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A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-05-13 , DOI: 10.1016/j.artmed.2021.102108
Evangelia Kyrimi 1 , Scott McLachlan 2 , Kudakwashe Dube 3 , Mariana R Neves 1 , Ali Fahmi 1 , Norman Fenton 1
Affiliation  

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.



中文翻译:

医疗保健中贝叶斯网络的全面范围审查:过去、现在和未来

在医疗保健贝叶斯网络(BNS)的不全面审查已经公布在过去,因此很难组织研究的贡献我ñ本,并确定挑战和被忽视的领域是需要解决的问题在未来. 这种对医疗保健领域 BN 的独特而新颖的范围界定审查为全面表征该领域及其当前状态提供了一个分析框架。使用相关关键字对健康和健康信息学文献数据库进行文献搜索,发现 3810 篇文章减少到 123 篇。多种机器学习算法,其中BN只是一种。使用新的分析框架,我们表明:(1)医疗保健中的 BN 没有充分发挥其潜力;(2) 缺乏通用的BN开发流程;(3) 文献中介绍医疗保健中 BN 的方式存在局限性,这会影响理解,就系统方法、实践和采用达成共识;(4) 准确的 BN 和影响临床实践的有用 BN 之间存在差距。这篇综述强调了几个被忽视的问题,例如 BN 的受限目标、临时 BN 开发方法以及实践中缺乏 BN 的采用,并向研究人员和临床医生揭示了解决这些问题的必要性。为了指明前进的方向,本文提出了未来的研究方向,并就 BN 的开发方法和实践中的采用提出了建议。BN 在实践中缺乏采用,这向研究人员和临床医生揭示了解决这些问题的必要性。为了指明前进的方向,本文提出了未来的研究方向,并就 BN 的开发方法和实践中的采用提出了建议。BN 在实践中缺乏采用,这向研究人员和临床医生揭示了解决这些问题的必要性。为了指明前进的方向,本文提出了未来的研究方向,并就 BN 的开发方法和实践中的采用提出了建议。

更新日期:2021-05-23
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