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EasyMiner: data mining in medical databases

Saraee, M, Koundourakis, G and Theodoulidis, B 1998, EasyMiner: data mining in medical databases , in: IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications, 20 October 1998, York, UK.

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    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

    Item Type: Conference or Workshop Item (Paper)
    Themes: Health and Wellbeing
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: IEEE Colloquium on Intelligent Methods in Healthcare and Medical Applications
    Publisher: IEEE
    Refereed: Yes
    Related URLs:
    Depositing User: Dr Mo Saraee
    Date Deposited: 27 Oct 2011 09:39
    Last Modified: 20 Aug 2013 18:16
    URI: http://usir.salford.ac.uk/id/eprint/18697

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