  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.
