Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/38938
Title: Mining statistical relations for better decision making in healthcare processes
Authors: Koorn, Jelmer J.
Lu, Xixi
Leopold, Henrik
MARTIN, Niels 
Verboven, Sam
Reijers, Hajo A.
Issue Date: 2022
Source: Proceedings of the 2022 International Conference on Process Mining,
Abstract: 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.
Keywords: process mining;statistical relations;confounding variables;healthcare
Document URI: http://hdl.handle.net/1942/38938
ISBN: 9798350397147
DOI: 10.1109/ICPM57379.2022.9980719
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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