# Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs

arXiv: https://arxiv.org/abs/2103.03305 · HTML: https://mlsanity.com/paper/2103.03305/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Deep Learning for Patient-Specific Kidney Graft Survival Analysis](https://mlsanity.com/paper/1705.10245/) (2017)

## Built on by (most cited first, 3 of 3)

- [A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation](https://mlsanity.com/paper/2211.02234/) (2022)
- [LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models](https://mlsanity.com/paper/2506.15492/) (2025)
- [Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants](https://mlsanity.com/paper/2608.03017/) (2026)

## Abstract (arXiv)

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.