Analysing disease trajectories of multimorbidity through process mining techniques: a case study

Daniel Petrov, Thu Nguyen, Areti Manataki*, Colin McCowan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multimorbidity is a global public health challenge, where an individual has two or more chronic conditions, making it difficult to treat and manage illnesses. Understanding the disease trajectories of multimorbidity is crucial for providing patient-centred care. Previous research has primarily employed regression-based approaches, which don’t consider the specific diseases involved and the order in which they occur. Process mining was recently proposed to address this gap, showing promising results in modelling disease trajectories across the entire spectrum of diseases. However, that study involved admissions to a single hospital, and hence the size of the dataset was much smaller than what is typically used in population-level studies on multimorbidity. In this paper, we present a case study where process mining techniques are applied to a much larger dataset of patients in Scotland. We present the disease trajectories discovered for the entire population, as well as stratified by sex. We also describe temporal patterns of disease trajectories, including trajectories with rapid progression. Finally, we discuss the experience of employing process mining within a trusted research environment, and we reflect on challenges that we faced when mining disease trajectories based on a large and complex dataset. Our main contribution involves providing additional evidence around the feasibility of disease trajectory modelling through process mining techniques, in particular when a much larger health dataset is involved.
Original languageEnglish
Title of host publicationProcess mining workshops
Subtitle of host publicationICPM 2024 international workshops, Lyngby, Denmark, October 14–18, 2024, revised selected papers
EditorsAndrea Delgado, Tijs Slaats
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Pages460-472
Number of pages13
ISBN (Electronic)9783031822254
ISBN (Print)9783031822247
DOIs
Publication statusPublished - 28 Mar 2025
Event6th International Conference on Process Mining - Technical University of Denmark, Lyngby, Denmark
Duration: 14 Oct 202418 Oct 2024
https://icpmconference.org/2024/

Publication series

NameLecture notes in business information processing
PublisherSpringer Nature
Volume533
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Conference

Conference6th International Conference on Process Mining
Abbreviated titleICPM 2024
Country/TerritoryDenmark
CityLyngby
Period14/10/2418/10/24
Internet address

Keywords

  • Multimorbidity
  • Disease trajectories
  • Process mining

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