Self-Organizing Maps for Translating Health Care Knowledge: A Case Study in Diabetes Management

  • Kumari Wickramasinghe
  • Damminda Alahakoon
  • Peter Schattner
  • Michael Georgeff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

Chronic Disease Management (CDM) is an important area of health care where Health Knowledge Management can provide substantial benefits. A web-based chronic disease management service, called cdmNet, is accumulating detailed data on CDM as it is being rolled out across Australia. This paper presents the application of unsupervised neural networks to cdmNet data to: (1) identify interesting patterns in diabetes data; and (2) assist diabetes related policy-making at different levels. The work is distinct from existing research in: (1) the data; (2) the objectives; and (3) the techniques used. The data represents the diabetes population across the entire primary care sector. The objectives include diabetes related decision and policy making at different levels. The pattern recognition techniques combine a traditional approach to data mining, involving the Self-Organizing Map (SOM), with an extension to include the Growing Self-Organizing Map (GSOM).

Keywords

Spread Factor Chronic Disease Management Business Intelligence Medical Feature Chronic Care Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kumari Wickramasinghe
    • 1
  • Damminda Alahakoon
    • 2
  • Peter Schattner
    • 1
  • Michael Georgeff
    • 1
  1. 1.Department of General Practice, Faculty of Medicine, Nursing and Health SciencesMonash UniversityAustralia
  2. 2.Faculty of Information TechnologyMonash UniversityAustralia

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