# Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models

arXiv: https://arxiv.org/abs/2210.15674 · HTML: https://mlsanity.com/paper/2210.15674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 76 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Variational Dropout Sparsifies Deep Neural Networks](https://mlsanity.com/paper/1701.05369/) (2017)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Dropout Feature Ranking for Deep Learning Models](https://mlsanity.com/paper/1712.08645/) (2017)
- [Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare](https://mlsanity.com/paper/1806.01551/) (2018)
- [Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models](https://mlsanity.com/paper/2004.14480/) (2020)
- [Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture](https://mlsanity.com/paper/2204.04542/) (2022)

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

- [Latent Dynamical Implicit Diffusion Processes](https://mlsanity.com/paper/2306.07077/) (2023)

## Abstract (arXiv)

The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in survival analysis applications. Ensuring the reliability and explainability of deep survival models deployed in healthcare is a necessity. Since DNN models often behave like a black box, their predictions might not be easily trusted by clinicians, especially when predictions are contrary to a physician's opinion. A deep survival model that explains and justifies its decision-making process could potentially gain the trust of clinicians. In this research, we propose the reverse survival model (RSM) framework that provides detailed insights into the decision-making process of survival models. For each patient of interest, RSM can extract similar patients from a dataset and rank them based on the most relevant features that deep survival models rely on for their predictions.