Citation

Khorshidi HA, Haffari G, Aickelin U, Hassani-Mahmooei B. Stud. Health Technol. Inform. 2019; 266: 1-6.

Affiliation

Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia.

Copyright

(Copyright © 2019, IOS Press)

DOI

10.3233/SHTI190764

PMID

31397293

Abstract

Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use (HSU) of transport accident injured patients within the first week post-injury. Aiming those patterns that are associated with the outcome of interest. To recognize these patterns, we propose a multi-objective optimization model that minimizes the k-medians cost function and regression error simultaneously. Thus, we use a semi-supervised clustering approach to identify patient groups based on HSU patterns and their association with total cost. To solve the optimization problem, we introduce an evolutionary algorithm using stochastic gradient descent and Pareto optimal solutions. As a result, we find the best optimal clusters by minimizing both objective functions. The results show that the proposed semi-supervised approach identifies distinct groups of HSUs and contributes to predict total cost. Also, the experiments prove the performance of the multi-objective approach in comparison with single- objective approaches.

Language: en

Keywords

Health service patterns; Injured patients; Injury outcomes; Multi-objective optimization.; Semi-supervised clustering