Process Mining of Disease Trajectories: A Feasibility Study

Guntur Kusuma, Samantha Sykes, Ciarán McInerney, Owen Johnson

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.

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Paper Citation


in Harvard Style

Kusuma G., Sykes S., McInerney C. and Johnson O. (2020). Process Mining of Disease Trajectories: A Feasibility Study.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 705-712. DOI: 10.5220/0009166607050712


in Bibtex Style

@conference{healthinf20,
author={Guntur Kusuma and Samantha Sykes and Ciarán McInerney and Owen Johnson},
title={Process Mining of Disease Trajectories: A Feasibility Study},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2020},
pages={705-712},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009166607050712},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Process Mining of Disease Trajectories: A Feasibility Study
SN - 978-989-758-398-8
AU - Kusuma G.
AU - Sykes S.
AU - McInerney C.
AU - Johnson O.
PY - 2020
SP - 705
EP - 712
DO - 10.5220/0009166607050712