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 there are any differences 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 there is a lack of understanding regarding how BNs work and what they are capable of, 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.

贝叶斯网络 (BN) 受到越来越多的研究关注，但在实践中的采用并不匹配，但仍有可能显着使医疗保健受益。迄今为止，研究工作尚未调查用 BN 建模的医疗条件类型，也没有研究将它们应用于不同条件的方式和原因是否存在任何差异。本研究旨在确定和量化提出医疗保健相关 BN 模型的医疗条件范围，以及应用这些模型的最常见医疗条件之间的方法差异。我们发现，几乎三分之二的医疗保健 BN 都关注四种疾病：心脏、癌症、心理和肺部疾病。我们认为，人们对 BN 的工作方式及其能力缺乏了解，