Getoor Lise, Rhee Jeanne T, Koller Daphne, Small Peter
Computer Science Deptartment and UMIACS, University of Maryland, College Park, MD 20742, USA.
Artif Intell Med. 2004 Mar;30(3):233-56. doi: 10.1016/j.artmed.2003.11.003.
Molecular epidemiological studies can provide novel insights into the transmission of infectious diseases such as tuberculosis. Typically, risk factors for transmission are identified using traditional hypothesis-driven statistical methods such as logistic regression. However, limitations become apparent in these approaches as the scope of these studies expand to include additional epidemiological and bacterial genomic data. Here we examine the use of Bayesian models to analyze tuberculosis epidemiology. We begin by exploring the use of Bayesian networks (BNs) to identify the distribution of tuberculosis patient attributes (including demographic and clinical attributes). Using existing algorithms for constructing BNs from observational data, we learned a BN from data about tuberculosis patients collected in San Francisco from 1991 to 1999. We verified that the resulting probabilistic models did in fact capture known statistical relationships. Next, we examine the use of newly introduced methods for representing and automatically constructing probabilistic models in structured domains. We use statistical relational models (SRMs) to model distributions over relational domains. SRMs are ideally suited to richly structured epidemiological data. We use a data-driven method to construct a statistical relational model directly from data stored in a relational database. The resulting model reveals the relationships between variables in the data and describes their distribution. We applied this procedure to the data on tuberculosis patients in San Francisco from 1991 to 1999, their Mycobacterium tuberculosis strains, and data on contact investigations. The resulting statistical relational model corroborated previously reported findings and revealed several novel associations. These models illustrate the potential for this approach to reveal relationships within richly structured data that may not be apparent using conventional statistical approaches. We show that Bayesian methods, in particular statistical relational models, are an important tool for understanding infectious disease epidemiology.
分子流行病学研究能够为结核病等传染病的传播提供全新见解。通常，使用传统的假设驱动统计方法（如逻辑回归）来确定传播的风险因素。然而，随着这些研究的范围扩大到纳入更多的流行病学和细菌基因组数据，这些方法的局限性变得明显。在此，我们研究使用贝叶斯模型来分析结核病流行病学。我们首先探索使用贝叶斯网络（BNs）来确定结核病患者属性（包括人口统计学和临床属性）的分布。利用从观测数据构建BNs的现有算法，我们从1991年至1999年在旧金山收集的结核病患者数据中学习了一个BN。我们验证了所得的概率模型实际上确实捕捉到了已知的统计关系。接下来，我们研究在结构化领域中表示和自动构建概率模型的新引入方法的使用。我们使用统计关系模型（SRMs）对关系域上的分布进行建模。SRMs非常适合结构丰富的流行病学数据。我们使用一种数据驱动的方法直接从存储在关系数据库中的数据构建统计关系模型。所得模型揭示了数据中变量之间的关系并描述了它们的分布。我们将此过程应用于1991年至1999年旧金山结核病患者的数据、他们的结核分枝杆菌菌株以及接触调查数据。所得的统计关系模型证实了先前报道的发现，并揭示了几个新的关联。这些模型说明了这种方法揭示结构丰富的数据中使用传统统计方法可能不明显的关系的潜力。我们表明，贝叶斯方法，特别是统计关系模型，是理解传染病流行病学的重要工具。
