论文标题
关于混合贝叶斯网络推断的增量解释，用于提高模型的可信度和支持临床决策
An Incremental Explanation of Inference in Hybrid Bayesian Networks for Increasing Model Trustworthiness and Supporting Clinical Decision Making
论文作者
论文摘要
各种AI模型越来越被视为临床决策支持工具的一部分。但是，很少考虑这种模型的可信度。如果临床医生能够理解和信任其预测，则更有可能使用模型。这样做的关键是，如果可以解释其基本推理。贝叶斯网络（BN）模型的优势是它不是黑框，可以解释其推理。在本文中，我们提出了可以应用于混合BN的推理的增量解释，即那些包含离散节点和连续节点的BN。我们回答的关键问题是：（1）重要的证据支持或与预测相矛盾，以及（2）中间变量可以通过其中来完成信息流。使用真实的临床案例研究来说明该解释。还进行了一项小型评估研究。
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.
