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Development of network of chronic kidney disease related factors and therapeutic target using big data and artificial intelligence and information and communication technology

Research Project

Project/Area Number 19K08740
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 53040:Nephrology-related
Research InstitutionKawasaki Medical School

Principal Investigator

Kanda Eiichiro  川崎医科大学, 医学部, 教授 (40401377)

Co-Investigator(Kenkyū-buntansha) 柏原 直樹  川崎医科大学, 医学部, 教授 (10233701)
Project Period (FY) 2019-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Keywords慢性腎臓病 / 透析 / 機械学習 / 深層学習 / AI / ネットワーク / 自然言語処理 / ビッグデータ / サポートベクターマシーン / アンサンブルモデル / テキストマイニング / ICT
Outline of Research at the Start

慢性腎臓病(CKD)は透析、心血管疾患および死亡の危険因子である。日本では高齢化とともにCKD患者は増加傾向にあるため、CKD進行の病態因子や治療標的の発見が重要な課題である。これまで私共は種々のコホート研究により病態因子を解析し、ICT技術により大規模CKD患者データベースを構築してきた。本研究では、AI・ICTの技術を活用し、インターネット上のテキスト情報および患者ビッグデータの解析によって、CKDに関係する因子のネットワークを構築する。このネットワークにより全く新規の病態因子が抽出され、治療標的が開発される。最新のAI・ICT技術を活用したビックデータ解析法を確立し、医療へ貢献したい。

Outline of Final Research Achievements

Since the number of patients with chronic kidney disease (CKD) is expected to increase in Japan with the aging of society, the discovery of new pathological factors and therapeutic targets for the progression of CKD is important. However, there are limits to the conventional literature search and epidemiological research methods.
Therefore, using AI/ICT technology, we analyzed information on the internet (literature on MEDLINE), clarified the mathematical structure of medical term data, and constructed a medical-term network. On the basis of this medical-term network, we analyzed big data of dialysis patients and developed a machine learning model for the prediction of the prognosis of life. This machine learning model expresses the pathological concept of CKD with a mathematical model.

Academic Significance and Societal Importance of the Research Achievements

本研究では、正確に予後を予測する機械学習モデルを開発した。この機械学習モデルは疾患の病態を数理学的モデルで表現しており、新規の危険因子や治療法の開発につながる可能性がある。例えば、この機械学習モデルの臨床での活用法として以下の流れが考えられる。①予後の悪い患者をスクリーニングする。②スクリーニングされた患者を対象にデータを解析し、予後を予測する。③患者に対して、癌・感染症・低栄養・心血管疾患などの合併症がないか精査し、適切な介入・治療を行う。このシステムの臨床活用により患者予後の改善が見込まれ、腎疾患診療に対して多大な波及効果がもたらされると考えられる。

Report

(4 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • Research Products

    (32 results)

All 2021 2020 2019 Other

All Journal Article (9 results) (of which Int'l Joint Research: 4 results,  Peer Reviewed: 7 results,  Open Access: 7 results) Presentation (21 results) (of which Int'l Joint Research: 11 results,  Invited: 1 results) Remarks (1 results) Patent(Industrial Property Rights) (1 results)

  • [Journal Article] High-performance dialyzers and mortality in maintenance hemodialysis patients2021

    • Author(s)
      Abe M, Masakane I, Wada A, Nakai S, Kanda E, Nitta K, Nakamoto H
    • Journal Title

      Scientific Reports

      Volume: 11 Issue: 1 Pages: 12272-12272

    • DOI

      10.1038/s41598-021-91751-w

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Methods and Nutritional Interventions to Improve the Nutritional Status of Dialysis Patients in JAPAN?A Narrative Review2021

    • Author(s)
      Kanno Y, Kanda E, Kato A
    • Journal Title

      Nutrients

      Volume: 13 Issue: 5 Pages: 1390-1390

    • DOI

      10.3390/nu13051390

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Association between anemia and mortality in hemodialysis patients is modified by the presence of diabetes2021

    • Author(s)
      Maruyama Y, Kanda E, Kikuchi K, Abe M, Masakane I, Yokoo T, Nitta K
    • Journal Title

      Journal of Nephrology

      Volume: 34 Issue: 3 Pages: 781-790

    • DOI

      10.1007/s40620-020-00879-x

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] デジタルヘルスケアの医療活用の取り組み 自然言語処理を用いたガイドライン作成支援システムの開発2021

    • Author(s)
      神田英一郎
    • Journal Title

      腎と透析

      Volume: 90 Pages: 205-211

    • Related Report
      2021 Annual Research Report
  • [Journal Article] AIを活用した慢性腎臓病・透析患者の予後予測2021

    • Author(s)
      神田英一郎
    • Journal Title

      腎臓内科

      Volume: 14 Pages: 451-458

    • Related Report
      2021 Annual Research Report
  • [Journal Article] Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan2020

    • Author(s)
      Kanda E, Epureanu BI, Adachi T, Tsuruta Y, Kikuchi K, Kashihara N, Abe M, Masakane I, Nitta K.
    • Journal Title

