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).
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Wickramasinghe, K., Alahakoon, D., Schattner, P., Georgeff, M. (2011). Self-Organizing Maps for Translating Health Care Knowledge: A Case Study in Diabetes Management. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_17
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DOI: https://doi.org/10.1007/978-3-642-25832-9_17
Publisher Name: Springer, Berlin, Heidelberg
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