# A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future

arXiv: https://arxiv.org/abs/2002.08627 · HTML: https://mlsanity.com/paper/2002.08627/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 22 of 22)

- [pgmpy: A Python Toolkit for Bayesian Networks](https://mlsanity.com/paper/2304.08639/) (2023)
- [bnRep: A repository of Bayesian networks from the academic literature](https://mlsanity.com/paper/2409.19158/) (2024)
- [SimSUM: Simulated Benchmark with Structured and Unstructured Medical Records](https://mlsanity.com/paper/2409.08936/) (2024)
- [Clinical Reasoning over Tabular Data and Text with Bayesian Networks](https://mlsanity.com/paper/2403.09481/) (2024)
- [FedGES: A Federated Learning Approach for BN Structure Learning](https://mlsanity.com/paper/2502.01538/) (2025)
- [Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data](https://mlsanity.com/paper/2211.04924/) (2022)
- [Fast Parallel Bayesian Network Structure Learning](https://mlsanity.com/paper/2212.04259/) (2022)
- [A hybrid Bayesian network for medical device risk assessment and management](https://mlsanity.com/paper/2209.03352/) (2022)
- [Stable Structure Learning with HC-Stable and Tabu-Stable Algorithms](https://mlsanity.com/paper/2504.01740/) (2025)
- [Dynamic Expert-Guided Model Averaging for Causal Discovery](https://mlsanity.com/paper/2601.16715/) (2026)
- [A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech data](https://mlsanity.com/paper/2512.07741/) (2025)
- [Structural Dimension Reduction in Bayesian Networks](https://mlsanity.com/paper/2601.08236/) (2026)
- [Motivating explanations in Bayesian networks using MAP-independence](https://mlsanity.com/paper/2208.03121/) (2022)
- [Neural Bayesian Network Understudy](https://mlsanity.com/paper/2211.08243/) (2022)
- [Bayesian Network Models of Causal Interventions in Healthcare Decision Making: Literature Review and Software Evaluation](https://mlsanity.com/paper/2211.15258/) (2022)
- [Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study](https://mlsanity.com/paper/2306.04335/) (2023)
- [A SAT-based approach to rigorous verification of Bayesian networks](https://mlsanity.com/paper/2408.00986/) (2024)
- [Structural Refinement of Bayesian Networks for Efficient Model Parameterisation](https://mlsanity.com/paper/2510.00334/) (2025)
- [Patient-level Information Extraction by Consistent Integration of Textual and Tabular Evidence with Bayesian Networks](https://mlsanity.com/paper/2511.17056/) (2025)
- [Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space](https://mlsanity.com/paper/2511.19273/) (2025)
- [How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding](https://mlsanity.com/paper/2607.09449/) (2026)
- [The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling](https://mlsanity.com/paper/2608.04046/) (2026)

## Abstract (arXiv)

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. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.