Sage Journals: Discover world-class research

Abstract

The new computational algorithms emerging in the data mining literature—in particular, the self-organizing map (SOM) and decision tree analysis (DTA)—offer qualitative researchers a unique set of tools for analyzing health informatics data. The uniqueness of these tools is that although they can be used to find meaningful patterns in large, complex quantitative databases, they are qualitative in orientation. To illustrate the utility of these tools, the authors review the two most popular: the SOM and DTA. They provide a basic definition of health informatics, focusing on how data mining assists this field, and apply the SOM and DTA to a hypothetical example to demonstrate what these tools are and how qualitative researchers can use them.

Get full access to this article

View all access options for this article.

References

Berry, M., & Linoff, S. (2000). Mastering data mining: The art and science of customer relationship management. New York: Wiley.

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Boca Raton, FL: Chapman & Hall/CRC.

Capra, F. (1996). The web of life. New York: Anchor Doubleday.

Castellani, B., Castellani, J., & Spray, S. L. (in press). Grounded neural networking: Modeling complex quantitative data. Symbolic Interaction.

Cilliers, P. (1998). Complexity and postmodernism: Understanding complex systems. New York: Routledge.

Cios, K. J. (2001). Medical data mining and knowledge discovery. Denver, CO: Springer-Verlag.

Department of Health and Human Services. (2000). Healthy people 2010: With understanding and improving health and objectives for improving health (2nd ed., 2 vols.). Washington, DC: Government Printing Office.

Englebardt, S. (2002). Health care informatics: An interdisciplinary approach. St. Louis, MO: C. V. Mosby.

Garson, D. (1998). Neural networks: An introductory guide for social scientists. Thousand Oaks, CA: Sage.

10.

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. New York: Aldine De Gruyter.

11.

Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann.

12.

Hebda, T, (2001). Handbook of informatics for nurses and health care professionals. Upper Saddle River, NJ: Prentice Hall.

13.

Klose, A., Nurnberger, A., Nauck D., & Kruse, R. (2001). Data mining with neuro-fuzzy models. In A. Kandel, M. Last, & H. Bunke (Eds.), Data mining and computational intelligence (pp. 1-36). Denver, CO: Springer-Verlag.

14.

Kohonen, T. (1999). Spotting relevant information in extremely large document collections. In B. Reusch (Ed.), Computational intelligence: Theory and applications (pp. 59-61). New York: Springer.

15.

Kohonen, T. (2001). Self-organizing maps (3rd ed.). New York: Springer.

16.

Kosko, B. (1993). Fuzzy thinking: The new science of fuzzy logic. New York: Hyperion.

17.

Lloyd-Williams, M. (1999). Empirical studies of the knowledge discovery approach to health- information analysis. In M. A. Bramer (Ed.), Knowledge discovery and data mining. London: Institution of Electrical Engineers.

18.

Morse, J. (2002). Qualitative health research: Challenges for the 21st century. Qualitative Health Research, 12, 116-129.

19.

Ragin, C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.

20.

Richards, G., Rayward-Smith, V., Sönksen, P., Carey, S., & Weng, C. (2001). Data mining for indicators of early mortality in a database of clinical records. Artificial Intelligence in Medicine, 22, 215-231.

21.

Stefanelli, M. (2001). The socio-organizational age of artificial intelligence in medicine. Artificial Intelligence in Medicine, 23, 25-47.

22.

Tan, J. (2001). Health management information systems: Method and practical applications. Gaithersburg, MD: Aspen.

23.

Tsantis, L., & Castellani, J. (2001). Enhancing learning environments through solution-based knowledge discovery tools: Forecasting for self-perpetuating systemic reform. Journal of Special Education Technology, 16(4), 39-52.

24.

Young, K. (2000). Informatics for healthcare professionals. Philadelphia: F. A. Davis.

Data Mining: Qualitative Analysis with Health Informatics Data

Abstract

Get full access to this article

References