Although combination antiretroviral therapy (ART) with three or more drugs is highly effective in suppressing viral load for people with HIV (human immunodeficiency virus), many ART agents may exacerbate mental health-related adverse effects including depression. Therefore, understanding the effects of combination ART on mental health can help clinicians personalize medicine with less adverse effects to avoid undesirable health outcomes. The emergence of electronic health records offers researchers' unprecedented access to HIV data including individuals' mental health records, drug prescriptions, and clinical information over time. However, modeling such data is challenging due to high dimensionality of the drug combination space, the individual heterogeneity, and sparseness of the observed drug combinations. To address these challenges, we develop a Bayesian nonparametric approach to learn drug combination effect on mental health in people with HIV adjusting for sociodemographic, behavioral, and clinical factors. The proposed method is built upon the subset-tree kernel that represents drug combinations in a way that synthesizes known regimen structure into a single mathematical representation. It also utilizes a distance-dependent Chinese restaurant process to cluster heterogeneous populations while considering individuals' treatment histories. We evaluate the proposed approach through simulation studies, and apply the method to a dataset from the Women's Interagency HIV Study, showing the clinical utility of our model in guiding clinicians to prescribe informed and effective personalized treatment based on individuals' treatment histories and clinical characteristics.

中文翻译：

---

用于推断药物组合对艾滋病毒感染者心理健康影响的贝叶斯非参数方法


尽管联合抗逆转录病毒疗法 (ART) 使用三种或多种药物可以非常有效地抑制 HIV（人类免疫缺陷病毒）感染者的病毒载量，但许多 ART 药物可能会加剧与心理健康相关的不良反应，包括抑郁症。因此，了解联合抗逆转录病毒治疗对心理健康的影响可以帮助临床医生个性化用药，减少不良反应，避免不良的健康结果。电子健康记录的出现使研究人员能够前所未有地获取艾滋病毒数据，包括个人的心理健康记录、药物处方和一段时间内的临床信息。然而，由于药物组合空间的高维性、个体异质性以及观察到的药物组合的稀疏性，对此类数据进行建模具有挑战性。为了应对这些挑战，我们开发了一种贝叶斯非参数方法来了解药物组合对艾滋病毒感染者心理健康的影响，并根据社会人口统计、行为和临床因素进行调整。所提出的方法建立在子集树内核的基础上，该子集树内核以将已知的方案结构合成为单个数学表示的方式表示药物组合。它还利用依赖距离的中餐馆流程对异质人群进行聚类，同时考虑个人的治疗历史。我们通过模拟研究评估所提出的方法，并将该方法应用于女性机构间艾滋病毒研究的数据集，显示我们的模型在指导临床医生根据个人的治疗史和临床特征制定明智且有效的个性化治疗方面的临床效用。