Pang Xinyu, Ge Yong-Feng, Wang Kate, Traina Agma J M, Wang Hua
Guangdong Technion Israel Institute of Technology, Shantou, China.
Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia.
Health Inf Sci Syst. 2023 Jun 29;11(1):30. doi: 10.1007/s13755-023-00230-1. eCollection 2023 Dec.
Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.
将互联网技术与传统医疗系统相结合催生了云医疗系统。这些系统旨在优化在线诊断与线下治疗之间的平衡，以有效减少患者等待时间并提高闲置医疗资源的利用率。本文提出了一种分布式遗传算法（DGA），作为优化云医疗系统中患者分配（PA）平衡的一种手段。所提出的DGA将个体用作PA优化问题的解决方案，并通过执行交叉、变异和选择算子来生成更好的解决方案。此外，还提出了DGA中的分布式框架以提高其种群多样性和可扩展性。实验结果证明了所提出的DGA在优化云医疗系统内的PA问题方面的有效性。
