mlsanityData to 14 Oct 2026

Bayesian Networks in Healthcare: Distribution by Medical Condition

arXiv 2002.00224 ↗First posted 1 Feb 2020cs.CY cs.AIcited by 23

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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.

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

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Citing papers per year: 2021: 5; 2022: 3; 2023: 0; 2024: 4; 2025: 8; 2026: 3.

23 papers.

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  1. 2021Learning Bayesian Networks in the Presence of Structural Side Informationcited by 12
  2. 2021Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R packagecited by 8
  3. 2021Staged trees and asymmetry-labeled DAGscited by 8
  4. 2022Methods for Recovering Conditional Independence Graphs: A Surveycited by 7
  5. 2022VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing valuescited by 5
  6. 2024Clinical Reasoning over Tabular Data and Text with Bayesian Networkscited by 4
  7. 2025FedGES: A Federated Learning Approach for BN Structure Learningcited by 4
  8. 2022Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal datacited by 3
  9. 2024Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approachcited by 1
  10. 2024Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networkscited by 1
  11. 2025Bayesian Network Structural Consensus via Greedy Min-Cut Analysiscited by 1
  12. 2025Can LLMs Assist Expert Elicitation for Probabilistic Causal Modeling?cited by 1
11 more
  1. 2021Global sensitivity analysis in probabilistic graphical modelscited by 0
  2. 2021High-Dimensional Inference in Bayesian Networkscited by 0
  3. 2024A SAT-based approach to rigorous verification of Bayesian networkscited by 0
  4. 2025Enhancing Bayesian Network Structural Learning with Monte Carlo Tree Searchcited by 0
  5. 2025Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Predictioncited by 0
  6. 2025BayesL: a Logical Framework for the Verification of Bayesian Networkscited by 0
  7. 2025Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operationcited by 0
  8. 2025Patient-level Information Extraction by Consistent Integration of Textual and Tabular Evidence with Bayesian Networkscited by 0
  9. 2026Meta-probabilistic Modelingcited by 0
  10. 2026The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modelingcited by 0
  11. 2026Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruningcited by 0