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 combines 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 a level of comorbidity and relative risks. 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 exists 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.

翻译：阿片类药物是治疗急性和慢性疼痛的有效镇痛药，但也存在显著的成瘾风险，每年导致美国发生数百万例阿片类药物使用障碍（OUD）病例及数万例过早死亡。在开具处方前评估OUD风险可提升治疗方案、监测计划和干预策略的有效性，但传统风险评估通常依赖自我报告数据或问卷调查。我们开发了一种实验设计和计算方法，将OUD相关遗传变异与从GPS及Wi-Fi时空坐标中提取的行为特征相结合，以评估OUD风险。由于同一队列中缺乏同时存在的OUD移动性与基因数据，我们设计了两种算法：（1）基于经验分布生成移动性特征；（2）在假设一定共病率与相对风险水平的前提下，合成移动性与遗传样本。我们证明，整合遗传与移动性模态通过分类准确率、精确率-召回率曲线下面积、受试者工作特征曲线下面积以及$F_1$得分改进了风险建模效果。对拟合模型的解释表明，移动性特征对OUD风险的影响更大，但遗传贡献（尤其在线性模型中）仍具有显著意义。尽管在临床实践中实施前需通过临床试验评估隐私、安全、偏见及泛化性等问题，但我们的框架为行为与遗传特征可提升OUD风险评估以辅助个性化临床决策提供了初步证据。