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.
|Title of host publication||2022 4th International Conference on Process Mining (ICPM)|
|Editors||Andrea Burattin, Artem Polyvyanyy, Barbara Weber|
|Number of pages||8|
|Publication status||Published - 14 Dec 2022|
|Event||4th International Conference on Process Mining (ICPM) - Free University of Bozen-Bolzano, Bolzano, Italy|
Duration: 23 Oct 2022 → 28 Oct 2022
|Name||Proceedings - 2022 4th International Conference on Process Mining, ICPM 2022|
|Conference||4th International Conference on Process Mining (ICPM)|
|Period||23/10/22 → 28/10/22|
Bibliographical noteFunding 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.
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- process mining
- statistical relations
- confounding variables