Skip to main content
SpringerLink
Log in

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

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Agrawal, R., Imielinski, T. & Swami, A. (1993). Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering 5(6): 914-925.

    Google Scholar 

  • Brill, E. (1993). A Corpus-based Approach to Language Learning. PhD thesis, The University of Pennsylvania, Philadelphia, PA.

    Google Scholar 

  • Chen, H., Chung, Y., Houston, A. L., Li, P. C. & Schatz, B. R. (1999). Using Neural Networks for Vocabulary Switching. Submitted to IEEE Expert.

  • Chen, H., Houston, A., Yen, J. & Nunamaker, J. F. (1996a). Toward Intelligent Meeting Agents. IEEE COMPUTER (August) 29(8): 62-70.

    Google Scholar 

  • Chen, H., Houston, A. L., Swell, R. R. & Schatz B. R. (1998a). Internet Browsing and Searching: User Evaluations of Category Map and Concept Space Techniques. Journal of the American Society for Information Science (May) 49(7): 582-603.

    Google Scholar 

  • Chen, H., Hsu, P., Orwig, R., Hoopes, L. & Nunamaker, J. F. (1994). Automatic Concept Classification of Text from Electronic Meetings. Communications of the ACM (October) 37(10): 56-73.

    Google Scholar 

  • Chen, H. & Lynch, K. J. (1992). Automatic Construction of Networks of Concepts Characterizing Document Databases. IEEE Transactions on Systems, Man and Cybernetics (September/October) 22(5): 885-902.

    Google Scholar 

  • Chen, H., Lynch, K. J. Basu, K. & Ng, D. T. (1993). Generating, Integrating, and Activating Thesauri for Concept-Based Document Retrieval. IEEE EXPERT, Special Series on Artificial Intelligence in Text-Based Information Systems (April) 8(2): 25-34.

    Google Scholar 

  • Chen, H. & Ng, D. T. (1995). An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound vs. Connectionist Hopfield Net Activation. Journal of the American Society for Information Science (June) 46(5): 348-369.

    Google Scholar 

  • Chen, H., Schuffels, C. & Orwig, R. (1996b). Internet Categorization and Search: A Machine Learning approach. Journal of Visual Communications and Image Representations (March) 7(1): 88-102.

    Google Scholar 

  • Chen, H., Zhang, Y. & Houston, A. L. (1998b). Semantic Indexing and Searching Using a Hopfield Net. Journal of Information Science (JIS) (January) 24(1): 3-18.

    Google Scholar 

  • Dalton, J. & Deshmane A. (1991). Artificial Neural Networks. IEEE Potentials (April) 10(2): 33-36.

    Google Scholar 

  • Decker, K. M. & Focardi, S. (1995). Technology Overview: A Report on Data Mining. Technical Report CSCS TR-95-92, Swiss Scientific Computing Center.

  • Fayyad, U. M., Piatetsky-Shapiro, G. & Smyth, P. (1996a). From Data Mining to Knowledge Discovery: An Overview. In Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, 1-36. AAAI Press/MIT Press.

  • Fayyad, U. M., Piatetsky-Shapiro, G. & Smyth, P. (1996b). From Data Mining to Knowledge Discovery in Databases. AI Magazine 17(3): 37-54.

    Google Scholar 

  • Holsheimer, M. & Siebes, A. P. J. M. (1994). Data Mining: The Search for Knowledge in Databases. Technical Report CS-R9406, CWI: Dutch National Research Center.

  • Honkela, T., Kaski, S., Kohonen, T. & Lagus, K. (1998). Self-Organizing Maps of Very Large Document Collections: Justification for the WEBSOM Method. In Balderjahn, I., Mathar, R. & Schader, M. (eds.) Classification, Data Analysis, and Data Highways, 245-252. Springer: Berlin.

    Google Scholar 

  • Honkela, T., Kaski, S., Lagus, K. & Kohonen, T. (1996a). Newsgroup Exploration with WEBSOM Method and Browsing Interface. Techical Report A32, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland.

    Google Scholar 

  • Honkela, T., Kaski, S., Lagus, K. & Kohonen, T. (1996b). Self-Organizing Maps of Document Collections. ALMA 1(2). Electronic Journal, address: (http://www.diemme.it/luigi/alma.html).

