Machine Learning Approaches for Early DRG Classification and Resource Allocation

DOI10.1287/ijoc.2015.0655zbMath1338.90274OpenAlexW2178355895WikidataQ57436179 ScholiaQ57436179MaRDI QIDQ2802250

Daniel Gartner, Rainer Kolisch, Daniel B. Neill, Rema Padman

Publication date: 25 April 2016

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: http://orca.cf.ac.uk/87485/1/GartnerKolischNeillPadman_final.pdf

zbMATH Keywords

classificationmathematical programmingmachine learningdiagnosis-related groupsattribute selection

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Mixed integer programming (90C11) Resource and cost allocation (including fair division, apportionment, etc.) (91B32)

Related Items (1)

Divide and conquer: a granular concept-cognitive computing system for dynamic classification decision making

Uses Software

• bnlearn
• C4.5
• ReliefF
• PRMLT

Cites Work

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• Optimizing feature selection to improve medical diagnosis
• Benchmark optimization and attribute identification for improvement of container terminals
• Theoretical and empirical analysis of ReliefF and RReliefF
• Very simple classification rules perform well on most commonly used datasets
• Tabu Search-Enhanced Graphical Models for Classification in High Dimensions
• A Model of ICU Bumping
• 10.1162/153244304322972694

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