  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.
