Experimental Analyses
Daniel Gartner
Additional contact information
Daniel Gartner: Technische Universität München
Chapter Chapter 4 in Optimizing Hospital-wide Patient Scheduling, 2014, pp 55-92 from Springer
Abstract:
Abstract The structure of this chapter is as follows: In the first section, a thorough analysis of the presented machine learning methods for early DRG classification and its comparison with a DRG grouper is provided. In the second section, a computational and economic analysis of scheduling the hospital-wide patient flow of elective patients is given.
Keywords: Classification Accuracy; Machine Learning Method; Admission Diagnosis; Attribute Selection; Misclassification Cost (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-319-04066-0_4
Ordering information: This item can be ordered from
http://www.springer.com/9783319040660
DOI: 10.1007/978-3-319-04066-0_4
Access Statistics for this chapter
More chapters in Lecture Notes in Economics and Mathematical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().