BACKGROUND As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. OBJECTIVE We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). METHODS Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. RESULTS We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. CONCLUSIONS Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

中文翻译：

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创建用于患者呼吸机异步的鲁棒且通用的机器学习分类器

背景技术随着医疗保健日益数字化，可以从各种来源获得流波形数据，但是仍然缺乏有效的临床决策支持系统。例如，在重症监护室（ICU）中，现有的自动警报系统通常依赖于简单的阈值设置，从而导致频繁的误报。反复出现的误报警报会导致对可能直接损害患者健康的警报机制的不信任。为了改善ICU中的患者护理，我们需要既普及又准确的警报系统，以使提供者能够提供信息并受到其信任。目的我们旨在开发一种基于机器学习的分类器，以使用机械通风波形分析的用例来检测异常波形事件，以及检测对患者的有害通气形式。我们特别专注于检测患者-呼吸机异步（PVA）的有害亚型。方法使用从35个不同患者处记录的呼吸数据集，我们使用机器学习来创建计算模型，以自动检测并分类两种有害的PVA，即双触发异步（DTA），呼吸叠加异步（BSA）。我们研究了使用合成少数样本过采样技术（SMOTE）来克服类不平衡问题，使用多种方法进行特征选择以及使用集成方法来优化模型的性能。结果我们创建了一个整体分类器，能够准确地检测DTA的灵敏度/特异性为0.960 / 0.975，BSA的灵敏度/特异性为0.944 / 0.987，和非PVA事件的敏感度/特异性为.967 / .980。结论我们的结果表明，尽管在患者内部和患者之间波形模式存在变化，并且存在临床伪像，但仍可以创建一种基于机器学习的高性能模型来检测机械呼吸机波形数据中的PVA如咳嗽和吸痰程序。我们的工作强调了解决临床数据集中类别不平衡的重要性，以及在特征选择中结合使用统计方法和专家知识的重要性。以及是否存在临床伪影，例如咳嗽和吸痰程序。我们的工作强调了解决临床数据集中类别不平衡的重要性，以及在特征选择中结合使用统计方法和专家知识的重要性。以及是否存在临床伪影，例如咳嗽和吸痰程序。我们的工作强调了解决临床数据集中类别不平衡的重要性，以及在特征选择中结合使用统计方法和专家知识的重要性。