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

Mohammad R. Rezaei; Reza Saadati Fard; Ebrahim Pourjafari; Navid Ziaei; Amir Sameizadeh; Mohammad Shafiee; Mohammad Alavinia; Mansour Abolghasemian; Nick Sajadi

arXiv · 2210.15674

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

Mohammad R. Rezaei·Reza Saadati Fard·Ebrahim Pourjafari·Navid Ziaei·Amir Sameizadeh·Mohammad Shafiee·Mohammad Alavinia·Mansour Abolghasemian·Nick Sajadi

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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.

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Mohammad R. Rezaei, Reza Saadati Fard, Ebrahim Pourjafari, Navid Ziaei, Amir Sameizadeh, Mohammad Shafiee, Mohammad Alavinia, Mansour Abolghasemian, Nick Sajadi. 2022-10-27. Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models. https://arxiv.org/abs/2210.15674

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