STRUCTURING MEDICAL KNOWLEDGE--PROBABILISTIC INFERENCE

该项目的目标是改进和评估以下技术 从临床数据库自动构建贝叶斯信念网络 可作为诊断和预后辅助手段。金额的多少 存储在数据库中的临床信息在 在过去的二十年里，这一趋势似乎将继续下去。 信念网络能够表示网络之间的概率依赖 临床变量以相对一般的方式。研究人员已经 开发了使用信念执行概率推理的算法 网络，他们已经将这些算法应用于医疗 诊断和预后。尽管在开发方面取得了进展 信念网络的理论与应用，人工构造 这些网络往往仍然是一项困难、耗时的任务。这个 从高质量数据库自动生成信念网络可以 极大地促进了诊断和预后的构建 系统，可以作为临床决策辅助，在它们的准确性之后 并验证了其有效性。 这项研究的长期目标是增进我们对 开发可用作有用诊断的概率系统 以及内科医生的预测工具。这样的系统可以作为一种方法 用于传播高质量获取的临床知识 数据库，如从港口研究中开发的数据库。在这个范围内 背景，目前提出的研究项目的具体目标是 致： *改进和扩展当前自动构建信念的方法 来自大型数据库的网络； *测试基于以下各项的系统的诊断和预测准确性 从高质量数据库自动构建的信念网络， 与几种标准的统计技术相比较； *测试自动化和基于专家的方法的组合是否 构建信念网络将产生诊断和预测系统 比基于信念网络的系统更准确 是自动构建的。 这三个目标将使用大型、高质量的临床- 匹兹堡大学的研究数据库包含 有关晕厥患者和PORT研究中患者的信息 社区获得性肺炎。

The goal of this project is to refine and evaluate techniques that automatically construct, from clinical databases, Bayesian belief networks that can be used as diagnostic and prognostic aids. The amount of clinical information stored in databases has increased markedly in the last two decades, and it seems likely that this trend will continue. Belief networks are able to represent the probabilistic dependencies among clinical variables in a relatively general manner. Researchers have developed algorithms for performing probabilistic inference using belief networks, and they have applied these algorithms to perform medical diagnosis and prognosis. Although advances have been made in developing the theory and application of belief networks, the manual construction of these networks often remains a difficult, time-consuming task. The automated generation of belief networks from high-quality databases may facilitate significantly the construction of diagnostic and prognostic systems, which can serve as clinical decision aids, after their accuracy and usefulness are validated. The long-range goal of this research is to advance our understanding and development of probabilistic systems that can serve as useful diagnostic and prognostic tools for physicians. Such systems can serve as one method for disseminating the clinical knowledge captured in high-quality databases, such as those developed from PORT studies. Within this context, the specific aims of the current, proposed research project are to: * refine and extend current methods for automatically constructing belief networks from large databases; * test the diagnostic and prognostic accuracy of systems that are based on belief networks constructed automatically from high quality databases, compared to several standard statistical techniques; * test whether a combination of automated and expert-based methods for constructing belief networks will yield diagnostic and prognostic systems that are more accurate than systems that are based on belief networks that are constructed automatically. These three aims will be pursued using large, high-quality clinical- research databases at the University of Pittsburgh that contain information on patients with syncope and patients in a PORT study with community-acquired-pneumonia.