Abstract
Process mining is an emerging study area adopting a data-driven approach and classical model-based process analysis. Process mining techniques are applicable in different domains and may represent standalone tools or integrated solutions within other fields. In this paper, we propose an approach based on a meta-states concept to extract additional features from discovered process models for predictive modelling. We show how a simple assumption about cyclic process behaviours can not only help to structure and interpret the process model but to be used in machine learning tasks. We demonstrate the proposed approach for hypertension control status prognosis within a remote monitoring program. The results are potential for medical diagnosis and model interpretation.
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Acknowledgements
This research is financially supported by the Russian Science Foundation, Agreement #17–15-01177. Participation in the ICCS conference is supported by the NWO Science Diplomacy Fund project #483.20.038 “Russian-Dutch Collaboration in Computational Science”. The authors also wish to thank the colleagues from PMT Online for the data provided and valuable cooperation.
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Elkhovskaya, L., Kovalchuk, S. (2021). Feature Engineering with Process Mining Technique for Patient State Predictions. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_48
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