# Deep Learning for Patient-Specific Kidney Graft Survival Analysis

arXiv: https://arxiv.org/abs/1705.10245 · HTML: https://mlsanity.com/paper/1705.10245/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2017.

## Built on by (most cited first, 18 of 18)

- [SurvTRACE: Transformers for Survival Analysis with Competing Events](https://mlsanity.com/paper/2110.00855/) (2021)
- [SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2110.14001/) (2021)
- [DySurv: dynamic deep learning model for survival analysis with conditional variational inference](https://mlsanity.com/paper/2310.18681/) (2023)
- [BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values](https://mlsanity.com/paper/2101.03170/) (2021)
- [Evidential time-to-event prediction with calibrated uncertainty quantification](https://mlsanity.com/paper/2411.07853/) (2024)
- [A Federated Cox Model with Non-Proportional Hazards](https://mlsanity.com/paper/2207.05050/) (2022)
- [SAVAE: Leveraging the variational Bayes autoencoder for survival analysis](https://mlsanity.com/paper/2312.14651/) (2023)
- [Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs](https://mlsanity.com/paper/2103.03305/) (2021)
- [Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis](https://mlsanity.com/paper/2409.06209/) (2024)
- [Variable selection for nonlinear Cox regression model via deep learning](https://mlsanity.com/paper/2211.09287/) (2022)
- [Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation](https://mlsanity.com/paper/2110.11312/) (2021)
- [A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation](https://mlsanity.com/paper/2211.02234/) (2022)
- [SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms](https://mlsanity.com/paper/2412.07638/) (2024)
- [Dynamic Survival Prediction using Longitudinal Images based on Transformer](https://mlsanity.com/paper/2508.09328/) (2025)
- [Survival-oriented embeddings for improving accessibility to complex data structures](https://mlsanity.com/paper/2110.11303/) (2021)
- [OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis](https://mlsanity.com/paper/2402.01955/) (2024)
- [Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions](https://mlsanity.com/paper/2506.07933/) (2025)
- [Random Hazard Forests](https://mlsanity.com/paper/2608.21597/) (2026)

## Abstract (arXiv)

An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to jointly predict the time of the event, and its rank in the cox partial log likelihood framework, our deep learning approach outperforms, in terms of survival time prediction quality and concordance index, other common methods for survival analysis, including the Cox Proportional Hazards model and a network trained on the cox partial log-likelihood.