ABSTRACT
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.
- W Aalst and et al. 2011. Process Mining Manifesto. In Business Process Management Workshops. 169--194.Google Scholar
- Italian Association of Medical Oncology AIOM. 2016. Lung Cancer treatment guidelines. (2016). http://www.aiom.it/Google Scholar
- C M Bishop. 2006. Mixture Models and EM. In Pattern Recognition and Machine Learning (9th ed.). Springer, 450--455.Google Scholar
- Joos C. A. M. Buijs, Boudewijn F. van Dongen, and Wil M. P. van der Aalst. 2012. On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery. In Proceedings of the Conference On the Move to Meaningful Internet Systems: OTM. 305--322.Google Scholar
- A. Dagliati, L. Sacchi, A. Zambelli, V. Tibollo, L. Pavesi, J.H. Holmes, and R. Bellazzi. 2017. Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics 66 (2017), 136--147. Google ScholarDigital Library
- Carlos Fernandez-Llatas, Aroa Lizondo, Eduardo Monton, Jose-Miguel Benedi, and Vicente Traver. 2015. Process mining methodology for health process tracking using real-time indoor location systems. Sensors 15, 12 (2015), 29821--29840.Google ScholarCross Ref
- Roberto Gatta, Jacopo Lenkowicz, Mauro Vallati, Eric Rojas, Andrea Damiani, Lucia Sacchi, Berardino De Bari, Arianna Dagliati, Carlos Fernandez-Llatas, Matteo Montesi, Antonio Marchetti, Maurizio Castellano, and Vincenzo Valentini. 2017. pMineR: An Innovative R Library for Performing Process Mining in Medicine.Google Scholar
- L Kaufman and P J Rousseeuw. 1987. Clustering by Means of Medoids. In Statistical Data Analysis based on the L1 Norm. 405--416.Google Scholar
- Angelina Prima Kurniati, Owen Johnson, David Hogg, and Geoff Hall. 2016. Process mining in oncology: A literature review. In Information Communication and Management (ICICM), International Conference on. IEEE, 291--297.Google Scholar
- Ronny Mans, Wil M. P. van der Aalst, and Rob J. B. Vanwersch. 2015. Process Mining in Healthcare - Evaluating and Exploiting Operational Healthcare Processes. Springer. Google ScholarDigital Library
- R. S. Mans, M. H. Schonenberg, Minseok Song, Wil M. P. van der Aalst, and Piet J. M. Bakker. 2008. Process Mining in Healthcare - A Case Study. In Proceedings of the First International Conference on Health Informatics, HEALTHINF. 118--125.Google Scholar
- F. Pacini, M. Schlumberger, H. Dralle, R. Elisei, J. W. Smit, and W. Wiersinga. 2006. European consensus for the management of patients with differentiated thyroid carcinoma of the follicular epithelium. European Thyroid Cancer Taskforce. Eur J Endocrinol 154, 6 (2006), 787--803.Google ScholarCross Ref
- E Rojas, J Munoz-Gama, M Sepúlveda, and D Capurro. 2016. Process mining in healthcare: A literature review. J Biomed Inform. 61 (2016), 224--s36. Google ScholarDigital Library
- Wil M. P. van der Aalst, Ton Weijters, and Laura Maruster. 2004. Workflow Mining: Discovering Process Models from Event Logs. IEEE Trans. Knowl. Data Eng. 16, 9 (2004), 1128--1142. Google ScholarDigital Library
Index Terms
- Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR
Recommendations
Process Mining for Clinical Processes: A Comparative Analysis of Four Australian Hospitals
Special Issue on Business Process IntelligenceBusiness process analysis and process mining, particularly within the health care domain, remain under-utilized. Applied research that employs such techniques to routinely collected health care data enables stakeholders to empirically investigate care ...
Monitoring care processes in the gynecologic oncology department
The care processes of healthcare providers are typically considered as human-centric, flexible, evolving, complex and multi-disciplinary. Consequently, acquiring an insight in the dynamics of these care processes can be an arduous task.A novel event log ...
Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach
ACSW '19: Proceedings of the Australasian Computer Science Week MulticonferenceChronic diseases and associated conditions are the leading causes of death in most of the countries worldwide. Due to this, governments all over the world are concerned about the burden of chronic diseases. These diseases often pose severe health risks ...
Comments