The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm using closed-form coordinate ascent variational inference (CAVI) to determine the patient phenotype latent class. We then implement a variational Bayes logistic regression, where we determine the probability of the phenotype in the supplied EHR cohort, the shift in biomarkers for patients with the phenotype of interest versus a healthy population and evaluate predictive performance of binary indicator clinical codes and medication codes. The logistic model likelihood applies the latent class from the GMM step to inform the conditional.

翻译：VBphenoR软件包为R语言提供了一种基于电子健康记录（EHR）数据的闭式变分贝叶斯患者表型分析方法。我们采用闭式坐标上升变分推断（CAVI）实现变分贝叶斯高斯混合模型（GMM）算法，以确定患者表型的潜在类别。随后实施变分贝叶斯逻辑回归模型，通过该模型计算给定EHR队列中表型出现的概率，分析目标表型患者相较于健康人群的生物标志物偏移，并评估二元指示临床编码与药物编码的预测性能。该逻辑模型似然函数利用GMM步骤得到的潜在类别信息作为条件先验。