  Bayesian networks (BNs) have received increasing research attention that is
not matched by adoption in practice and yet have potential to significantly
benefit healthcare. Hitherto, research works have not investigated the types of
medical conditions being modelled with BNs, nor whether any differences exist
in how and why they are applied to different conditions. This research seeks to
identify and quantify the range of medical conditions for which
healthcare-related BN models have been proposed, and the differences in
approach between the most common medical conditions to which they have been
applied. We found that almost two-thirds of all healthcare BNs are focused on
four conditions: cardiac, cancer, psychological and lung disorders. We believe
that a lack of understanding regarding how BNs work and what they are capable
of exists, and that it is only with greater understanding and promotion that we
may ever realise the full potential of BNs to effect positive change in daily
healthcare practice.
