Risk and Information Management, Queen Mary University of London, United Kingdom; Health Informatics and Knowledge Engineering Research (HiKER) Group, United Kingdom.
Health Informatics and Knowledge Engineering Research (HiKER) Group, United Kingdom; School of Fundamental Sciences, Massey University, New Zealand.
Artif Intell Med. 2020 Jul;107:101912. doi: 10.1016/j.artmed.2020.101912. Epub 2020 Jun 10.
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 (BNs) 受到越来越多的研究关注，但在实践中并未得到广泛应用，然而它们有可能极大地有益于医疗保健。迄今为止，研究工作尚未调查使用 BNs 进行建模的医疗条件的类型，也没有调查它们在不同条件下的应用方式和原因是否存在差异。本研究旨在确定和量化已提出的与医疗保健相关的 BNs 模型所针对的医疗条件的范围，以及它们应用于最常见医疗条件的方法之间的差异。我们发现，几乎三分之二的医疗保健 BNs 都集中在四种疾病上：心脏疾病、癌症、心理和肺部疾病。我们认为，人们对 BNs 的工作原理和功能缺乏了解，只有通过更深入的了解和推广，我们才能充分发挥 BNs 的潜力，使其在日常医疗保健实践中产生积极的影响。
