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：贝叶斯网络适用于表格数据的临床推理，但难以兼容自然语言数据——这类数据更适合采用神经网络框架。本文比较并探讨了以生成式与判别式两种方式将神经文本表征融入贝叶斯网络的策略，并通过初级诊疗场景（肺炎诊断）的模拟结果进行验证，同时在更广泛的临床背景下展开讨论。