COMPARING DECISION TREE AND OPTIMAL RISK PATTERN MINING FOR ANALYSING EMERGENCY ULTRA SHORT STAY UNIT DATA

摘要：

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.

关键词： Data mining decision trees association rules risk pattern mining

作者: KHALEEL PETRUS JIU-YONG LI PAUL FAHEY

作者单位: Department of Mathematics and Computing, University Southern Queensland, Toowoomba, Australia School of Computer and Information Science, University of South Australia, Adelaide, Australia

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 234-239

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）