Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube. The management of weaning from mechanical ventilation comprises a significant proportion of the care of critically ill intubated patients in Intensive Care Units (ICUs). Both prolonged dependence on mechanical ventilation and premature extubation expose patients to an increased risk of complications and increased health care costs. This work aims to develop a decision support model using routinely-recorded patient information to predict extubation readiness. In order to do so, we have deployed Convolutional Neural Networks (CNN) to predict the most appropriate treatment action in the next hour for a given patient state, using historical ICU data extracted from MIMIC-III. The model achieved 86% accuracy and 0.94 area under the receiver operating characteristic curve (AUC-ROC). We also performed feature importance analysis for the CNN model and interpreted these features using the DeepLIFT method. The results of the feature importance assessment show that the CNN model makes predictions using clinically meaningful and appropriate features. Finally, we implemented counterfactual explanations for the CNN model. This can help clinicians understand what feature changes for a particular patient would lead to a desirable outcome, i.e. readiness to extubate.

退出机械通气包括将患者从机械支持中解放出来并移除相关的气管插管的过程。在重症监护病房 (ICU) 中，机械通气撤机管理占插管危重患者护理的很大一部分。长期依赖机械通气和过早拔管都会使患者面临更高的并发症风险和医疗保健费用。这项工作旨在开发一个决策支持模型，使用常规记录的患者信息来预测拔管准备情况。为此，我们部署了卷积神经网络 (CNN)，使用从 MIMIC-III 中提取的历史 ICU 数据，针对给定的患者状态预测下一小时内最合适的治疗措施。该模型实现了 86% 的准确率和 0.94 的受试者工作特征曲线下面积 (AUC-ROC)。我们还对 CNN 模型进行了特征重要性分析，并使用 DeepLIFT 方法解释了这些特征。特征重要性评估的结果表明，CNN 模型使用具有临床意义和适当的特征进行预测。最后，我们对 CNN 模型实施了反事实解释。这可以帮助临床医生了解特定患者的哪些特征变化会导致理想的结果，即准备拔管。特征重要性评估的结果表明，CNN 模型使用具有临床意义和适当的特征进行预测。最后，我们对 CNN 模型实施了反事实解释。这可以帮助临床医生了解特定患者的哪些特征变化会导致理想的结果，即准备拔管。特征重要性评估的结果表明，CNN 模型使用具有临床意义和适当的特征进行预测。最后，我们对 CNN 模型实施了反事实解释。这可以帮助临床医生了解特定患者的哪些特征变化会导致理想的结果，即准备拔管。