Medical Knowledge Discovery Systems: Data Abstraction And Performance Measurement

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Charlie Chen Ph.D, professor (Creator)
Institution
Appalachian State University (ASU )
Web Site: https://library.appstate.edu/

Abstract: Knowledge discovery systems can be traced back to their origin, artificial intelligence and expert systems, but use the modern technique of data mining for the knowledge discovery process. To that end, the technical community views data mining as one step in the knowledge discovery process, while the non-technical community seems to view it as encompassing all of the steps to knowledge discovery. In this exploratory study, we look at medical knowledge discovery systems (MKDSs) by first looking at three examples of expert systems to generate medical knowledge. We then expand on the use of data abstraction as a pre-processing step in the comprehensive task of medical knowledge discovery. Next, we look at how performance of a medical knowledge discovery system is measured. Finally, the conclusions point to a bright future for MKDSs, but an area that needs extensive development to reach its full potential.

Additional Information

Publication
Albert L Harris, Charlie C. Chen, William J Perkins (2004) "Medical knowledge discovery systems: data abstraction and performance measurement" Knowledge Management Research & Practice #2 pp.95–102 Version of Record Available @ www.sciencedirect.com [DOI:10.1057/palgrave.kmrp.8500027]
Language: English
Date: 2004
Keywords
medical knowledge discovery, artificial intelligence, expert systems, intelligent data analysis, data mining

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