A Progressive Explanation of Inference in ‘Hybrid’ Bayesian Networks for Supporting Clinical Decision Making

Abstract

Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support.