Lab of Computing Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Physiol Meas. 2019 Oct 14;40(9):095006. doi: 10.1088/1361-6579/ab4119.
Alarms are a substantial part of clinical practice, warning clinicians of patient complications. In this paper, we focus on alarms in the intensive care unit and especially on the use of machine learning techniques for the creation of alarms for the ventilator support of patients. The aim is to study a method to enable timely interventions for intubated patients and prevent complications induced by high driving pressure (ΔP) and lung strain during mechanical ventilation.
The relation between the ΔP and the total set of the ventilator parameters was examined and resulted in a predictive model with bimodal implementation for the short-term prediction of the ΔP level (high/low). The proposed method includes two sub-models for the prediction of future ΔP level based on the current level being high or low, named cH and cL, respectively. Based on this method, for both sub-models, an alarm will be triggered when the predicted ΔP level is considered to be high. In this vein, three classifiers (the random forest, linear support vector machine, and kernel support vector machine methods) were tested for each sub-model. To adjust the highly unbalanced classes, four different sampling methods were considered: downsampling, upsampling, synthetic minority over-sampling technique (SMOTE) sampling, and random over-sampling examples (ROSE) sampling.
For the cL sub-model the combination of linear support vector machine with SMOTE sampling showed the best performance, resulting in accuracy of 93%, while the cH sub-model reached the best performance, with accuracy of 73%, with kernel support vector machine combined with the downsampling method.
The results are positive in terms of the generation of new alarms in mechanical ventilation. The technical and organizational possibility of integrating data from multiple modalities is expected to further advance this line of work.
报警是临床实践的重要组成部分，可提醒临床医生患者的并发症。本文重点研究重症监护病房的报警问题，尤其是使用机器学习技术为患者呼吸机支持创建报警。目的是研究一种方法，以便为插管患者及时进行干预，防止机械通气时高驱动压（ΔP）和肺应变引起的并发症。
检查了ΔP 与呼吸机参数总集之间的关系，得到了一个具有双峰实现的预测模型，用于短期预测ΔP 水平（高/低）。所提出的方法包括基于当前水平高或低来预测未来ΔP 水平的两个子模型，分别命名为 cH 和 cL。基于该方法，对于这两个子模型，当预测的ΔP 水平被认为较高时，将触发报警。在此基础上，对每个子模型分别测试了三种分类器（随机森林、线性支持向量机和核支持向量机方法）。为了调整高度不平衡的类别，考虑了四种不同的采样方法：下采样、上采样、合成少数过采样技术（SMOTE）采样和随机过采样实例（ROSE）采样。
对于 cL 子模型，线性支持向量机与 SMOTE 采样的组合表现最佳，准确率为 93%，而 cH 子模型的准确率为 73%，使用核支持向量机结合下采样方法。
就机械通气中产生新报警而言，结果是积极的。预计整合多种模式数据的技术和组织可能性将进一步推进这一工作。
