Journal article
xPM: Enhancing exogenous data visibility
Artificial intelligence in medicine, Vol.133, pp.1-15
11/2022
Appears in Recent Faculty of Health Publications
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Abstract
Process mining is a well-established discipline with applications in many industry sectors, including healthcare. To date, few publications have considered the context in which processes execute. Little consideration has been given as to how contextual data (exogenous data) can be practically included for process mining analysis, beyond including case or event attributes in a typical event log. We show that the combination of process data (endogenous) and exogenous data can generate insights not possible with standard process mining techniques. Our contributions are a framework for process mining with exogenous data and new analyses, where exogenous data and process behaviour are linked to process outcomes. Our new analyses visualise exogenous data, highlighting the trends and variations, to show where overlaps or distinctions exist between outcomes. We applied our analyses in a healthcare setting and show that clinicians could extract insights about differences in patients’ vital signs (exogenous data) relevant to clinical outcomes. We present two evaluations, using a publicly available data set, MIMIC-III, to demonstrate the applicability of our analysis. These evaluations show that process mining can integrate large amounts of physiologic data and interventions, with resulting discrimination and conversion to clinically interpretable information.
Details
- Title
- xPM: Enhancing exogenous data visibility
- Creators
- Adam Banham - Queensland University of TechnologySander J.J. Leemans - RWTH, Aachen, GermanyMoe T. Wynn - Queensland University of TechnologyRobert Andrews - Queensland University of TechnologyKevin B. Laupland - Queensland University of TechnologyLucy Shinners - Southern Cross University
- Publication Details
- Artificial intelligence in medicine, Vol.133, pp.1-15
- Publisher
- Elsevier B.V
- Grant note
- Adam Banham’s work on this project was jointly funded through an Australian Government Research Training Program Scholarship and a Queensland University of Technology, Centre for Data Science Scholarship.
- Identifiers
- 991013056612702368
- Copyright
- © 2022 Elsevier B.V. All rights reserved.
- Academic Unit
- Faculty of Health; Nursing
- Language
- English
- Resource Type
- Journal article