  Opioids are an effective analgesic for acute and chronic pain, but also carry
a considerable risk of addiction leading to millions of opioid use disorder
(OUD) cases and tens of thousands of premature deaths in the United States
yearly. Estimating OUD risk prior to prescription could improve the efficacy of
treatment regimens, monitoring programs, and intervention strategies, but risk
estimation is typically based on self-reported data or questionnaires. We
develop an experimental design and computational methods that combine genetic
variants associated with OUD with behavioral features extracted from GPS and
Wi-Fi spatiotemporal coordinates to assess OUD risk. Since both OUD mobility
and genetic data do not exist for the same cohort, we develop algorithms to (1)
generate mobility features from empirical distributions and (2) synthesize
mobility and genetic samples assuming an expected level of disease
co-occurrence. We show that integrating genetic and mobility modalities
improves risk modelling using classification accuracy, area under the
precision-recall and receiver operator characteristic curves, and $F_1$ score.
Interpreting the fitted models suggests that mobility features have more
influence on OUD risk, although the genetic contribution was significant,
particularly in linear models. While there exist concerns with respect to
privacy, security, bias, and generalizability that must be evaluated in
clinical trials before being implemented in practice, our framework provides
preliminary evidence that behavioral and genetic features may improve OUD risk
estimation to assist with personalized clinical decision-making.
