Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications.

中文翻译：

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使用非参数贝叶斯方法分离间质性肺疾病治疗的成本驱动因素

混合建模是一种流行的方法，可以适应医疗保健利用数据中非常常见的过度分散、偏斜和多模态特征。然而，混合建模往往依赖于关于混合成分的适当数量的主观判断或关于如何对数据进行聚类的一些假设。在这项工作中，我们采用非参数变分贝叶斯方法，允许模型在估计参数的同时选择组件的数量。我们的模型允许将观测结果概率分类为簇，并同时估计每个簇内的高斯回归模型。当我们将这种方法应用于间质性肺病患者的数据时，我们发现不同的患者亚组在医疗保健成本、健康和治疗协变量以及协变量与成本之间的关系方面存在差异。确定的子组很容易解释，这表明这种非参数变分推理方法可以发现驱动治疗成本的因素的有效见解。此外，我们采用的学习算法非常快速且可扩展，这应该使该技术可用于广泛的应用。