Sedki Karima, Tsopra Rosy
LIMICS, INSERM UMRS 1142, Université Paris 13, Sorbonne Paris Cité, 93017 Bobigny, France UPMC Université Paris 6, Sorbonne Universités, Paris.
Stud Health Technol Inform. 2018;247:740-744.
Bayesian Networks (BNs) are often used for designing diagnosis decision support systems. They are a well-established method for reasoning under uncertainty and making inferences. But, eliciting the probabilities can be tedious and time-consuming especially in medical domain where variables are often related by qualitative terms rather than probabilities. The goal of this paper is to propose a method for eliciting the probabilities required in BNs by using and transforming causal rules which are often used in medicine. The method consists in first constructing the structure of BNs by reporting medical expert's knowledge in the form of causal rules, and then constructing the parameters of the BNs by transforming the terms used for qualified causal rules into probabilities. Example is given in obesity domain. Further works are needed to reinforce our method like the consideration of circular causal rules.
贝叶斯网络（BNs）常用于设计诊断决策支持系统。它们是一种在不确定性下进行推理和推断的成熟方法。但是，获取概率可能既繁琐又耗时，尤其是在医学领域，其中变量通常由定性术语而非概率相关联。本文的目标是提出一种通过使用和转换医学中常用的因果规则来获取贝叶斯网络所需概率的方法。该方法包括首先通过以因果规则的形式报告医学专家的知识来构建贝叶斯网络的结构，然后通过将用于定性因果规则的术语转换为概率来构建贝叶斯网络的参数。在肥胖领域给出了示例。需要进一步的工作来加强我们的方法，例如考虑循环因果规则。
