On the Comparison of Markov Chains-based Models in Process Mining for Healthcare: A Case Study

Mauro Vallati, Stefania Orini, Mariagrazia Lorusso, Mariachiara Savino, Roberto Gatta, Massimiliano Filosto

Research output: Contribution to journalConference articlepeer-review

Abstract

In the last decade, Process Mining has become a significant field to help healthcare process experts understand and gain relevant insights about the processes they execute. One of the most challenging questions in Process Mining, and particularly in healthcare, typically is: how good are the discovered models? Previous studies have suggested approaches for comparing the (few) available discovery algorithms and measure their quality. However, a general and clear comparison framework is missing, and none of the analyzed algorithms exploits Markov Chains-based Models. In this paper, we propose and discuss effective ways for assessing both quality and performance of discovered models. This is done by focusing on a case study, where the pMiner tool is used for generating Markov Chainsbased models, on a large set of real Clinical Guidelines and workflows.
Original languageEnglish
Number of pages7
JournalProceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS
VolumeFLAIRS-36
DOIs
Publication statusPublished - 8 May 2023
Event36th International FLAIRS Conference: The Florida Artificial Intelligence Research Society Conference - Sheraton Sandy Key Resort, Clearwater Beach, United States
Duration: 14 May 202317 May 2023
Conference number: 36
https://www.flairs-36.info/

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