Advanced Bayesian Networks for Epidemiology

我们将通过扩展贝叶斯网络的功能来展示先进人工智能在医学信息学中的潜力。当研究人员需要在以不确定性为特征的领域将联合收割机因果推理和诊断推理结合起来时，贝叶斯网络表现出色。但它们有一个缺陷，这阻碍了它们的用途：它们还不能很容易地混合连续变量和离散变量。我们将扩展它们来处理这种混合，然后证明它们可以在多大程度上改善目前预测冠心病（CHD）的方法。

We will demonstrate the potential of advanced Artificial Intelligence for medical informatics by extending the capabilities of Bayesian Networks. Bayesian Networks excel when researchers need to combine causal and diagnostic reasoning in areas characterised by uncertainty. But they have one flaw which hinders their use: they do not yet easily mix continuous and discrete variables. We will extend them to handle such mixes, then demonstrate how much they can improve on current methods for predicting, among other things, coronary heart disease (CHD).