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

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