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Machine Learning Approach for Personalized Prediction of Renal Graft Survival

Research Project

Project/Area Number 23K17002
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 62010:Life, health and medical informatics-related
Research InstitutionShonan Kamakura General Hospital, Medical Corporation Tokushukai (Center for Clinical and

Principal Investigator

Salybekov Amankeldi  医療法人徳洲会湘南鎌倉総合病院(臨床研究センター), 湘南先端医学研究所 再生医療開発研究部, 主席研究員 (80850776)

Project Period (FY) 2023-04-01 – 2025-03-31
Project Status Granted (Fiscal Year 2023)
Budget Amount *help
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2024: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2023: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
KeywordsMachine learning / Graft failure prediction / Neural network / Kidney transplantation / Kidney / Transplantation
Outline of Research at the Start

1) Data preprocessing
2) Phenotype data normalization (histology, laboratory, and clinical variables)
3) Missing data imputation and synthetic data generation
4) Model development ( random forest, LGBM, XgBoost, SVM etc. Important features development.
5) Validation of the dataset and deployment of final model

Outline of Annual Research Achievements

We developed algorithms for predicting the survival of living and deceased donor grafts before transplantation. We trained a large dataset (n = 135000 for deceased donors and n = 110000 for living donors) obtained from USA & Japanese cohorts. We split training and testing groups into 70/25% and 5% for external validation. All of the missing values of variables were filled up using MisRanger. Then we used LGBM, Random Forest, and reccurrent neural network algorithms. The top 20 predicted variables are further used for graft failure prediction. AUROC for 5-year graft failure prediction were AUROC-97 for living and AUROC-96 for deseased transplants. We also checked the F-1 score. Currently, we are preparing full paper for publication soon. The results were presented at ASN 2023 and JSN 2023.

Current Status of Research Progress
Current Status of Research Progress

1: Research has progressed more than it was originally planned.

Reason

Currently, we finalizing obtained data for full paper publication.

Strategy for Future Research Activity

We plan to develop software for prediction of graft failure. Currently we are working on it.

Report

(1 results)
  • 2023 Research-status Report
  • Research Products

    (5 results)

All 2023

All Journal Article (2 results) (of which Int'l Joint Research: 2 results,  Open Access: 2 results) Presentation (3 results) (of which Int'l Joint Research: 3 results)

  • [Journal Article] Amankeldi Salybekov, Ainur Yerkos, Aidyn D. Kunikeyev, Zholdas Buribayev, Yasuhiro Mochida, Kunihiro Ishioka, Sumi Hidaka,2023

    • Author(s)
      Predictive Modeling of Graft Failure Risk in Deceased Donor Kidney Transplants: Leveraging Machine Learning for Improved Outcomes and Data-Driven Insights
    • Journal Title

      JASN

      Volume: 34 Pages: 99-99

    • Related Report
      2023 Research-status Report
    • Open Access / Int'l Joint Research
  • [Journal Article] Abstract 16084: Impairment of Cardio-Spleno-Bone Marrow Axis Following Myocardial Infarction in Diabetes Mellitus2023

    • Author(s)
      Salybekov Amankeldi A、Hassanpour Mehdi、Tashov Kanat、Salybekova Ainur、Sheng Yin、Shinozaki Yoshiko、Kobayashi Shuzo、Asahara Takayuki
    • Journal Title

      Circulation

      Volume: 148 Issue: Suppl_1

    • DOI

      10.1161/circ.148.suppl_1.16084

    • Related Report
      2023 Research-status Report
    • Open Access / Int'l Joint Research
  • [Presentation] Predictive Modeling of Graft Failure Risk in Deceased Donor Kidney Transplants: Leveraging Machine Learning for Improved Outcomes and Data-Driven Insights2023

    • Author(s)
      Amankeldi Salybekob, Sumi Hidaka, Shuzo Kobayashi
    • Organizer
      JSN
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] Predictive Modeling of Graft Failure Risk in Deceased Donor Kidney Transplants: Leveraging Machine Learning for Improved Outcomes and Data-Driven Insights2023

    • Author(s)
      Amankeldi Salybekob, Sumi Hidaka, Shuzo Kobayashi
    • Organizer
      American Society for Nephrology
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] Regeneration-Associated Cells-derived Extracellular Vesicles Preserved Kidney Function After Acute Ischemia Injury2023

    • Author(s)
      Amankeldi Salybekov
    • Organizer
      Inflammation and Regeneration
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research

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Published: 2023-04-13   Modified: 2024-12-25  

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