Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.

贝叶斯网络（BN）是图形概率模型，已被证明在医疗应用中很受欢迎。虽然众多医疗贝叶斯网络已经公布，大部分都既成事实没有的网络结构是如何开发或为什么它表示给定医学应用的正确结构的理由解释。这意味着由专家建立医学BN的过程通常是临时的并且几乎没有方法改进的机会。本文提出了普遍适用和可重复使用的医学推理模式，以帮助那些开发医学BN的人。所提出的方法是对Neil，Fenton和Nielsen在2000年引入的基于成语的方法的补充和扩展。我们提出了针对医学BN的通用成语的实例。我们将建议的医学推理模式称为医学习语。此外，我们扩展了惯用语的使用，以表示介入和反事实推理。我们认为，提议的医学惯用法是可以组合，重用和通用地用于帮助开发医学BN的逻辑推理模式。已经使用关于冠状动脉疾病的医学实例说明了所有提议的医学成语。该方法也已应用于与医学专家一起开发的其他正在进行的BN。最后，我们表明，将所提出的医学惯用法应用于已发布的BN模型会导致结构更清晰的模型。