What is Interactive Process Mining?

Success cases

Publications

 


 

What is Interactive Process Mining?

  • More and more intelligent systems are able to extract information about how patients are provided with health services.
  • However, these systems are usually black boxes for professionals who have to rely blindly on the results they offer without getting clues as to why they make those decisions.
  • The proposal of this experience is based on the application of interactive models. Interactive models are intelligent systems that are focused on producing models that can be understood by health professionals. 
  • Within these models, we propose the use of Process Mining techniques, which are able to show the process that the patient follows in an understandable and interactive way.
  • In this way it is intended that the expert be able to incorporate their own professional experience to understand, evaluate the value chain and optimize the care processes that are applied to patients.
  • This line presents an application for the application of Interactive Process Mining in the field of health called PMApp.

This tool has been tested in numerous health centers around the world and in different fields such as obesity, diabetes, surgical procedures, urgencies, stroke, dementia … among many others with very promising results.

The objective of this seminar is to expose the possibilities of interactive process mining for its application in the Sanitary environment in order to understand the processes that make people sick and to analyze, evaluate and measure the impact of health policies and treatments. apply in the patient from a value-based medicine point of view.

 


 

Success cases

Coming soon

 


Publications

  • Martinez-Millana, A. Lizondo, R. Gatta, S. Vera, V. T. Salcedo, and C. Fernandez-Llatas, Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process, International Journal of Environmental Research and Public Health, vol. 16, no. 2, p. 199, Jan. 2019.
  • Fernandez-Llatas, G. Ibanez-Sanchez, A. Celda, J. Mandingorra, L. Aparici-Tortajada, A. Martinez-Millana, J. Munoz-Gama, M. Sepúlveda, E. Rojas, V. Gálvez, D. Capurro, and V. Traver, Analyzing Medical Emergency Processes with Process Mining: The Stroke Case, in Business Process Management Workshops, Cham, 2019, pp. 214–225.
  • Dogan, J.-L. Bayo-Monton, C. Fernandez-Llatas, and B. Oztaysi, Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application, Sensors, vol. 19, no. 3, 2019.
  • Conca, C. Saint-Pierre, V. Herskovic, M. Sepúlveda, D. Capurro, F. Prieto, and C. Fernandez-Llatas, Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining, Journal of medical Internet research, vol. 20, no. 4, 2018.
  • Fernandez-Llatas, E. Montón, J. Rovira, S. Vera, and V. Traver, Mineria de procesos interactiva: Aproximando el Big Data a la practica clinica, I+ S: informática y salud, no. 124, pp. 44–50, 2017.
  • Rojas, C. Fernández-Llatas, V. Traver, J. Munoz-Gama, M. Sepúlveda, V. Herskovic, and D. Capurro, PALIA-ER: Bringing Question-Driven Process Mining Closer to the Emergency Room, in 15th International Conference on Business Process Management (BPM 2017), 2017.
  • Gatta, J. Lenkowicz, M. Vallati, E. Rojas, A. Damiani, L. Sacchi, B. De Bari, A. Dagliati, C. Fernandez-Llatas, M. Montesi, and others, pMineR: An Innovative R Library for Performing Process Mining in Medicine, in Proceedings of the Conference on Artificial Intelligence in Medicine (AIME 2017), Springer, 2017.
  • Rojas, M. Sepúlveda, J. Munoz-Gama, D. Capurro, V. Traver, and C. Fernandez-Llatas, Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining, Applied Sciences, vol. 7, no. 3, p. 302, 2017.
  • Fernandez-Llatas, J. L. Bayo, A. Martinez-Romero, J. M. Benedi, and V. Traver, Interactive Pattern Recognition in Cardiovascular Diseases Management. A Process Mining Approach, in Proceedings of the IEEE International Conference on Biomedical and Health Informatics 2016, las Vegas, EEUU, 2016.
  • Fernandez-Llatas, A. Lizondo, E. Monton, J.-M. Benedi, and V. Traver, Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems, Sensors, vol. 15, no. 12, pp. 29821–29840, Nov. 2015.
  • Fernandez-Llatas, B. Valdivieso, V. Traver, and J. M. Benedi, Using Process Mining for Automatic Support of Clinical Pathways Design, in Data Mining in Clinical Medicine, no. 1246, C. Fernández-Llatas and J. M. García-Gómez, Eds. Springer New York, 2015, pp. 79–88.
  • Fernandez-Llatas, A. Martinez-Millana, A. Martinez-Romero, J. M. Benedi, and V. Traver, Diabetes care related process modelling using Process Miningtechniques. Lessons learned in the application of InteractivePattern Recognition: coping with the Spaghetti Effect, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 2127–2130.
  • Fernandez-Llatas, L. Sacchi, J. M. Benedí, A. Dagliati, V. Traver, and R. Bellazzi, Temporal Abstractions to Enrich Activity-Based Process Mining Corpus with Clinical Time Series, in Proceedings of the International conference on Biomedical and HealthInformatics (BHI2014), 2014.
  • Fernández-Llatas, J.-M. Benedi, J. M. García-Gómez, and V. Traver, Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes, Sensors, vol. 13, no. 11, pp. 15434–15451, 2013.
  • Fernández-Llatas, T. Meneu, V. Traver, and J.-M. Benedi, Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation, International Journal of Environmental Research and Public Health, vol. 10, no. 11, pp. 5671–5682, 2013.