Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems.

This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

机械通气 (MV) 是呼吸衰竭或急性呼吸窘迫综合征 (ARDS) 患者的核心生命支持疗法。呼吸衰竭是一系列伤害和疾病的次要结果，导致几乎一半的重症监护病房 (ICU) 患者接受某种形式的 MV。为 ICU 不断增长的需求提供资金是一个主要问题，尤其是 MV，由于患者的显着差异、过度镇静以及患者管理所需的大量临床时间，每天的成本可能会翻倍。降低该领域的成本既需要通过改善护理来减少 MV 的平均持续时间，也需要减少临床工作量。

本文介绍了常见的肺部模型，并提供了一个更加自动化的未来愿景，并探索了一些当前模型的预测能力。这一愿景包括使用基于模型的方法来实时了解患者状况，通过前瞻性预测 MV 变化的结果来提高安全性，以及开发虚拟患者用于临床方案的计算机设计和测试。最后，将考虑使用动态系统模型和系统识别来指导治疗，以改进 ICU 中氧合和 MV 治疗的个性化控制。这些方法是未来医学的重要组成部分，其中包括更好的个性化和预测能力，以优化护理和降低成本。