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A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future. Artif Intell Med 2021; 117:102108. [PMID: 34127238 DOI: 10.1016/j.artmed.2021.102108] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 05/07/2021] [Accepted: 05/10/2021] [Indexed: 12/15/2022]
Abstract
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.
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Kyrimi E, Dube K, Fenton N, Fahmi A, Neves MR, Marsh W, McLachlan S. Bayesian networks in healthcare: What is preventing their adoption? Artif Intell Med 2021; 116:102079. [PMID: 34020755 DOI: 10.1016/j.artmed.2021.102079] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 04/14/2021] [Accepted: 04/20/2021] [Indexed: 12/15/2022]
Abstract
There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems.
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Affiliation(s)
- Evangelia Kyrimi
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
| | - Kudakwashe Dube
- Health Informatics and Knowledge Engineering Research (HiKER) Group; School of Fundamental Sciences, Massey University, Palmerston North, 4442, New Zealand
| | - Norman Fenton
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Ali Fahmi
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Mariana Raniere Neves
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - William Marsh
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Scott McLachlan
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK; Health Informatics and Knowledge Engineering Research (HiKER) Group
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Ding X, Bucholc M, Wang H, Glass DH, Wang H, Clarke DH, Bjourson AJ, Dowey LRC, O'Kane M, Prasad G, Maguire L, Wong-Lin K. A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data. Sci Rep 2018; 8:9774. [PMID: 29950585 PMCID: PMC6021389 DOI: 10.1038/s41598-018-27997-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 06/12/2018] [Indexed: 12/20/2022] Open
Abstract
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.
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Affiliation(s)
- Xuemei Ding
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK.
- Faculty of Mathematics and Informatics, Fujian Normal University, Fuzhou, China.
| | - Magda Bucholc
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - David H Glass
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - Dave H Clarke
- Clarke Analytics Ltd., 6 Dernville, Annabella Mallow, Cork, Ireland
| | - Anthony John Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital, Derry~Londonderry, Northern Ireland, UK
| | - Le Roy C Dowey
- C-TRIC, Altnagelvin Hospital campus, Derry~Londonderry, Northern Ireland, UK
- School of Biomedical Sciences, Ulster University, Coleraine Campus, Northern Ireland, UK
| | - Maurice O'Kane
- C-TRIC, Altnagelvin Hospital campus, Derry~Londonderry, Northern Ireland, UK
| | - Girijesh Prasad
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - Liam Maguire
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK.
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