      PLOS ONE

      Volume: 15 Issue: 5 Pages: e0233491-e0233491

    • DOI

      10.1371/journal.pone.0233491

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Prevalence of anemia in patients with chronic kidney disease in Japan: A nationwide, cross-sectional cohort study using data from the Japan Chronic Kidney Disease Database (J-CKD-DB)2020

    • Author(s)
      Sofue Tadashi、Nakagawa Naoki、Kanda Eiichiro、Nagasu Hajime、Matsushita Kunihiro、Nangaku Masaomi、Maruyama Shoichi、Wada Takashi、Terada Yoshio、Yamagata Kunihiro、Narita Ichiei、Yanagita Motoko、Sugiyama Hitoshi、et al.
    • Journal Title

      PLOS ONE

      Volume: 15 Issue: 7 Pages: e0236132-e0236132

    • DOI

      10.1371/journal.pone.0236132

    • NAID

      120006888500

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Prevalences of hyperuricemia and electrolyte abnormalities in patients with chronic kidney disease in Japan: A nationwide, cross-sectional cohort study using data from the Japan Chronic Kidney Disease Database (J-CKD-DB)2020

    • Author(s)
      Sofue Tadashi、Nakagawa Naoki、Kanda Eiichiro、Nagasu Hajime、Matsushita Kunihiro、Nangaku Masaomi、Maruyama Shoichi、Wada Takashi、Terada Yoshio、Yamagata Kunihiro、Narita Ichiei、Yanagita Motoko、Sugiyama Hitoshi、et al.
    • Journal Title

      PLOS ONE

      Volume: 15 Issue: 10 Pages: e0240402-e0240402

    • DOI

      10.1371/journal.pone.0240402

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan2020

    • Author(s)
      Nakagawa Naoki、Sofue Tadashi、Kanda Eiichiro、Nagasu Hajime、Matsushita Kunihiro、Nangaku Masaomi、Maruyama Shoichi、Wada Takashi、Terada Yoshio、Yamagata Kunihiro、Narita Ichiei、Yanagita Motoko、Sugiyama Hitoshi、et al.
    • Journal Title

      Scientific Reports

      Volume: 10 Issue: 1 Pages: 7351-7351

    • DOI

      10.1038/s41598-020-64123-z

    • NAID

      120006958093

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access
  • [Presentation] Usefulness of machine-learning-predicted probability as a new risk index for prediction of renal and life prognoses of chronic kidney disease2021

    • Author(s)
      Kanda E, Epureanu BI, Adachi T, Sasaki T, Kashihara N
    • Organizer
      Society for Industrial and Applied Mathematics
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Usefulness of machine-learning-predicted probability as a new risk index for prediction of renal and life prognoses of chronic kidney disease. Kidney Week 2021. American Society of Nephrology.2021

    • Author(s)
      Kanda E, Epureanu BI, Adachi T, Sasaki T, Kashihara N.
    • Organizer
      Kidney Week 2021. American Society of Nephrology.
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] MACHINE LEARNING MODELS TO PREDICT CARDIOVASCULAR AND RENAL OUTCOMES AND MORTALITY IN HYPERKALEMIC PATIENTS.2021

    • Author(s)
      Kanda E, Okami S, Kohsaka S, Ma X, Okada M, Kimura T, Yajima T.
    • Organizer
      Kidney Week 2021. American Society of Nephrology.
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] AI・ICTを活用した透析患者の栄養管理2021

    • Author(s)
      神田英一郎
    • Organizer
      第59回日本人工臓器学会
    • Related Report
      2021 Annual Research Report
  • [Presentation] AI・ICTを活用したCKD患者管理システムの開発2021

    • Author(s)
      神田英一郎
    • Organizer
      第41回医療情報学連合大会
    • Related Report
      2021 Annual Research Report
  • [Presentation] AI・ICTを活用したCKD患者管理システムの開発2021

    • Author(s)
      神田英一郎
    • Organizer
      第1回日本腎・血液浄化AI学会学術集会・総会
    • Related Report
      2021 Annual Research Report
  • [Presentation] CKD進行と生命予後を精緻に予測するAIシステムの開発2021

    • Author(s)
      神田英一郎, 安達泰治, 佐々木環, 柏原直樹
    • Organizer
      第64回日本腎臓学会学術総会
    • Related Report
      2021 Annual Research Report
  • [Presentation] 透析療法における遠隔診療のあり方 AI・ICTを活用した透析患者の栄養管理2021

    • Author(s)
      神田英一郎
    • Organizer
      第66回日本透析医学会学術集会・総会
    • Related Report
      2021 Annual Research Report
  • [Presentation] Explainable artificial intelligence system for hemodialysis patients reveals disease background difference.2020

    • Author(s)
      Kanda E, Epureanu BI, Adachi T, Tsuruta Y, Kikuchi K, Kashihara N, Abe M, Masakane I, Nitta K.
    • Organizer
      American Society of Nephrology Kidney week 2020
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Machine Learning Prediction of ESKD and Death in CKD Patients: Electronic Medical Record-Based Cohort Study.2020

