ArticleProceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences2021
Identifying Opioid Withdrawal Using Wearable Biosensors.
Article in Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Leveraging Trauma Informed Care for Digital Health Intervention Development in Opioid Use Disorder.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2025Review
- Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review.JMIR mHealth and uHealth · 2024Article
- Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder.Cogent mental health · 2023Article
- Leveraging Accelerometry as a Prognostic Indicator for Increase in Opioid Withdrawal Symptoms.Biosensors · 2022Article
- New and Emerging Opioid Overdose Risk Factors.Current addiction reports · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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. 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.
Identifiers
33568965PMC7871978What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.