2023 Fiscal Year Research-status Report
Machine Learning Approach for Personalized Prediction of Renal Graft Survival
| Project/Area Number |
23K17002
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| Research Institution | Shonan Kamakura General Hospital, Medical Corporation Tokushukai (Center for Clinical and |
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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)
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| Project Period (FY) |
2023-04-01 – 2025-03-31
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| Keywords | Machine learning / Graft failure prediction / Neural network / Kidney transplantation |
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| 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.
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| 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.
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| Strategy for Future Research Activity |
We plan to develop software for prediction of graft failure. Currently we are working on it.
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| Causes of Carryover |
We planned to perform transcriptomics analysis; however, due to a lack of time, we could not realize it last year. This year, we have already started transcriptomics analysis, which will be completed within 4-5 months.
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