of Pediatrics, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan.
• Masataka Hisano
  Department of Nephrology, Chiba Children's Hospital, Chiba, Japan.
• Yoshimitsu Gotoh
  Department of Pediatric Nephrology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya, Japan.
• Toshiyuki Ohta
  Department of Pediatric Nephrology, Hiroshima Prefectural Hospital, Hiroshima, Japan.
• Eichi Takaya
  Graduate School of Science and Technology, Keio University.
• Carlos Makoto Miyauchi
  AI Lab, Tohoku University Hospital, Sendai, Japan.
• Shinya Sonobe
  AI Lab, Tohoku University Hospital, Sendai, Japan.
• Tadashi Kaname
  Department of Genome Medicine, National Center for Child Health and Development, Tokyo, Japan.
• Motoshi Hattori
  Department of Pediatric Nephrology, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. hattori@twmu.ac.jp.

Keywords

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