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Interactive Process Mining in Healthcare [electronic resource]
Fernandez-Llatas, Carlos
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| 자료유형 | 단행본 |
|---|---|
| 개인저자 | Fernandez-Llatas, Carlos.,editor. |
| 단체저자명 | SpringerLink (Online service). |
| 서명/저자사항 | Interactive Process Mining in Healthcare [electronic resource] / edited by Carlos Fernandez-Llatas. |
| 판사항 | 1st ed. 2021. |
| 형태사항 | XIV, 306 p. 130 illus., 92 illus. in color:online resource. |
| 총서사항 | Health Informatics=2197-3741 |
| 총서부출표목 | Health Informatics, |
| 기본자료 저록 | Springer Nature eBook |
| 기타형태 저록 | Printed edition:9783030539924Printed edition:9783030539948Printed edition:9783030539955 |
| ISBN | 9783030539931 |
| 기타표준부호 | 10.1007/978-3-030-53993-1 |
| 내용주기 | Introduction -- Toward an integration of Data Science and Medical Domain -- To an interactive machine learning approach -- Process Mining for Healthcare -- Interactive process Mining paradigm -- Interactive Process Mining in Practice: Interactive Key Process Indicators -- Data Quality in Process Mining, Legal Issues and Open Data Integration -- Real Success Cases -- New Challenges. . |
| 요약 | 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. . |
| 일반주제명 | Medical informatics. Data mining. Bioinformatics. Health Informatics. Data Mining and Knowledge Discovery. Bioinformatics. |
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