A context-aware miner for medical processes

Medical process mining is gaining much attention in recent years, but the available mining algorithms can hardly cope with medical application peculiarities, that require to properly contextualize process patterns. Indeed, most approaches loose the connection between a mined pattern and the relevant portion of the input event log, and can have a limited precision, i.e., they can mine incorrect paths, never appearing in the input log traces. These issues can be very harmful in medical applications, where it is vital that mining results are reliable as much as possible, and properly reference the contextual information, in order to facilitate the work of physicians and hospital managers in guaranteeing the highest quality of service to patients. In this paper we propose a novel approach to medical process mining that operates in a context-aware fashion. We show on a set of critical examples how our algorithm is able to cope with the issues sketched above. The process model we mine can then be adopted to support efficient and flexible trace retrieval, as described in [1]. Indeed, the model can be used as an indexing structure, well suited to quickly retrieve traces corresponding to the pattern being looked for. In the future, we plan to test the approach on a real-world medical dataset, taken from the stroke patient management domain.