ArticleInternational Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications2017
A New Mining Method to Detect Real Time Substance Use Events from Wearable Biosensor Data Stream.
Article in International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 24 citations in OpenAlex.
- Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing.AI (Basel, Switzerland) · 2024Article
- Willingness to use a wearable device capable of detecting and reversing overdose among people who use opioids in Philadelphia.Harm reduction journal · 2021Article
- Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology.Sensors (Basel, Switzerland) · 2021Article
- Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review.JMIR mHealth and uHealth · 2020Article
- Harmonizing Wearable Biosensor Data Streams to Test Polysubstance Detection.International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications · 2020Article
- Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk.Sensors (Basel, Switzerland) · 2018Article
- Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2018Review
- Automatic Detection of Opioid Intake Using Wearable Biosensor.International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications · 2018Article
- Secondary Analysis to Advance Characterization of On-body Electrocardiographic Sensors in a Clinical Cocaine Self-administration Paradigm.Journal of addiction medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Detecting real time substance use is a critical step for optimizing behavioral interventions to prevent drug abuse. Traditional methods based on self-reporting or urine screening are inefficient or intrusive for drug use detection, and inappropriate for timely interventions. For example, self-report suffers from distortion or recall bias; while urine screening often detects drug use that occurred only within the previous 72 hours. Methods for real-time substance use detection are severely underdeveloped, partly due to the novelty of wearable biosensor technique and the lack of substantive clinical data for evaluation. We propose a new real-time drug use event detection method using data obtained from wearable biosensors. Specifically, this method is built upon the slide window technique to process the data stream, and a distance-based outlier detection method to identify substance use events. This novel method is designed to examine how to detect and set up the thresholds of parameters in real-time drug use event detection for wearable biosensor data streams. Our numerical analyses empirically identified the thresholds of parameters used to detect the cocaine use and showed that this proposed method could be adapted to detect other substance use events.
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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.