Discovering Probabilistic Structures of Healthcare Processes

Medical protocols and guidelines can be looked upon as concurrent programs, where the patient's state dynamically changes over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into the correctness of guidelines and protocols by adopting a logical point of view. However, there is uncertainty involved both in the management of the disease and the way the disease will develop, and, therefore, a probabilistic view on medical protocols seems more appropriate. Representations using Bayesian networks capture that uncertainty, but usually concern a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learning to represent and identify patient groups for obtaining insight into the care that patients have received. We evaluate this approach using data obtained from general practitioners and identify significant differences in patients who were diagnosed with a transient ischemic attack. Finally, we discuss the implications of such a computational method for the analysis of medical protocols and guidelines.