Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framework. This paper compares and discusses strategies to augment Bayesian networks with neural text representations, both in a generative and discriminative manner. This is illustrated with simulation results for a primary care use case (diagnosis of pneumonia) and discussed in a broader clinical context.

翻译：贝叶斯网络非常适合处理表格数据的临床推理，但与自然语言数据的兼容性较差，而神经网络为此类数据提供了成功的处理框架。本文比较并讨论了以生成式和判别式两种方式，利用神经文本表示增强贝叶斯网络的策略。通过初级诊疗场景（肺炎诊断）的模拟结果进行例证，并在更广泛的临床背景下展开讨论。