Kusuma, G orcid.org/0000-0002-0208-125X, Sykes, S, McInerney, C orcid.org/0000-0001-7620-7110 et al. (1 more author) (2020) Process Mining of Disease Trajectories: A Feasibility Study. In: Cabitza, F, Fred, A and Gamboa, H, (eds.) Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (Volume 5). 13th International Joint Conference on Biomedical Engineering Systems and Technologies, 24-26 Feb 2020, Valletta, Malta. Science and Technology Publications , pp. 705-712. ISBN 978-989-758-398-8
Abstract
Modelling patient disease trajectories from evidence in electronic health records could help clinicians and medical researchers develop a better understanding of the progression of diseases within target populations. Process mining provides a set of well-established tools and techniques that have been used to mine electronic health record data to understand healthcare care pathways. In this paper we explore the feasibility for using a process mining methodology and toolset to automate the identification of disease trajectory models. We created synthetic electronic health record data based on a published disease trajectory model and developed a series of event log transformations to reproduce the disease trajectory model using standard process mining tools. Our approach will make it easier to produce disease trajectory models from routine health data.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. This article is under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/. |
Keywords: | Disease Trajectories, Process Mining, Electronic Health Records |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Mar 2020 14:42 |
Last Modified: | 18 Apr 2020 04:52 |
Status: | Published |
Publisher: | Science and Technology Publications |
Identification Number: | 10.5220/0009166607050712 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158247 |