Explainable deep learning in healthcare: A methodological survey from an attribution view
Name
Explainable_Deep_Learning_in_Healthcare_A_Methodol.pdf
Description
Accepted version
Size
615.83 KB
Format
Adobe PDF
Checksum (MD5)
a8c0acd0d3bcec04ece2d5ae32801384
Author(s)
Jin, Di
Sergeeva, Elena
Weng, Wei-Hung
Chauhan, Geeticka
Szolovits, Peter
Date Issued
2022
Journal
WIREs Mechanisms of Disease
Publisher
Wiley
Citation
Jin, Di, Sergeeva, Elena, Weng, Wei-Hung, Chauhan, Geeticka and Szolovits, Peter. 2022. "Explainable deep learning in healthcare: A methodological survey from an attribution view." WIREs Mechanisms of Disease, 14 (3).
Version
Author's final manuscript
Abstract
The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken. In this review, we focus on the interpretability of the DL models in healthcare. We start by introducing the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners in this field. Besides the methods' details, we also include a discussion of advantages and disadvantages of these methods and which scenarios each of them is suitable for, so that interested readers can know how to compare and choose among them for use. Moreover, we discuss how these methods, originally developed for solving general-domain problems, have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies. Overall, we hope this survey can help researchers and practitioners in both artificial intelligence and clinical fields understand what methods we have for enhancing the interpretability of their DL models and choose the optimal one accordingly. This article is categorized under: Cancer > Computational Models.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/WSBM.1548
