Kurniati, AP orcid.org/0000-0002-4747-1067, Hall, G orcid.org/0000-0002-8864-5932, Hogg, D orcid.org/0000-0002-6125-9564 et al. (1 more author) (2018) Process mining in oncology using the MIMIC-III dataset. In: Journal of Physics : Conference Series. International Conference on Data and Information Science (ICoDIS) 2017, 05-06 Dec 2017, Bandung, Indonesia. IOP Publishing
Abstract
Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There is a growing body of literature on process mining in healthcare, including oncology, the study of cancer. In earlier work we found 37 peer-reviewed papers describing process mining research in oncology with a regular complaint being the limited availability and accessibility of datasets with suitable information for process mining. Publicly available datasets are one option and this paper describes the potential to use MIMIC-III, for process mining in oncology. MIMIC-III is a large open access dataset of de-identified patient records. There are 134 publications listed as using the MIMIC dataset, but none of them have used process mining. The MIMIC-III dataset has 16 event tables which are potentially useful for process mining and this paper demonstrates the opportunities to use MIMIC-III for process mining in oncology. Our research applied the L* lifecycle method to provide a worked example showing how process mining can be used to analyse cancer pathways. The results and data quality limitations are discussed along with opportunities for further work and reflection on the value of MIMIC-III for reproducible process mining research.
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Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. To view a copy of this license, visit https://creativecommons.org/licenses/by/3.0/ |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Nov 2017 11:00 |
Last Modified: | 23 Jun 2023 22:39 |
Status: | Published |
Publisher: | IOP Publishing |
Identification Number: | 10.1088/1742-6596/971/1/012008 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124307 |
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