Abstract
Estimating resource consumption of hospital patients is important for various tasks such as hospital funding, and management and allocation of resources. The common approach is to group patients based on their diagnostic characteristic and infer their resource consumption based on their group membership. This research looks at two alternative forms of grouping of patients based on supervised (classification trees) and unsupervised (self organising map) learning methods. This research is a longitudinal comparison of the effect of supervised and unsupervised learning methods on the groupings of patients. The results for the four-year study indicate that the learning paradigms appear to group patients similarly according to their resource consumption.
Keywords
- Resource Consumption
- Terminal Node
- Unsupervised Learning
- Longitudinal Comparison
- Unsupervised Learning Method
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.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Rights and permissions
About this chapter
Cite this chapter
Siew, EG., A. Smith, K., Churilov, L., Wassertheil, J. A Longitudinal Comparison of Supervised and Unsupervised Learning Approaches to Iso-Resource Grouping for Acute Healthcare in Australia. In: K. Halgamuge, S., Wang, L. (eds) Classification and Clustering for Knowledge Discovery. Studies in Computational Intelligence, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11011620_18
Download citation
DOI: https://doi.org/10.1007/11011620_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26073-8
Online ISBN: 978-3-540-32404-1
eBook Packages: EngineeringEngineering (R0)