Medical Data Mining on the Internet: Research on a Cancer Information System

Authors:
Andrea L. Houston;Hsinchun Chen;Susan M. Hubbard;Bruce R. Schatz;Tobun D. Ng;Robin R. Sewell;Kristin M. Tolle
Affiliations:
Management Information Systems Department, University of Arizona, Tucson, Arizona 85721 (E-mail: ahouston@bpa.arizona.edu);Management Information Systems Department, University of Arizona, Tucson, Arizona 85721 (E-mail: hchen@bpa.arizona.edu);National Cancer Institute, Bethesda, MD 20852 (E-mail: su@icicb.nci.nih.gov);University of Illinois at Urbana-Champaign, Urbana, IL 61801 (E-mail: schatz@csl.ncsa.uiuc.edu);Management Information Systems Department, University of Arizona, Tucson, Arizona 85721 (E-mail: TNg@bpa.arizona.edu);School of Library Science, University of Arizona, Tucson, Arizona 85721 (E-mail: rrs@ai2.bpa.arizona.edu);Management Information Systems Department, University of Arizona, Tucson, Arizona 85721 (E-mail: ktolle@bpaosf.bpa.arizona, edu)
Venue:
Artificial Intelligence Review - Special issue on data mining on the Internet
Year:
1999

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Abstract

This paper discusses several data mining algorithms and techniques thatwe have developed at the University of Arizona Artificial Intelligence Lab.We have implemented these algorithms and techniques into severalprototypes, one of which focuses on medical information developed incooperation with the National Cancer Institute (NCI) and the University ofIllinois at Urbana-Champaign. We propose an architecture for medicalknowledge information systems that will permit data mining across severalmedical information sources and discuss a suite of data mining tools that weare developing to assist NCI in improving public access to and use of theirexisting vast cancer information collections.