利用骨关节炎倡议组织的数据，评估动态贝叶斯信念网络以预测骨关节炎性膝关节疼痛。
Evaluation of a dynamic bayesian belief network to predict osteoarthritic knee pain using data from the osteoarthritis initiative.
作者信息
Watt Emily W, Bui Alex A T
机构信息
University of California Los Angeles, Los Angeles, CA, USA.
出版信息
AMIA Annu Symp Proc. 2008 Nov 6;2008:788-92.
Abstract
The most common cause of disability in older adults in the United States is osteoarthritis. To address the problem of early disease prediction, we have constructed a Bayesian belief network (BBN) composed of knee OA-related symptoms to support prognostic queries. The purpose of this study is to evaluate a static and dynamic BBN--based on the NIH Osteoarthritis Initiative (OAI) data--in predicting the likelihood of a patient being diagnosed with knee OA. Initial validation results are promising: our model outperforms a logistic regression model in several designed studies. We can conclude that our model can effectively predict the symptoms that are commonly associated with the presence of knee OA.
摘要
在美国，老年人残疾的最常见原因是骨关节炎。为了解决疾病早期预测的问题，我们构建了一个由膝关节骨关节炎相关症状组成的贝叶斯信念网络（BBN），以支持预后查询。本研究的目的是评估一个基于美国国立卫生研究院骨关节炎计划（OAI）数据的静态和动态BBN，用于预测患者被诊断为膝关节骨关节炎的可能性。初步验证结果很有前景：在多项设计研究中，我们的模型优于逻辑回归模型。我们可以得出结论，我们的模型能够有效预测通常与膝关节骨关节炎存在相关的症状。
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