用证据流图解释贝叶斯网络推理，让医疗决策更易懂
Explaining Bayesian Networks in Natural Language using Factor Arguments. Evaluation in the medical domain
- 基于因子论证构建证据流动的逻辑图
- 算法自动排序独立论证，按强度输出
- 在医疗场景中用户评估显示解释效果更优
本文提出一种基于因子论证的自然语言解释模型，用于描述贝叶斯网络推理过程中的证据流动关系，将观测证据与目标变量关联。引入因子论证独立性概念，解决论证应合并或分开展示的问题，并设计算法从证据节点和目标节点出发，生成按强度排序的全部独立因子论证列表。进一步实现基于该方法的自然语言解释生成方案。在医疗领域通过人工评估验证，对比现有解释方法，结果表明用户认为本方法显著更有利于理解贝叶斯网络推理。
原文摘要 · Abstract (English)
In this paper, we propose a model for building natural language explanations for Bayesian Network Reasoning in terms of factor arguments, which are argumentation graphs of flowing evidence, relating the observed evidence to a target variable we want to learn about. We introduce the notion of factor argument independence to address the outstanding question of defining when arguments should be presented jointly or separately and present an algorithm that, starting from the evidence nodes and a target node, produces a list of all independent factor arguments ordered by their strength. Finally, we implemented a scheme to build natural language explanations of Bayesian Reasoning using this approach. Our proposal has been validated in the medical domain through a human-driven evaluation study where we compare the Bayesian Network Reasoning explanations obtained using factor arguments with an alternative explanation method. Evaluation results indicate that our proposed explanation approach is deemed by users as significantly more useful for understanding Bayesian Network Reasoning than another existing explanation method it is compared to.
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