Deep Learning for Patient-Specific Kidney Graft Survival Analysis

TL;DR

This paper introduces a deep learning model that directly predicts patient-specific kidney graft survival functions, outperforming traditional methods like Cox models in accuracy and concordance.

Contribution

It presents a novel deep learning approach that models survival functions directly using multi-task learning, improving prediction quality over existing survival analysis methods.

Findings

Outperforms Cox Proportional Hazards model in survival prediction

Achieves higher concordance index than traditional methods

Demonstrates improved accuracy in patient-specific survival estimation

Abstract

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.