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Discovering interpretable medical process models: A case study in trauma resuscitation
Journal article   Open access   Peer reviewed

Discovering interpretable medical process models: A case study in trauma resuscitation

Keyi Li, Ivan Marsic, Aleksandra Sarcevic, Sen Yang, Travis M. Sullivan, Peyton E. Tempel, Zachary P. Milestone, Karen J. O'Connell and Randall S. Burd
Journal of biomedical informatics, v 140, 104344
Apr 2023
DOI: https://doi.org/10.1016/j.jbi.2023.104344
PMID: 36940896
url
https://doi.org/10.1016/j.jbi.2023.104344View
Published, Version of Record (VoR)Open Access (Publisher-Specific),  Open

Abstract

Consensus sequence Knowledge discovery Process mining Resuscitation
• Introduced TAD Miner, a data-driven process model discovery approach for complex medical processes based on trace alignment. • TAD models feature a backbone process, concurrent activities, and uncommon-but-critical activities. Compared to the models discovered by the state-of-the-art methods, TAD models are more interpretable and achieve comparable accuracy. • Discovered process models using 308 real trauma resuscitation cases for five resuscitation goals. The models enhanced the understanding of complex medical processes. • Identified the errors and the best positions for the tentative steps in knowledge-driven process models. Understanding the actual work (i.e., “work-as-done”) rather than theorized work (i.e., “work-as-imagined”) during complex medical processes is critical for developing approaches that improve patient outcomes. Although process mining has been used to discover process models from medical activity logs, it often omits critical steps or produces cluttered and unreadable models. In this paper, we introduce a TraceAlignment-based ProcessDiscovery method called TAD Miner to build interpretable process models for complex medical processes. TAD Miner creates simple linear process models using a threshold metric that optimizes the consensus sequence to represent the backbone process, and then identifies both concurrent activities and uncommon-but-critical activities to represent the side branches. TAD Miner also identifies the locations of repeated activities, an essential feature for representing medical treatment steps. We conducted a study using activity logs of 308 pediatric trauma resuscitations to develop and evaluate TAD Miner. TAD Miner was used to discover process models for five resuscitation goals, including establishing intravenous (IV) access, administering non-invasive oxygenation, performing back assessment, administering blood transfusion, and performing intubation. We quantitively evaluated the process models with several complexity and accuracy metrics, and performed qualitative evaluation with four medical experts to assess the accuracy and interpretability of the discovered models. Through these evaluations, we compared the performance of our method to that of two state-of-the-art process discovery algorithms: Inductive Miner and Split Miner. The process models discovered by TAD Miner had lower complexity and better interpretability than the state-of-the-art methods, and the fitness and precision of the models were comparable. We used the TAD process models to identify (1) the errors and (2)the best locations for the tentative steps in knowledge-driven expert models. The knowledge-driven models were revised based on the modifications suggested by the discovered models. The improved modeling using TAD Miner may enhance understanding of complex medical processes.

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2 citations in Web of Science
2 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Interdisciplinary Applications
Medical Informatics
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