Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, California, USA.
School of Medicine, University of California, Irvine, Irvine, California, USA.
J Am Med Inform Assoc. 2021 Mar 18;28(4):874-878. doi: 10.1093/jamia/ocaa324.
This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic.
The system presented is simulated with disease impact statistics from the Institute of Health Metrics, Centers for Disease Control and Prevention, and Census Bureau. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications.
The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93% to 95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74 ± 30.8% in simulations with 5 states to 93.50 ± 0.003% with 50 states.
These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.
本研究旨在探讨强化学习和深度学习模型如何促进医疗设备的近最优再分配，以增强应对类似 COVID-19 大流行的未来危机的公共卫生反应。
该系统使用疾病影响统计数据进行模拟，这些数据来自健康指标研究所、疾病控制与预防中心和人口普查局。我们提出了一种强大的数据预处理、未来需求推断和再分配算法的管道，可以在广泛的规模和应用中采用。
强化学习再分配算法的性能优化范围从 93%到 95%。随着参与交换的随机状态数量的增加，性能不断提高，在 5 个状态的模拟中，平均短缺减少 78.74±30.8%，在 50 个状态的模拟中，短缺减少 93.50±0.003%。
这些发现增强了对强化学习技术能够可靠地指导未来公共卫生紧急情况下资源分配的信心。
