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Combining the clinical and operational perspectives in heterogeneous treatment effect inference in healthcare processes

Book Contribution - Book Chapter Conference Contribution

Recent developments in causal machine learning open perspectives for new approaches that support decision-making in healthcare processes using causal models. In particular, Heterogeneous Treatment Effect (HTE) inference enables the estimation of causal treatment effects for individual cases, offering great potential in a process mining context. At the same time, HTE literature typically focuses on clinical outcome measures, disregarding process efficiency. This paper shows the potential of jointly considering the clinical and operational effects of treatments in the context of healthcare processes. Moreover, we present a simple pipeline that makes existing HTE machine learning techniques directly applicable to event logs. Besides these conceptual contributions, a proof-of-concept application starting from the publicly available sepsis event log is outlined, forming the basis for a critical reflection regarding HTE estimation in a process mining context.
Book: PROCESS MINING WORKSHOPS, ICPM 2021
Series: Lecture Notes in Business Information Processing
Volume: 433
Pages: 327 - 339
ISBN:9783030985806
Publication year:2022
Keywords:Heterogeneous Treatment Effect, Process Mining, Machine Learning, Event Log
Accessibility:Open