| 研究課題/領域番号 |
23K17002
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分62010:生命、健康および医療情報学関連
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| 研究機関 | 医療法人徳洲会湘南鎌倉総合病院(臨床研究センター) |
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研究代表者 |
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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| 研究期間 (年度) |
2023-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2024年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
2023年度: 2,730千円 (直接経費: 2,100千円、間接経費: 630千円)
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| キーワード | Machine learning / Graft failure prediction / Neural network / Kidney transplantation / Kidney / Transplantation |
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| 研究開始時の研究の概要 |
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
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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
Currently, we finalizing obtained data for full paper publication.
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| 今後の研究の推進方策 |
We plan to develop software for prediction of graft failure. Currently we are working on it.
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