EasyMiner: data mining in medical databases

Saraee, MH; Koundourakis, G; Theodoulidis, B

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

Citation

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

• Presentation Conference Type: Other
• Conference Name: IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications
• Conference Location: York, UK
• Start Date: Oct 20, 1998
• Deposit Date: Oct 27, 2011
• Book Title: IEE Colloquium Intelligent Methods in Healthcare and Medical Applications
• DOI: https://doi.org/10.1049/ic%3A19981038
• Publisher URL: http://dx.doi.org/10.1049/ic:19981038
• Additional Information: Event Type : Conference