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.
Reprinted by permission, Daniel Gartner, Rainer Kolisch, Daniel B. Neill and Rema Padman, Machine Learning Approaches for Early DRG Classification and Resource Allocation, INFORMS Journal on Computing. Copyright 2015, the Institute for Operations Research and the Management Sciences, 5521 Research Park Drive, Suite 200, Catonsville, Maryland 21228 USA.
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Gartner, D. (2014). Experimental Analyses. In: Optimizing Hospital-wide Patient Scheduling. Lecture Notes in Economics and Mathematical Systems, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-04066-0_4
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DOI: https://doi.org/10.1007/978-3-319-04066-0_4
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