Alharbi, A, Bulpitt, A orcid.org/0000-0002-7905-4540 and Johnson, OA orcid.org/0000-0003-3998-541X (2018) Towards Unsupervised Detection of Process Models in Healthcare. In: Studies in Health Technology and Informatics. Medical Informatics Europe (MIE2018), 24-26 Apr 2018, Gothenburg, Sweden. IOS Press , pp. 381-385. ISBN 978-1-61499-851-8
Abstract
Process mining techniques can play a significant role in understanding healthcare processes by supporting analysis of patient records in electronic health record systems. Healthcare processes are complex and patterns of care may vary considerably within similar cohorts of patients. Process mining often creates "spaghetti" models and require significant domain expert input to refine. Machine learning approaches such as Hidden Markov Models (HMM) may assist this refinement process. HMMs have been advocated for patient pathways clustering purposes; however these models can also be utilized for detecting hidden processes to help event abstraction. We explore use of an unsupervised method for detecting hidden healthcare sub-processes using HMMs, in particular the Viterbi algorithm. We describe an approach to enrich the event log with HMM-derived states and remodeling the healthcare processes as state transitions using a process mining tool. Our method is applied to event data for 'Altered Mental Status' patients that was extracted from a US hospital database (MIMIC-III). The results are promising and show a successful reduction of model complexity and detection of several hidden processes unsupervised by a domain expert.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | IOS Press Copyright 2018. This is an author produced version of a paper published in Studies in Health Technology and Informatics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | process mining; unsupervised learning; hidden Markov models; electronic health records; event abstraction; MIMIC-III |
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) |
Funding Information: | Funder Grant number NHSA Northern Health Science No Ext Ref |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 May 2018 15:32 |
Last Modified: | 13 Dec 2018 10:30 |
Published Version: | https://www.iospress.nl/book/building-continents-o... |
Status: | Published |
Publisher: | IOS Press |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130320 |