Title

A Machine Learning-based Approach for Collaborative Non-Adherence Detection during Opioid Abuse Surveillance using a Wearable Biosensor

UMMS Affiliation

Department of Emergency Medicine

Publication Date

2019-02-01

Document Type

Conference Proceeding

Disciplines

Artificial Intelligence and Robotics | Biomedical | Biomedical Devices and Instrumentation | Biomedical Engineering and Bioengineering | Biotechnology | Medical Toxicology | Substance Abuse and Addiction | Translational Medical Research

Abstract

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. The effectiveness of biosensor-based monitoring is threatened by the potential of a patient's collaborative non-adherence (CNA) to the monitoring. We define CNA as the process of giving one's biosensor to someone else when surveillance is ongoing. The principal aim of this paper is to leverage accelerometer and blood volume pulse (BVP) measurements from a wearable biosensor and use machine-learning for the novel problem of CNA detection in opioid surveillance. We use accelerometer and BVP data collected from 11 patients who were brought to a hospital Emergency Department while undergoing naloxone treatment following an opioid overdose. We then used the data collected to build a personalized classifier for individual patients that capture the uniqueness of their blood volume pulse and triaxial accelerometer readings. In order to evaluate our detection approach, we simulate the presence (and absence) of CNA by replacing (or not replacing) snippets of the biosensor readings of one patient with another. Overall, we achieved an average detection accuracy of 90.96% when the collaborator was one of the other 10 patients in our dataset, and 86.78% when the collaborator was from a set of 14 users whose data had never been seen by our classifiers before.

Keywords

Adherence, Biosensor, Machine Learning, Opioid Epidemic, Wearable Technology, UMCCTS funding

DOI of Published Version

10.5220/0007382503100318

Source

Biomed Eng Syst Technol Int Jt Conf BIOSTEC Revis Sel Pap. 2019 Feb;5:310-318. doi: 10.5220/0007382503100318. Link to article on publisher's site

Journal/Book/Conference Title

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)

Related Resources

Link to Article in PubMed

PubMed ID

30993266

Share

COinS