Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to acute respiratory distress syndrome, longer duration of mechanical ventilation, ventilator associated conditions and finally increased mortality.

In this study, we explore the viability and compare machine learning methods to generate personalized predictive alerts indicating violation of the safe tidal volume per ideal body weight (IBW) threshold that is accepted as the upper limit for lung protective ventilation (LPV), prior to application to patients. We process streams of patient respiratory data recorded per minute from ventilators in an intensive care unit and apply several state-of-the-art time series prediction methods to forecast the behavior of the tidal volume metric per patient, 1 hour ahead.

Our results show that boosted regression delivers better predictive accuracy than other methods that we investigated and requires relatively short execution times. Long short-term memory neural networks can deliver similar levels of accuracy but only after much longer periods of data acquisition, further extended by several hours computing time to train the algorithm. Utilizing Artificial Intelligence, we have developed a personalized clinical decision support tool that can predict tidal volume behavior within $10\text{\%}$ accuracy and compare alerts recorded from a real world system to highlight that our models would have predicted violations 1 hour ahead and can therefore conclude that the algorithms can provide clinical decision support.

机械通气是一种救生工具，为呼吸衰竭患者提供器官支持。然而，由于高潮气量输送不当导致的有害通气可能引发或加剧肺损伤。这可能导致急性呼吸窘迫综合征、机械通气持续时间延长、呼吸机相关疾病，并最终增加死亡率。

在这项研究中，我们探索了可行性并比较了机器学习方法，以生成个性化的预测警报，表明违反了每理想体重 (IBW) 阈值的安全潮气量，该阈值被接受为肺保护性通气 (LPV) 的上限。对患者的应用。我们处理重症监护室呼吸机每分钟记录的患者呼吸数据流，并应用几种最先进的时间序列预测方法来预测每个患者提前 1 小时的潮气量指标的行为。

我们的结果表明，增强回归比我们研究的其他方法具有更好的预测准确性，并且需要相对较短的执行时间。长短期记忆神经网络可以提供类似的准确性水平，但前提是需要更长的数据采集时间，并进一步延长几个小时的计算时间来训练算法。利用人工智能，我们开发了一种个性化的临床决策支持工具，可以预测潮气量行为$\mathrm{<span\; data-immersive-translate-walked="3f548b49-3eec-47d2-b0d8-cd492fee4dfa">10</span>}\text{<span data-immersive-translate-walked="3f548b49-3eec-47d2-b0d8-cd492fee4dfa">\%</span>}$准确性并比较从现实世界系统记录的警报，以强调我们的模型将提前 1 小时预测违规行为，因此可以得出结论，算法可以提供临床决策支持。