# Personalized Survival Prediction with Contextual Explanation Networks

arXiv: https://arxiv.org/abs/1801.09810 · HTML: https://mlsanity.com/paper/1801.09810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Contextualized Machine Learning](https://mlsanity.com/paper/2310.11340/) (2023)

## Abstract (arXiv)

Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices. In this paper, we design a model that concurrently learns to accurately predict patient-specific survival distributions and to explain its predictions in terms of patient attributes such as clinical tests or assessments. Our model is flexible and based on a recurrent network, can handle various modalities of data including temporal measurements, and yet constructs and uses simple explanations in the form of patient- and time-specific linear regression. For analysis, we use two publicly available datasets and show that our networks outperform a number of baselines in prediction while providing a way to inspect the reasons behind each prediction.