基于模型的患者-呼吸机波形合成数据集生成方法用于机器学习和教育用途
A Model-Based Approach to Synthetic Data Set Generation for Patient-Ventilator Waveforms for Machine Learning and Educational Use
摘要：
虽然机械通气是ICU中的救生干预，但它具有有害的副作用，例如气压伤和体积伤。这些伤害可能是由寄生虫引起的。不同步被定义为呼吸机定时和患者呼吸努力之间的不匹配。自动检测这些呼吸道疾病，并随后反馈，将改善肺通气和减少肺损伤的可能性。用于检测呼吸困难的神经网络提供了一种有前景的新方法，但需要大的注释数据集，这是难以获得的，并且需要复杂的吸气努力监测。在这项工作中，我们提出了一种基于模型的方法来生成一个合成数据集，用于机器学习和教育用途，通过扩展现有的肺模型与一阶呼吸机模型。衍生肺模型的生理性质允许适应各种疾病原型，从而产生多样化的数据集。我们使用9种不同的患者原型生成了一个合成数据集，这些原型来自文献中的测量。该模型和合成数据质量已通过与临床数据的比较、临床专家的审查以及在实验数据上训练的人工智能模型进行了验证。评价表明，可以生成患者-呼吸机波形，包括具有实验患者-呼吸机波形最重要特征的波形。
英文摘要：
Although mechanical ventilation is a lifesaving intervention in the ICU, it has harmful side-effects, such as barotrauma and volutrauma. These harms can occur due to asynchronies. Asynchronies are defined as a mismatch between the ventilator timing and patient respiratory effort. Automatic detection of these asynchronies, and subsequent feedback, would improve lung ventilation and reduce the probability of lung damage. Neural networks to detect asynchronies provide a promising new approach but require large annotated data sets, which are difficult to obtain and require complex monitoring of inspiratory effort. In this work, we propose a model-based approach to generate a synthetic data set for machine learning and educational use by extending an existing lung model with a first-order ventilator model. The physiological nature of the derived lung model allows adaptation to various disease archetypes, resulting in a diverse data set. We generated a synthetic data set using 9 different patient archetypes, which are derived from measurements in the literature. The model and synthetic data quality have been verified by comparison with clinical data, review by a clinical expert, and an artificial intelligence model that was trained on experimental data. The evaluation showed it was possible to generate patient-ventilator waveforms including asynchronies that have the most important features of experimental patient-ventilator waveforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
