# An Incremental Explanation of Inference in Hybrid Bayesian Networks for Increasing Model Trustworthiness and Supporting Clinical Decision Making

arXiv: https://arxiv.org/abs/2003.02599 · HTML: https://mlsanity.com/paper/2003.02599/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems](https://mlsanity.com/paper/2502.09849/) (2025)
- [Explaining Bayesian Networks in Natural Language using Factor Arguments. Evaluation in the medical domain](https://mlsanity.com/paper/2410.18060/) (2024)
- [Finding, Scoring and Explaining Arguments in Bayesian Networks](https://mlsanity.com/paper/2112.00799/) (2021)
- [Comparing verbal, visual and combined explanations for Bayesian Network inferences](https://mlsanity.com/paper/2511.16961/) (2025)

## Abstract (arXiv)

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to hybrid BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.