Mining Statistical Relations for Better Decision Making in Healthcare Processes

Jelmer Jan Koorn, Xixi Lu, Henrik Leopold, Niels Martin, Sam Verboven, Hajo A. Reijers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    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 publication4th International Conference on Process Mining, ICPM 2022, Bolzano, Italy, October 23-28, 2022
    EditorsAndrea Burattin, Artem Polyvyanyy, Barbara Weber
    Number of pages8
    Publication statusPublished - 2022


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