Kusuma, GP orcid.org/0000-0002-0208-125X, Kurniati, AP, Rojas, E et al. (3 more authors) (2021) Process Mining of Disease Trajectories: A Literature Review. In: Mantas, J, Stoicu-Tivadar, L, Chronaki, C, Hasman, A, Weber, P, Gallos, P, Crişan-Vida, M, Zoulias, E and Sorina Chirila, O, (eds.) Studies in Health Technology and Informatics. IOS Press , pp. 457-461. ISBN 978-1-64368-184-9
Abstract
Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 European Federation for Medical Informatics (EFMI) 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). |
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) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Jul 2021 11:43 |
Last Modified: | 22 Jul 2021 11:43 |
Published Version: | https://ebooks.iospress.nl/volume/public-health-an... |
Status: | Published |
Publisher: | IOS Press |
Identification Number: | 10.3233/shti210200 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176357 |