# Bayesian Networks in Healthcare: Distribution by Medical Condition

arXiv: https://arxiv.org/abs/2002.00224 · HTML: https://mlsanity.com/paper/2002.00224/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 23 of 23)

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- [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)
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- [Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach](https://mlsanity.com/paper/2403.19083/) (2024)
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- [Enhancing Bayesian Network Structural Learning with Monte Carlo Tree Search](https://mlsanity.com/paper/2502.01527/) (2025)
- [Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction](https://mlsanity.com/paper/2506.13920/) (2025)
- [BayesL: a Logical Framework for the Verification of Bayesian Networks](https://mlsanity.com/paper/2506.23773/) (2025)
- [Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation](https://mlsanity.com/paper/2510.09223/) (2025)
- [Patient-level Information Extraction by Consistent Integration of Textual and Tabular Evidence with Bayesian Networks](https://mlsanity.com/paper/2511.17056/) (2025)
- [Meta-probabilistic Modeling](https://mlsanity.com/paper/2601.04462/) (2026)
- [The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling](https://mlsanity.com/paper/2608.04046/) (2026)
- [Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning](https://mlsanity.com/paper/2609.03724/) (2026)

## Abstract (arXiv)

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 any differences exist 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 that a lack of understanding regarding how BNs work and what they are capable of exists, 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.