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.Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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.
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.
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a practically applicable guide to themethodologies 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 Healthcarecomprehensively covers howmachine 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. 320 pp. Englisch. Bestandsnummer des Verkäufers 9783030539924
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides analysis of real cases for illustrating the application of interactive process mining in medicineIncludes a compendium of tutorials, techniques and learning-oriented featuresUses an interacti. Bestandsnummer des Verkäufers 458548504
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 320 pp. Englisch. Bestandsnummer des Verkäufers 9783030539924
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a practically applicable guide to themethodologies 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 Healthcarecomprehensively covers howmachine 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. Bestandsnummer des Verkäufers 9783030539924
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