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Authors: Rohitpal Singh 1 ; Brittany Lewis 1 ; Brittany Chapman 2 ; Stephanie Carreiro 2 and Krishna Venkatasubramanian 1

Affiliations: 1 Worcester Polytechnic Institute, Worcester, MA and U.S.A. ; 2 University of Massachusetts Medical School, Worcester, MA and U.S.A.

ISBN: 978-989-758-353-7

Keyword(s): Opioid Epidemic, Wearable Technology, Biosensor, Adherence, Machine Learning.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Biomedical Signal Processing ; Devices ; Health Information Systems ; Human-Computer Interaction ; Pattern Recognition and Machine Learning ; Pervasive Health Systems and Services ; Physiological Computing Systems ; Wearable Sensors and Systems

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. (More)

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Paper citation in several formats:
Singh, R.; Lewis, B.; Chapman, B.; Carreiro, S. and Venkatasubramanian, K. (2019). A Machine Learning-based Approach for Collaborative Non-Adherence Detection during Opioid Abuse Surveillance using a Wearable Biosensor.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, ISBN 978-989-758-353-7, pages 310-318. DOI: 10.5220/0007382503100318

@conference{healthinf19,
author={Rohitpal Singh. and Brittany Lewis. and Brittany Chapman. and Stephanie Carreiro. and Krishna Venkatasubramanian.},
title={A Machine Learning-based Approach for Collaborative Non-Adherence Detection during Opioid Abuse Surveillance using a Wearable Biosensor},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,},
year={2019},
pages={310-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007382503100318},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,
TI - A Machine Learning-based Approach for Collaborative Non-Adherence Detection during Opioid Abuse Surveillance using a Wearable Biosensor
SN - 978-989-758-353-7
AU - Singh, R.
AU - Lewis, B.
AU - Chapman, B.
AU - Carreiro, S.
AU - Venkatasubramanian, K.
PY - 2019
SP - 310
EP - 318
DO - 10.5220/0007382503100318

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