A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

Autor:	Pan, Yihan, Xu, Zhenghang, Guang, Jin, Sun, Jingjing, Wang, Chengwenjian, Zhang, Xuanming, Chen, Xinyun, Dai, J. G., Ding, Yichuan, Shi, Pengyi, Pan, Hongxin, Yang, Kai, Wu, Song
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: 21 pages, 15 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2104.07451 Zobrazit plný text záznamu View this record from Arxiv