Title

OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks

UMMS Affiliation

Division of Medical Toxicology, Department of Emergency Medicine; School of Medicine

Publication Date

2021-09-14

Document Type

Conference Proceeding

Disciplines

Biomedical Devices and Instrumentation | Emergency Medicine | Substance Abuse and Addiction

Abstract

Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours ( approximately 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R (2) coefficient of 0.85.

Keywords

Channel and Temporal Attention, Depthwise convolutions, Opioid administration, Physiological signal, Temporal convolutional network

DOI of Published Version

10.1145/3478107

Source

Gullapalli BT, Carreiro S, Chapman BP, Ganesan D, Sjoquist J, Rahman T. OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2021 Sep;5(3):102. doi: 10.1145/3478107. Epub 2021 Sep 14. PMID: 35291374; PMCID: PMC8920039. Link to article on publisher's site

Journal/Book/Conference Title

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies

Related Resources

Link to Article in PubMed

PubMed ID

35291374

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