An optimization-based approach to patient grouping for acute healthcare in Australia

Authors:
A. M. Bagirov;L. Churilov
Affiliations:
Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, The University of Ballarat, Victoria, Australia;School of Business Systems, Monash University, Victoria, Australia
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Year:
2003

Citing 4
Cited 1

Machine learning, neural and statistical classification

Machine learning, neural and statistical classification
Data clustering: a review

ACM Computing Surveys (CSUR)
Mathematical Programming in Data Mining

Data Mining and Knowledge Discovery
A Neural Clustering Approach to Is0-Resource for Acute Healthcare in Australia

HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 6 - Volume 6

Data Mining with Combined Use of Optimization Techniques and Self-Organizing Maps for Improving Risk Grouping Rules: Application to Prostate Cancer Patients

Journal of Management Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on the nonsmooth optimization techniques is developed. The issues of applying this algorithm to large data sets are discussed and a feature selection procedure is demonstrated. The algorithm is then applied to a hospital data set to generate new knowledge about different patterns of patients resource consumption.