  • Hopfield, J. J. (1982). Neural Network and Physical Systems with Collective Computational Abilities. Proceedings of the National Academy of Science, USA 79(4): 2554-2558.

    Google Scholar 

  • Houston, A. L., Chen, H., Schatz, B. R., Hubbard, S. M., Doszkocs, T. E., Swell, R. R., Tolle, K. M. & Ng, T. D. (1996). Exploring the Use of Concept Space, Category Map Techniques and Natural Language Parsers to Improve Medical Information Retrieval. Technical report, University of Arizona, AI Group Working Paper, January.

  • Houston, A. L., Chen, H., Schatz, B. R., Hubbard, S. M., Swell, R. R. & Ng, T. D. (1998). Exploring the Use of Concept Space to Improve Medical Information Retrieval. International Journal of Decision Support Systems, forthcoming.

  • Hubbard, S. M., Martin, N. B. & Thurn, A. L. (1995). NCI's Cancer Information Systems-Bringing Medical Knowledge to Clinicians. Oncology (April) 9(4): 302-314.

    Google Scholar 

  • Kashi, S., Honkela, T., Lagus, K. & Kohonen, T. (1996). Creating an Order in Digital Libraries with Self-Organizing Maps. In Proceedings of WCNN '96, World Congress on Neural Networks, September 15-18, San Diego, California, 814-817. Mahwah, NJ: Lawrence Erlbaum and INNS Press.

    Google Scholar 

  • Khosla, R. & Dillon, T. (1997). Knowledge Discovery, Data Mining and Hybrid Systems. In Engineering Intelligent Hybrid Multi-Agent Systems, 143-177. Kluwer Academic Publishers.

  • Knight, K. (1990). Connectionist Ideas and Algorithms. Communications of the ACM (November) 33(11): 59-74.

    Google Scholar 

  • Kohonen, T. (1989). Self-Organization and Associate Memory, 3rd edn. Springer-Verlag: Berlin Heidelberg.

    Google Scholar 

  • Kohonen, T. (1995). Self-Organization Maps. Springer-Verlag: Berlin Heidelberg.

    Google Scholar 

  • Lippmann, R. P. (1987). An Introduction to Computing with Neural Networks. IEEE Acoustics Speech and Signal Processing Magazine (April) 4(2), 4-22.

    Google Scholar 

  • Ritter, H. & Kohonen, T. (1989). Self-Organizing Semantic Maps. Biological Cybernetics 61: 241-254.

    Google Scholar 

  • Salton, G. (1989). Automatic Text Processing. Addison-Wesley Publishing Company, Inc.: Reading, MA.

    Google Scholar 

  • Tank, D.W. & Hopfield, J. J. (1987). Collective Computation in Neuronlike Circuits. Scientific American (December) 257(6): 104-114.

    Google Scholar 

  • Tolle, K. M. (1997). Improving Concept Extracting from Text Using Natural Language Processing Noun Phrasing Tools: An Experiment in Medical Information Retrieval. Master's thesis, Unversity of Arizona, Department of MIS, Tucson, AZ, May.

    Google Scholar 

  • Uthurusamy, R. (1996). From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. In Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, 561-572. AAAI Press/MIT Press.

  • Varnado, S. (1995). The Role of Information Technology in Reducing Health Care Cost. In Proceedings of SPIE — The International Society for Optical Engineering volume 2618: Health Care Information Infrastructure, 36-46. Philadelphia, PA, October.

Download references

Author information

Authors and Affiliations

  1. Management Information Systems Department, University of Arizona, Tucson, Arizona, 85721

    Andrea L. Houston, Hsinchun Chen, Tobun D. Ng & Kristin M. Tolle

  2. National Cancer Institute, Bethesda, MD, 20852

    Susan M. Hubbard

  3. University of Illinois at Urbana-Champaign, Urbana, IL, 61801

    Bruce R. Schatz

  4. School of Library Science, University of Arizona, Tucson, Arizona, 85721

    Robin R. Sewell

Authors
  1. Andrea L. Houston
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsinchun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Susan M. Hubbard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bruce R. Schatz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tobun D. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robin R. Sewell
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kristin M. Tolle
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Houston, A.L., Chen, H., Hubbard, S.M. et al. Medical Data Mining on the Internet: Research on a Cancer Information System. Artificial Intelligence Review 13, 437–466 (1999). https://doi.org/10.1023/A:1006548623067

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1006548623067

Search

Navigation