Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
Insight Research, Research and development, Emerald Hills, CA 94065, USA.
Mil Med. 2021 Jan 25;186(Suppl 1):440-444. doi: 10.1093/milmed/usaa323.
The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension.
Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized.
The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals.
In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.
尽早准确地检测创伤患者的低血压对于改善创伤结局非常重要。检测的时间越早，采取纠正措施的时间就越多。目前，关于结合多种生理信号进行早期失血性休克检测的研究有限。我们研究了基于多生理信号和机器学习方法对低血压进行早期检测的可行性。我们探索了使用小（5 分钟）预测窗口将机器学习工具和多种生理信号应用于检测低血压的概念验证。
使用来自先前通过实验性出血模型生成的现有数据集的多变量生理信号。这些实验是由另一个研究小组进行的，并通过一个网络门户公开提供数据。该数据集是少数几个公开的数据集之一，其中包含在实验性出血期间对大型动物的多种生理信号进行测量。该数据包括涉及 8 只羊的两项出血研究。进行了监督机器学习实验，以便为识别生理信号开发深度学习（即长短期记忆或 LSTM）、集成学习（即随机森林）和经典学习（即支持向量机或 SVM）模型，以便能够在 5 分钟前识别是否整体失血量超过预设阈值。为了评估机器学习技术的性能，进行了 3 折交叉验证，并比较了精度（也称为阳性预测值）和召回率（也称为灵敏度）值。作为该开发过程的第一步，使用了 5 分钟的预测窗口。
结果表明，SVM 和随机森林优于 LSTM 神经网络，这可能是因为 LSTM 往往会在小型数据集上过度拟合数据。随机森林的召回率最高（84%），精度为 56%，而 SVM 的召回率为 62%，精度为 82%。在分析特征重要性时，观察到心电图的重要性最高，而动脉血压在所有其他信号中重要性最低。
在这项研究中，我们探索了基于现有动物出血数据集的多信号对低血压进行早期检测的可行性。结果表明，对于这种检测任务，多变量方法可能比单变量方法更有效。
