Gatta, Roberto, Vallati, Mauro, Lenkowicz, Jacopo, Rojas, Eric, Damiani, Andrea, Sacchi, Lucia, De Bari, Berardino, Dagliati, Arianna, Fernandez-Llatas, Carlos, Montesi, Matteo, Marchetti, Antonio, Castellano, Maurizio and Valentini, Vincenzo (2017) Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR. In: Proceedings of the 2017 Conference on Knowledge Capture (K-CAP). ACM. ISBN 9781450355537

Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated.
In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload
of the available resources in healthcare

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