基于多元Beta混合模型的比例数据显著贝叶斯聚类

中文摘要 AI 辅助

社区是多层面的实体，其属性涵盖社会文化、经济和环境维度。基于健康社会决定因素（SDoH）对社区进行聚类，可以为公共卫生资源的分配和针对社区需求的干预措施提供信息。连续且有界的数据（如聚合的社区级SDoH数据）可能对广泛使用的聚类方法（如k-means或高斯混合模型）构成挑战，这些方法依赖于欧氏距离或正态性假设。此外，并非所有SDoH变量都对有意义地表征社区聚类有同等贡献，包含不相关或弱信息性的决定因素可能会掩盖潜在结构。为解决此问题，我们提出了一种贝叶斯显著多元Beta混合（SAMBA-Mix）模型，该模型灵活地对比例数据建模，并嵌入特征显著性以识别用于聚类的相关变量。与现有模型相比，我们的模型在聚类识别和结构方面表现出更高的准确性。利用美国医疗保健研究与质量局（AHRQ）SDoH数据库的数据，我们展示了该模型在马萨诸塞州推导社区级SDoH概况以及识别最能表征不同概况之间差异的关键决定因素方面的实用性。

英文摘要

Neighborhoods are multifaceted entities whose attributes span sociocultural, economic, and environmental dimensions. Clustering neighborhoods based on social determinants of health (SDoH) can inform the allocation of public health resources and interventions tailored to community needs. Continuous and bounded data, as seen in aggregated neighborhood-level SDoH data, can pose challenges for widely used clustering methods (e.g., k-means or Gaussian mixture models) that rely on Euclidean distances or normality assumptions. Moreover, not all SDoH variables contribute equally to meaningfully characterize neighborhood clusters, and the inclusion of irrelevant or weakly informative determinants can obscure the underlying structure. To address this issue, we propose a Bayesian Salient Multivariate Beta mixture (SAMBA-Mix) model that flexibly models proportional data with embedded feature saliency to identify relevant variables for clustering. Our model showed improved accuracy in cluster identification and structure, compared to existing models. Using data from the U.S. Agency for Healthcare Research and Quality(AHRQ) SDoH database, we demonstrate the utility of our model to derive neighborhood-level SDoH profiles in Massachusetts and identify key determinants that best characterize differences amongst the different profiles.

发表机构

• Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）

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