一种用于医院超声手术的高保真、机器学习增强型神经网络仿真模型
A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations
摘要：
我们与中国深圳的一家大型教学医院合作，为其超声中心构建了一个高保真仿真模型，以高精度预测关键性能指标，包括队列长度、等待时间和逗留时间的分布。建立准确的仿真模型的关键挑战是了解超声中心复杂的患者路由。为了解决这个问题，我们提出了一种新的两级路由组件的路由网络模型。我们应用机器学习工具从数据中校准建模的关键组件，提高了准确性。
英文摘要：
We collaborate with a large teaching hospital in Shenzhen, China and build a high-fidelity simulation model for its ultrasound center to predict key performance metrics, including the distributions of queue length, waiting time and sojourn time, with high accuracy. The key challenge to build an accurate simulation model is to understanding the complicated patient routing at the ultrasound center. To address the issue, we propose a novel two-level routing component to the queueing network model. We apply machine learning tools to calibrate the key components of the queueing model from data with enhanced accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
