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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Rezaei, Mohammad R, Reza Saadati Fard, Pourjafari, Ebrahim, Ziaei, Navid, Sameizadeh, Amir, Shafiee, Mohammad, Alavinia, Mohammad, Mansour Abolghasemian, Sajadi, Nick
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 27.10.2022
Subjects
Artificial neural networks
Decision making
Health care
Reliability analysis
Survival
Survival analysis
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Summary: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.
ISSN:2331-8422