论文标题
使用贝叶斯网络模型选择方法的一致性类模型对患者共享网络的调查
Investigation of Patient-sharing Networks Using a Bayesian Network Model Selection Approach for Congruence Class Models
论文作者
论文摘要
介绍了一种用于进行网络模型选择的贝叶斯方法，该方法将针对称为一致性类模型（CCMS）的一般网络模型类别。 CCMS形成了一个广泛的类别，其中包括特殊情况，几种常见网络模型，例如ERDőS-Rényi-Gilbert模型，随机块模型和许多指数随机图模型。由于能够指定为CCM的模型范围，研究人员可以更好地选择与与当前方法相比，与观察到的网络相关的生成机制一致的模型。此外，该方法允许合并先验信息。我们利用提议的贝叶斯网络模型选择方法来调查可能导致患者共享网络结构的几种机制，这些机制与医疗服务的成本和质量有关。我们找到了支持社会性异质性的证据，而不是提供者类型和程度的选择性混合。
A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erdős-Rényi-Gilbert model, stochastic block model and many exponential random graph models. Due to the range of models able to be specified as a CCM, investigators are better able to select a model consistent with generative mechanisms associated with the observed network compared to current approaches. In addition, the approach allows for incorporation of prior information. We utilize the proposed Bayesian network model selection approach for CCMs to investigate several mechanisms that may be responsible for the structure of patient-sharing networks, which are associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type nor degree.
