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Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data

Watson, M; Awwad Shekh Hasan, B; Al Moubayed, N

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Matthew Watson matthew.s.watson@durham.ac.uk
Postdoctoral Research Associate



Abstract

It has been shown that identical Deep Learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and finance, where transparency and explainability is paramount, this can be a significant barrier to DL adoption. In this study we present a further analysis of explanation (in)consistency on 6 tabular datasets/tasks, with a focus on Electronic Health Records data. We propose a novel deep learning ensemble architecture that trains its sub-models to produce consistent explanations, improving explanation consistency by as much as 315% (e.g. from 0.02433 to 0.1011 on MIMIC-IV), and on average by 124% (e.g. from 0.12282 to 0.4450 on the BCW dataset). We evaluate the effectiveness of our proposed technique and discuss the implications our results have for both industrial applications of DL and explainability as well as future methodological work.

Citation

Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6

Journal Article Type Article
Acceptance Date Nov 14, 2022
Online Publication Date Nov 18, 2022
Publication Date 2022
Deposit Date Nov 17, 2022
Publicly Available Date Feb 8, 2023
Journal Scientific Reports
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 12
Issue 19899
Article Number 19899
DOI https://doi.org/10.1038/s41598-022-24356-6
Public URL https://durham-repository.worktribe.com/output/1185319

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http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.





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