Authors:
Introduces and evaluates a thorough examination of attribute selection techniques and classification approaches for early diagnosis-related group (DRG) classification
Formulates two hospital-wide patient scheduling models using mathematical programming in order to maximize contribution margin
Presents methods for a substantial improvement of classification accuracy and contribution margin as compared to current practice
Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Economics and Mathematical Systems (LNE, volume 674)
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Table of contents (5 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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TUM School of Management, Technische Universität München, München, Germany
Daniel Gartner
About the author
Bibliographic Information
Book Title: Optimizing Hospital-wide Patient Scheduling
Book Subtitle: Early Classification of Diagnosis-related Groups Through Machine Learning
Authors: Daniel Gartner
Series Title: Lecture Notes in Economics and Mathematical Systems
DOI: https://doi.org/10.1007/978-3-319-04066-0
Publisher: Springer Cham
eBook Packages: Business and Economics, Business and Management (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Softcover ISBN: 978-3-319-04065-3Published: 09 June 2015
eBook ISBN: 978-3-319-04066-0Published: 23 May 2015
Series ISSN: 0075-8442
Series E-ISSN: 2196-9957
Edition Number: 1
Number of Pages: XIV, 119
Number of Illustrations: 22 b/w illustrations
Topics: Operations Research/Decision Theory, Health Informatics, Health Informatics, Operations Research, Management Science, Health Care Management