Abstract

Abstract. Medical protocols and guidelines can be looked upon as con-current programs, where the patients dynamically change over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into their correctness 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 appropri-ate. On the other hand, representations using Bayesian networks usually involve a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learn-ing 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 (TIA). Finally, we discuss the implications of such a computational method for the analysis of medical protocols. Topics covered: Clinical guidelines; temporal knowledge representa-tions; knowledge extraction from healthcare databases