Abstract
In this paper, we present a novel process mining approach, specifically tailored to medical applications, which allows the user to build an initial process model from the hospital event log, and then supports further model refinements, by directly exploiting her knowledge-based model evaluation. In such a way, it supports the interactive construction of the process model at multiple and user-defined levels of abstraction, ranging from a model which perfectly adheres to the input traces (i.e., all of its paths correspond to at least one trace in the log) to models which increasingly loose precision, but gain generality. Our results in the field of stroke management, reported as a case study in this paper, show that our approach can provide relevant advantages with respect to traditional process mining techniques.
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Canensi, L., Leonardi, G., Montani, S., Terenziani, P. (2017). Multi-level Interactive Medical Process Mining. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_28
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DOI: https://doi.org/10.1007/978-3-319-59758-4_28
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