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

Petrus, Khaleel and Li, Jiu-Yong and Fahey, Paul (2008) Comparing decision tree and optimal risk pattern mining for analysing Emergency Ultra Short Stay Unit data. In: ICMLC 2008: 7th International Conference on Machine Learning and Cybernetics , 12-15 Jul 2008, Kunming, China.


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.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 15 Mar 2018 06:26
Last Modified: 15 Mar 2018 06:26
Uncontrolled Keywords: association rules; data mining; decision trees; risk pattern mining; patients; hospital admissions; patient records
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
11 Medical and Health Sciences > 1117 Public Health and Health Services > 111711 Health Information Systems (incl. Surveillance)
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/ICMLC.2008.4620410

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