Kurniati, AP orcid.org/0000-0002-4747-1067, Johnson, OA, Hogg, D et al. (1 more author) (2016) Process Mining in Oncology: a Literature Review. In: Proceedings of the 6th International Conference on Information Communication and Management (ICICM 2016). 2016 6th International Conference on Information Communication and Management (ICICM), 29-31 Oct 2016, Hatfield, UK. IEEE , pp. 291-297. ISBN 978-1-5090-3495-6
Abstract
Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | process mining; data mining; oncology; cancer; clinical pathways |
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: | 20 Jan 2017 14:52 |
Last Modified: | 12 Apr 2017 07:56 |
Published Version: | https://doi.org/10.1109/INFOCOMAN.2016.7784260 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/INFOCOMAN.2016.7784260 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110913 |