Comparing decision tree and optimal risk pattern mining for analysing Emergency Ultra Short Stay Unit data

Paper

Petrus, Khaleel, Li, Jiuyong and Fahey, Paul. 2008. "Comparing decision tree and optimal risk pattern mining for analysing Emergency Ultra Short Stay Unit data." ICMLC 2008: 7th International Conference on Machine Learning and Cybernetics. Kunming, China 12 - 15 Jul 2008 https://doi.org/10.1109/ICMLC.2008.4620410
Paper/Presentation Title

Comparing decision tree and optimal risk pattern mining for analysing Emergency Ultra Short Stay Unit data

Presentation TypePaper
AuthorsPetrus, Khaleel (Author), Li, Jiuyong (Author) and Fahey, Paul (Author)
Journal or Proceedings TitleProceedings of the 7th International Conference on Machine Learning and Cybernetics (ICMLC 2008)
Journal Citation1, pp. 234-239
Number of Pages6
Year2008
ISBN9781424420957
Digital Object Identifier (DOI)https://doi.org/10.1109/ICMLC.2008.4620410
Web Address (URL) of Paperhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4620410
Conference/EventICMLC 2008: 7th International Conference on Machine Learning and Cybernetics
Event Details
ICMLC 2008: 7th International Conference on Machine Learning and Cybernetics
Event Date
12 to end of 15 Jul 2008
Event Location
Kunming, China
Abstract

A data set contains patient records of Ultra Short Stay Unit (USSU) at emergency department at Toowoomba Base Hospital. Some patients were admitted to the hospital for further treatment after a long stay at USSU and other patients were discharged after a short stay at USSU. In most hospitals the USSU is not enough for large demand, and there will be better utilisation of the unit if medical professionals know what types of patients are more likely to be hospitalised before any treatment at USSU. Two data mining methods; decision trees and optimal risk pattern mining, have been applied on the data to explore cohorts of patients who are more likely to be admitted to the hospital. Results show that decision tree method is inadequate for finding understandable patterns, and that optimal risk pattern mining method is good for mining meaningful patterns for medical practitioners.

Keywordsassociation rules; data mining; decision trees; risk pattern mining; patients; hospital admissions; patient records
ANZSRC Field of Research 2020460902. Decision support and group support systems
469999. Other information and computing sciences not elsewhere classified
420399. Health services and systems not elsewhere classified
Public Notes

© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Byline AffiliationsDepartment of Mathematics and Computing
University of South Australia
Institution of OriginUniversity of Southern Queensland
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