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Title: A Bayesian nonparametric approach for inferring drug combination effects on mental health in people with HIV Authors:  Yanxun Xu - Johns Hopkins University (United States) [presenting]
Abstract: Although combination antiretroviral therapy (ART) is highly effective in suppressing viral load for people with HIV (PWH), many ART agents may exacerbate adverse effects including depression. Therefore, understanding the effects of ART drugs on mental health can help clinicians personalize medicine with less adverse effects for PWH. 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 very challenging due to the high-dimensionality of the drug combination space, the individual heterogeneity, and the sparseness of the observed drug combinations. We develop a Bayesian nonparametric approach to learn drug combination effect on mental health in PWH adjusting for socio-demographic, behavioral, and clinical factors. The proposed method is built upon the subset-tree kernel method 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 taking into account individuals' treatment histories. We apply the method to a dataset from the Women's Interagency HIV Study, yielding interpretable and promising results.