Elucidating Discrepancy in Explanations of Predictive Models Developed Using EMR
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Huang, Wenjie
Cook, David
Khanna, Sankalp
Bialkowski, Konstanty
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Bichel-Findlay, J
Otero, P
Scott, P
Huesing, E
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Sydney, Australia
Abstract
The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligence (XAI) methods have been proposed, little research has focused on the agreement between these methods and expert clinical knowledge. This study applies current state-of-the-art explainability methods to clinical decision support algorithms developed for Electronic Medical Records (EMR) data to analyse the concordance between these factors and discusses causes for identified discrepancies from a clinical and technical perspective. Important factors for achieving trustworthy XAI solutions for clinical decision support are also discussed.
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MEDINFO 2023 — The Future Is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics
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310
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© 2024 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
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Brankovic, A; Huang, W; Cook, D; Khanna, S; Bialkowski, K, Elucidating Discrepancy in Explanations of Predictive Models Developed Using EMR, MEDINFO 2023 — The Future Is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics, 2024, 310, pp. 865-869