    • Author(s)
      Kanda E, Tokuyama A, Itano S, Nagasu H, Kashihara N.
    • Organizer
      American Society of Nephrology Kidney week 2020
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Machine Learning Models for Risk Prediction of Adverse Events in Hyperkalemic Patients.2020

    • Author(s)
      Kanda E, Kohsaka S, Okami S, Okada M, Ma X, Yajima T.
    • Organizer
      American Society of Nephrology Kidney week 2020
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Explainable artificial intelligence system for hemodialysis patients reveals disease background difference.2020

    • Author(s)
      Kanda E, Epureanu BI, Adachi T, Tsuruta Y, Kikuchi K, Kashihara N, Abe M, Masakane I, Nitta K.
    • Organizer
      Society for Industrial and Applied Mathematics.
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Effect of Zinc Deficiency on CKD Progression and Effect Modification by Hypoalbuminemia.2020

    • Author(s)
      Tokuyama A, Kanda E, Itano S, Kondo M, Wada Y, Kadoya H, Kidokoro K, Nagasu H, Sasaki T, Kashihara N.
    • Organizer
      American Society of Nephrology Kidney week 2020
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] 大規模データベース解析の方向性と展開:CKD対策にどう活かすか 包括的慢性腎臓病データベース(J-CKD-DB)とリアルワールドデータベースの活用2020

    • Author(s)
      神田英一郎
    • Organizer
      第63回日本腎臓学会学術総会
    • Related Report
      2020 Research-status Report
    • Invited
  • [Presentation] AIを用いたCKD患者の予後予測システムの開発と予後パターンの分類2020

    • Author(s)
      神田英一郎, 徳山敦之, 板野精之, 長洲一, 柏原直樹.
    • Organizer
      第63回日本腎臓学会学術総会
    • Related Report
      2020 Research-status Report
  • [Presentation] 保存期CKD患者に対するアルゴリズムを用いた新しい個別療養指導システムの開発2020

    • Author(s)
      坂井敦子, 市川和子, 神田英一郎
    • Organizer
      第63回日本腎臓学会学術総会
    • Related Report
      2020 Research-status Report
  • [Presentation] Explainable Artificial Intelligence System for Hemodialysis Patients Reveals Disease Background Difference2020

    • Author(s)
      Eiichiro Kanda, Bogdan I. Epureanu, Taiji Adachi, Yuki Tsuruta, Kan Kikuchi, Naoki Kashihara, Masanori Abe, Ikuto Masakane, Kosaku Nitta
    • Organizer
      2020 SIAM Conference on the Life Sciences
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] AIを用いたCKD患者の予後予測システムの開発と予後パターンの分類2020

    • Author(s)
      神田英一郎、徳山敦之、板野精之、長洲一、柏原直樹
    • Organizer
      第63回日本腎臓学会学術総会
    • Related Report
      2019 Research-status Report
  • [Presentation] Autonomous total-care system for hemodialysis patients using artificial intelligence: a nationwide dialysis cohort study in Japan2020

    • Author(s)
      Eiichiro Kanda, Yuki Tsuruta, Kan Kikuchi, Naoki Kashihara, Masanori Abe, Ikuto Masakane, Kosaku Nitta
    • Organizer
      第65回日本透析医学会学術集会・総会
    • Related Report
      2019 Research-status Report
  • [Presentation] Development of an Automatic Risk-Prediction System for Hemodialysis Patients Using Artificial Intelligence: A Nationwide Dialysis Cohort Study in Japan2019

    • Author(s)
      Eiichiro Kanda, Yuki Tsuruta, Kan Kikuchi, Naoki Kashihara, Masanori Abe, Ikuto Masakane, Kosaku Nitta
    • Organizer
      American Society of Nephrology Kidney week 2019
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] Development of an Exhaustive-Risk-Prediction System Using Deep Learning and Different Patterns of Diabetic Kidney Disease Progression Based on Patient Characteristics2019

    • Author(s)
      Eiichiro Kanda, Atsuyuki Tokuyama, Seiji Itano, Hajime Nagasu, Bogdan I. Epureanu, Naoki Kashihara
    • Organizer
      American Society of Nephrology Kidney week 2019
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Remarks] 川崎医科大学 特任教員 (学長付・医学部)

    • URL

      https://kwweb-res.kawasaki-m.ac.jp/kwmhp/KgApp?section=13&resId=S004904

    • Related Report
      2020 Research-status Report
  • [Patent(Industrial Property Rights)] 文章検索システム、文章検索方法及び文章検索プログラム2020

    • Inventor(s)
      神田英一郎
    • Industrial Property Rights Holder
      神田英一郎
    • Industrial Property Rights Type
      特許
    • Filing Date
      2020
    • Acquisition Date
      2020
    • Related Report
      2020 Research-status Report

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Published: 2019-04-18   Modified: 2023-01-30  

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