Kulman Ethan, Chapman Brittany, Venkatasubramanian Krishna, Carreiro Stephanie
University of Rhode Island.
University of Massachusetts Medical School.
Proc Annu Hawaii Int Conf Syst Sci. 2021 Jan;54:3583-3592.
Wearable biosensors can be used to monitor opioid use, a problem of dire societal consequence given the current opioid epidemic in the US. Such surveillance can prompt interventions that promote behavioral change. Prior work has focused on the use of wearable biosensor data to detect opioid use. In this work, we present a method that uses machine learning to identify opioid withdrawal using data collected with a wearable biosensor. Our method involves developing a set of machine-learning classifiers, and then evaluating those classifiers using unseen test data. An analysis of the best performing model (based on the Random Forest algorithm) produced a receiver operating characteristic (ROC) area under the curve (AUC) of 0.9997 using completely unseen test data. Further, the model is able to detect withdrawal with just one minute of biosensor data. These results show the viability of using machine learning for opioid withdrawal detection. To our knowledge, the proposed method for identifying opioid withdrawal in OUD patients is the first of its kind.
可穿戴生物传感器可用于监测阿片类药物的使用情况，鉴于美国当前的阿片类药物泛滥，这是一个具有严重社会后果的问题。这种监测可以促使采取干预措施，以促进行为改变。先前的工作主要集中在利用可穿戴生物传感器数据来检测阿片类药物的使用。在这项工作中，我们提出了一种方法，该方法使用机器学习，通过可穿戴生物传感器收集的数据来识别阿片类药物戒断症状。我们的方法包括开发一组机器学习分类器，然后使用未见过的测试数据对这些分类器进行评估。对表现最佳的模型（基于随机森林算法）进行分析，使用完全未见过的测试数据得出曲线下面积（AUC）为0.9997的受试者工作特征（ROC）曲线。此外，该模型仅用一分钟的生物传感器数据就能检测出戒断症状。这些结果表明了使用机器学习进行阿片类药物戒断检测的可行性。据我们所知，所提出的用于识别阿片类药物使用障碍（OUD）患者戒断症状的方法尚属首次。
