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An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems: Proceedings, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

A variety of deep learning architectures have been developed for the goal of predictive modelling in regards to detecting health diagnoses in medical records. Several models have placed strong emphases on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a novel Electronic Patient Record (EPR) data set consisting of both diagnoses and medication data for the purpose of Adverse Drug Event (ADE) prediction. As such, a main contribution of this work is an empirical evaluation of two state-of-the-art deep learning architectures in terms of objective performance metrics for ADE prediction. We also assess the importance of attention mechanisms in regards to their usefulness for medical code-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.

Place, publisher, year, edition, pages
IEEE, 2019.
Series
Proceedings IEEE International Symposium on Computer-Based Medical Systems, ISSN 2372-918X, E-ISSN 2372-9198
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177135DOI: 10.1109/CBMS.2019.00075ISBN: 978-1-7281-2287-8 (print)ISBN: 978-1-7281-2286-1 (electronic)OAI: oai:DiVA.org:su-177135DiVA, id: diva2:1379855
Conference
2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 5-7 June, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-22Bibliographically approved

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Rebane, JonathanKarlsson, IsakPapapetrou, Panagiotis
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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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Output format
  • html
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