Using machine learning for real-time BAC estimation from a new-generation transdermal biosensor in the laboratory

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Transdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Older-generation transdermal devices feature bulky designs and sparse sampling intervals, limiting potential applications for transdermal technology. Recently a new-generation of transdermal device has become available, featuring smartphone connectivity, compact designs, and rapid sampling. Here we present initial laboratory research examining the validity of a new-generation transdermal sensor prototype. Methods: Participants were young drinkers administered alcohol (target BAC = .08 %) or no-alcohol in the laboratory. Participants wore transdermal sensors while providing repeated breathalyzer (BrAC) readings. We assessed the association between BrAC (measured BrAC for a specific time point) and eBrAC (BrAC estimated based only on transdermal readings collected in the immediately preceding time interval). Extra-Trees machine learning algorithms, incorporating transdermal time series features as predictors, were used to create eBrAC. Results: Failure rates for the new-generation prototype sensor were high (16 %–34 %). Among participants with useable new-generation sensor data, models demonstrated strong capabilities for separating drinking from non-drinking episodes, and significant (moderate) ability to differentiate BrAC levels within intoxicated participants. Differences between eBrAC and BrAC were 60 % higher for models based on data from old-generation vs new-generation devices. Model comparisons indicated that both time series analysis and machine learning contributed significantly to final model accuracy. Conclusions: Results provide favorable preliminary evidence for the accuracy of real-time BAC estimates from a new-generation sensor. Future research featuring variable alcohol doses and real-world contexts will be required to further validate these devices.

Original languageEnglish (US)
Article number108205
JournalDrug and Alcohol Dependence
Volume216
DOIs
StatePublished - Nov 1 2020

Keywords

  • Alcohol
  • Biosensor
  • Blood alcohol concentration
  • Machine learning
  • Real-time
  • Transdermal

ASJC Scopus subject areas

  • Toxicology
  • Pharmacology
  • Psychiatry and Mental health
  • Pharmacology (medical)

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