This book provides a practically applicable guide to the methodologies and technologies for the application of interactive process mining paradigm. Case studies are presented where this paradigm has been successfully applied in emergency medicine, surgery processes, human behavior modelling, strokes and outpatients' services, enabling the reader to develop a deep understanding of how to apply process mining technologies in healthcare to support them in inferring new knowledge from past actions, and providing accurate and personalized knowledge to improve their future clinical decision-making.

Interactive Process Mining in Healthcare comprehensively covers how machine learning algorithms can be utilized to create real scientific evidence to improve daily healthcare protocols, and is a valuable resource for a variety of health professionals seeking to develop new methods to improve their clinical decision-making.

Autorentext

Carlos Fernández-Llatas is Deputy Director in SABIEN at ITACA institute at Universitat Politècnica de València and Affiliated Researcher at Karolinska Institutet. He is founder & member of the Steering Committee of the Process Oriented Data Science 4 Healthcare Alliance and part of the IEEE Task Force on Process Mining. He participated in more than 30 projects through IV, V VI and VII European Framework program, H2020 program and Spanish Government funded projects. He has published more than 100 scientific papers. His research is mainly focused in the use and promotion of Process Mining technologies for their application in health.

Zusammenfassung

This book provides a practically applicable guide to the methodologies and technologies for the application of interactive process mining paradigm. Case studies are presented where this paradigm has been successfully applied in emergency medicine, surgery processes, human behavior modelling, strokes and outpatients' services, enabling the reader to develop a deep understanding of how to apply process mining technologies in healthcare to support them in inferring new knowledge from past actions, and providing accurate and personalized knowledge to improve their future clinical decision-making.

Interactive Process Mining in Healthcare comprehensively covers how machine learning algorithms can be utilized to create real scientific evidence to improve daily healthcare protocols, and is a valuable resource for a variety of health professionals seeking to develop new methods to improve their clinical decision-making.

Inhalt
Introduction.- Toward an integration of Data Science and Medical Domain.- To an interactive machine learning approach.- Process Mining for Healthcare.- Part II Interactive Process Mining.- Interactive process Mining paradigm.- Interactive Process Mining in Practice: Interactive Key Process Indicators.- Part III Interactive Process Mining in Action.- Data Quality in Process Mining, Legal Issues and Open Data Integration.- Real Success Cases.- New Challenges.