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

TL;DR

This paper introduces the Reverse Survival Model (RSM), a framework that explains deep survival model predictions by identifying and ranking relevant features and similar patients, enhancing interpretability in healthcare.

Contribution

The paper presents the RSM framework, a novel method for interpreting deep survival models by extracting similar patients and relevant features to justify predictions.

Findings

RSM effectively identifies relevant features influencing survival predictions.

RSM can retrieve similar patients to explain individual predictions.

The approach improves trustworthiness of deep survival models in clinical settings.

Abstract

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.