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.

翻译：阿片类药物是急性和慢性疼痛的有效镇痛药，但也存在显著的成瘾风险，导致美国每年出现数百万阿片类药物使用障碍（OUD）病例及数万例过早死亡。在处方前评估OUD风险有助于优化治疗方案、监测计划和干预策略的效能，但目前的风险评估通常依赖自我报告数据或问卷调查。我们设计了一种实验方案与计算方法，将OUD相关遗传变异与从GPS和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行为移动数据与遗传数据，我们开发了算法用于：(1) 从经验分布生成移动特征；(2) 在假设疾病共现预期水平的前提下合成移动与遗传样本。研究表明，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$F_1$分数评估，整合遗传与移动模态可改进风险建模。对拟合模型的解析表明，移动特征对OUD风险的影响更大，尽管遗传贡献（尤其在线性模型中）同样显著。尽管在临床实践前需通过临床试验评估隐私、安全、偏差及泛化性等问题，本研究为行为与遗传特征可能改善OUD风险评估以辅助个性化临床决策提供了初步证据。