DocumentCode :
2544256
Title :
EasyMiner: data mining in medical databases
Author :
Saraee, Mohammad ; Koundourakis, George ; Theodoulidis, Babis
fYear :
1998
fDate :
36088
Firstpage :
42552
Lastpage :
42554
Abstract :
Data mining techniques have rarely been applied to medical domain. The University of Manchester Institute of Science and Technology (UMIST) is currently in the process of experimenting with a data mining project using an extensive clinical database of stroke patients from East Lancashire to identify factors that contribute to this disease. EasyMiner is our data mining system designed and developed in the Timelab research laboratory at UMIST for interactive mining of interesting patterns in time-oriented medical databases. This system implements a wide spectrum of data mining functions, including generalisation, relevance analysis, classification and discovery of association rules. The eventual goal of this data mining effort is to identify factors that will improve the quality and cost effectiveness of patient care. In this paper, we briefly describe the EasyMiner data mining approach
fLanguage :
English
Publisher :
iet
Conference_Titel :
Intelligent Methods in Healthcare and Medical Applications (Digest No. 1998/514), IEE Colloquium on
Conference_Location :
York
Type :
conf
DOI :
10.1049/ic:19981038
Filename :
744743
Link To Document :
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