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