用神经网络和最优控制自动发现呼吸机管理策略。
Optimal Control of Mechanical Ventilators with Learned Respiratory Dynamics
- 基于马尔可夫决策过程建模呼吸机管理问题。
- 神经网络控制器在氧合和呼吸频率上表现优于临床指南。
- 无需依赖专家规则，可自主学习高效通气策略。
针对急性呼吸窘迫综合征（ARDS）患者，机械通气管理策略的选择显著影响治疗效果。本文将呼吸机管理建模为马尔可夫决策过程，比较了基于ARDSnet临床指南、最优控制理论以及基于神经网络学习的潜在动力学模型的控制器。采用脉搏生理引擎（Pulse Physiology Engine）的呼吸动力学仿真器进行可复现的基准测试，收集模拟数据并量化评估控制器性能。评分依据包括改善的呼吸率、氧合状态及生命体征等既定ARDS健康指标。结果表明，结合神经网络与最优控制的方法可在无须依赖显式管理规程（如ARDSnet协议）的情况下，自动发现有效的通气管理策略。
原文摘要 · Abstract (English)
Deciding on appropriate mechanical ventilator management strategies significantly impacts the health outcomes for patients with respiratory diseases. Acute Respiratory Distress Syndrome (ARDS) is one such disease that requires careful ventilator operation to be effectively treated. In this work, we frame the management of ventilators for patients with ARDS as a sequential decision making problem using the Markov decision process framework. We implement and compare controllers based on clinical guidelines contained in the ARDSnet protocol, optimal control theory, and learned latent dynamics represented as neural networks. The Pulse Physiology Engine's respiratory dynamics simulator is used to establish a repeatable benchmark, gather simulated data, and quantitatively compare these controllers. We score performance in terms of measured improvement in established ARDS health markers (pertaining to improved respiratory rate, oxygenation, and vital signs). Our results demonstrate that techniques leveraging neural networks and optimal control can automatically discover effective ventilation management strategies without access to explicit ventilator management procedures or guidelines (such as those defined in the ARDSnet protocol).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。