ArticleArtificial intelligence in medicine2023
Predicting kidney transplant survival using multiple feature representations for HLAs.
Article in Artificial intelligence in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants.Proceedings of machine learning research · 2026Article
- Immunosuppression Efficacy via Nonlinear Modeling and Probabilistic Analysis of Human Leukocyte Antigen Match and Dose Effects on Allograft Rejection Risk.Immunity, inflammation and disease · 2025Article
- LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models.Transactions on machine learning research · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose 4 new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.
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