Data Mining in Biomedicine: Current Applications and Further Directions for Research
DOI: 10.4236/jsea.2009.23022   PDF    HTML     8,897 Downloads   17,531 Views   Citations


Data mining is the process of finding the patterns, associations or relationships among data using different analytical techniques involving the creation of a model and the concluded result will become useful information or knowledge. The advancement of the new medical deceives and the database management systems create a huge number of data-bases in the biomedicine world. Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. This paper introduces some basic data mining techniques, unsupervised learning and supervising learning, and reviews the application of data mining in biomedicine. Applications of the multimedia mining, including text, image, video and web mining are discussed. The key issues faced by the computing professional, medical doctors and clinicians are highlighted. We also state some foreseeable future developments in the field. Although extracting useful information from raw biomedical data is a challenging task, data mining is still a good area of scientific study and remains a promising and rich field for research.

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S. TING, C. SHUM, S. KWOK, A. TSANG and W. LEE, "Data Mining in Biomedicine: Current Applications and Further Directions for Research," Journal of Software Engineering and Applications, Vol. 2 No. 3, 2009, pp. 150-159. doi: 10.4236/jsea.2009.23022.

Conflicts of Interest

The authors declare no conflicts of interest.


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