Using wearable technology to detect prescription opioid self-administration

Abstract

Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.

Trial registration: ClinicalTrials.gov NCT03462797.

Figure 1.

Bagged-tree model confusion matrix and ROC curve with the opioid report cases (class 1) designated as the positive class, and the baseline cases (class 0) designated as the negative class. In the confusion matrix on the left, the green tiles contain the number and percentage of correctly predicted values for a given class, while the red tiles contain the number and percentage of incorrectly predicted values. The ROC curve on the right graphs the performance of the bagged-tree model as the model’s parameters are varied to maximize the true-positive rate (TPR), while minimizing the false-positive rate (FPR). The current model with its TPR and FPR is marked as an orange point. AUC, Area under the ROC curve; ROC, receiver operating characteristic.

Figure 2.

Average bagged-tree model prediction accuracies for each 5-minute window after the administration of an opioid in the test set (ie, the prediction accuracy for window number 1 represents the average prediction accuracy for all the of the first 5-minute windows after the opioid administrations for all subjects in the test set). The solid blue markers highlight windows 15 to 22 where most of the average prediction accuracies noticeably exceeded the bagged-tree model’s testing accuracy of 84% (represented by the red dashed line).