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).

翻译：决定适当的机械通气管理策略对呼吸系统疾病患者的健康结果具有重要影响。急性呼吸窘迫综合征（ARDS）就是这样一种需要谨慎操作呼吸机才能有效治疗的疾病。在本研究中，我们将ARDS患者的呼吸机管理问题构建为基于马尔可夫决策过程的序列决策问题。我们基于ARDSnet协议中的临床指南、最优控制理论以及以神经网络表示的学习隐式动力学，实现并比较了多种控制器。利用Pulse Physiology Engine的呼吸动力学模拟器建立可重复的基准测试环境，收集模拟数据，并对这些控制器进行定量比较。我们根据已建立的ARDS健康指标（涉及改善的呼吸频率、氧合状态及生命体征）的测量改进来评估性能。研究结果表明，结合神经网络与最优控制的技术能够自动发现有效的通气管理策略，而无需依赖明确的呼吸机管理规程或指南（例如ARDSnet协议中定义的那些）。