Data Mining: Qualitative Analysis with Health Informatics Data

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.

[1]  Rudolf Kruse,et al.  Data mining with neuro-fuzzy models , 2001 .

[2]  G. David Garson,et al.  Neural Networks: An Introductory Guide for Social Scientists , 1999 .

[3]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[4]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[5]  Toni Hebda,et al.  Handbook of Informatics for Nurses & Health Care Professionals , 1997 .

[6]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[7]  Michael J. A. Berry,et al.  Mastering Data Mining: The Art and Science of Customer Relationship Management , 1999 .

[8]  P. Cilliers,et al.  Complexity and post-modernism: understanding complex systems , 1999 .

[9]  K. Cios.  Medical data mining and knowledge discovery. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[10]  Janice M Morse,et al.  Qualitative Health Research: Challenges for the 21st Century , 2002, Qualitative health research.

[11]  A. Strauss,et al.  The Discovery of Grounded Theory , 1967 .

[12]  K. Young,et al.  Informatics for Healthcare Professionals , 2000 .

[13]  Charles C. Ragin,et al.  Fuzzy-Set Social Science , 2001 .

[14]  John Castellani,et al.  Enhancing Learning Environments through Solution-Based Knowledge Discovery Tools: Forecasting for Self-perpetuating Systemic Reform , 2001 .

[15]  F. Capra.  The Web of Life , 1996 .

[16]  Michael Lloyd-Williams.  Empirical studies of the knowledge discovery approach to health-information analysis , 1999, KDD 1999.

[17]  Joseph K. Tan.  Health Management Information Systems: Methods and Practical Applications , 2001 .

[18]  K.J. Cios,et al.  From the guest editor medical data mining and knowledge discovery , 2000, IEEE Engineering in Medicine and Biology Magazine.

[19]  John Castellani,et al.  Grounded Neural Networking: Modeling Complex Quantitative Data , 2003 .

[20]  S. Englebardt,et al.  Health care informatics : an interdisciplinary approach , 2002 .

[21]  Mario Stefanelli,et al.  The socio-organizational age of artificial intelligence in medicine , 2001, Artif. Intell. Medicine.

[22]  Teuvo Kohonen.  Spotting Relevant Information in Extremely Large Document Collections , 1999, Fuzzy Days.

[23]  P. H. Sönksen,et al.  Data mining for indicators of early mortality in a database of clinical records , 2001, Artif. Intell. Medicine.