Khorshidi Hadi A, Haffari Gholamreza, Aickelin Uwe, Hassani-Mahmooei Behrooz
School of Computing & Information Systems, The University of Melbourne, Australia.
Faculty of Information Technology, Monash University, Australia.
Stud Health Technol Inform. 2019 Aug 8;266:1-6. doi: 10.3233/SHTI190764.
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.
在早期阶段识别出那些会出现不良后果的患者群体，对于提供最合适的护理水平至关重要。在本研究中，我们打算找出交通事故受伤患者在受伤后第一周内医疗服务利用（HSU）的独特模式。针对那些与感兴趣的结果相关的模式。为了识别这些模式，我们提出了一个多目标优化模型，该模型同时最小化k - 中位数成本函数和回归误差。因此，我们使用半监督聚类方法，根据医疗服务利用模式及其与总成本的关联来识别患者群体。为了解决优化问题，我们引入了一种使用随机梯度下降和帕累托最优解的进化算法。结果，我们通过最小化两个目标函数找到了最佳的最优聚类。结果表明，所提出的半监督方法识别出了不同的医疗服务利用群体，并有助于预测总成本。此外，实验证明了多目标方法与单目标方法相比的性能。
