Mining Statistical Relations for Better Decision Making in Healthcare Processes

Jelger J. Koorn, Xixi Lu, Henrik Leopold, Niels Martin, Sam Verboven, Hajo A. Reijers

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


An important part of healthcare decision making is to understand how certain actions relate to desired and undesired outcomes. One key challenge is to deal with confounding variables, i.e., variables that influence the relation between actions and outcomes. Existing techniques aim to uncover the underlying statistical relations between actions and outcomes, but either do not account for confounding variables or only consider the process or case level instead of the event level. Therefore, this paper proposes a novel relation mining approach for healthcare processes that 1) explicitly accounts for confounding variables at the event level, and 2) transparently communicates the effect of the confounding variables to the user. We demonstrate the applicability and importance of our approach using two evaluation experiments. We use a real-world healthcare dataset to show that the identified relations indeed provide important input for decision making in healthcare processes. We use a synthetic dataset to illustrate the importance of our approach in the general setting of causal model estimation.
Original languageEnglish
Title of host publication2022 4th International Conference on Process Mining (ICPM)
EditorsAndrea Burattin, Artem Polyvyanyy, Barbara Weber
Number of pages8
ISBN (Electronic)9798350397147
Publication statusPublished - 14 Dec 2022
Event4th International Conference on Process Mining (ICPM) - Free University of Bozen-Bolzano, Bolzano, Italy
Duration: 23 Oct 202228 Oct 2022

Publication series

NameProceedings - 2022 4th International Conference on Process Mining, ICPM 2022


Conference4th International Conference on Process Mining (ICPM)
Internet address

Bibliographical note

Funding Information:
Acknowledgment. This research was supported by the NWO TACTICS project (628.011.004) and Lunet Zorg in the Netherlands. We would also like to thank the experts from the Lunet Zorg for their valuable assistance and feedback.

Publisher Copyright:
© 2022 IEEE.

Copyright 2023 Elsevier B.V., All rights reserved.


  • process mining
  • statistical relations
  • confounding variables
  • healthcare


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