Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
Wenzhou City Bureau of Justice, Wenzhou, Zhejiang, 325000, China.
Comput Biol Med. 2023 Sep;163:107166. doi: 10.1016/j.compbiomed.2023.107166. Epub 2023 Jun 9.
Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.
近年来，随着医疗信息技术的发展和大数据的兴起，为优化医疗资源管理，提高医院门诊服务质量，减少患者等候时间，大中型综合医院纷纷采用人工智能大数据系统。然而，由于受物理环境、患者和医生行为等多方面因素的影响，实际的最佳治疗效果并不理想。为了促进有序就诊，本工作提出了一种考虑到患者流转移动态和客观规则的患者流预测模型来处理这个问题，并预测患者的医疗需求。首先，我们提出了一种高性能优化方法（SRXGWO），并将 Sobol 序列、Cauchy 随机替换策略和定向突变机制集成到灰狼优化（GWO）算法中。然后，使用 SRXGWO 优化支持向量回归（SVR）的参数，提出了患者流预测模型（SRXGWO-SVR）。在基准函数实验的消融和同行算法比较测试中，对 12 种高性能算法进行了检验，旨在验证 SRXGWO 的优化性能。为了在患者流预测试验中进行独立预测，将数据集分为训练集和测试集。结果表明，在预测精度和误差方面，SRXGWO-SVR 优于其他 7 种同行模型。因此，SRXGWO-SVR 有望成为一种可靠、高效的患者流预测系统，帮助医院尽可能有效地管理医疗资源。
