Short: Precision Polysubstance Use Episode Detection in Wearable Biosensor Data Streams

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Abstract
Wearable biosensors create the opportunity for continuous health monitoring by generating streams of measurements that reflect users' physiological conditions in natural environments. Continuous health monitoring is a key enabler of precision health, a means to detect individual-level changes early and initiate personalized preventive measures or other clinical interventions. Although the amount of data generated is theoretically unbounded, precise labeling is rare outside of controlled clinical environments. Using data streams from a study of patients recovering from cocaine use disorder, we demonstrate early results of a novel method to detect polysubstance use without precisely labeled training data using an anomaly detection paradigm. RP-STREAM performed better than an alternative in detecting polysubstance use in wearable biosensor data streams. The proposed semi-supervised learning model makes efficient use of training data and computational resources while also automating parameter selection. We also identify the effects of THC and cocaine polysubstance use in wearable biosensor data.
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Funding Information
  • NSF (National Science Foundation) (NSF 2140729)

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