Andreassen S, Jensen F V, Olesen K G
Aalborg University, Institute of Electronic Systems, Denmark.
Int J Biomed Comput. 1991 May-Jun;28(1-2):1-30. doi: 10.1016/0020-7101(91)90023-8.
Causal probabilistic networks (CPNs) offer new methods by which you can build medical expert systems that can handle all types of medical reasoning within a uniform conceptual framework. Based on the experience from a commercially available system and a couple of large prototype systems, it appears that CPNs are now an attractive alternative to other methods. A CPN is an intensional model of a domain, and it is therefore conceptually much closer to qualitative reasoning systems and to simulation systems than to rule-based or logic-based systems. Recent progress in Bayesian inference in networks has yielded computationally efficient methods. The inference method used follows the fundamental axioms of probability theory, and gives a sound framework for causal and diagnostic (deductive and abductive) reasoning under uncertainty. Experience with the prototypes indicates that it may be possible to use decision theory as a rational approach to test planning and therapy planning. The way in which knowledge is acquired and represented in CPNs makes it easy to express 'deep knowledge' for example in the form of physiological models, and the facilities for learning make it possible to make a smooth transition from expert opinion to statistics based on empirical data.
因果概率网络（CPNs）提供了新的方法，通过这些方法可以构建医学专家系统，该系统能够在统一的概念框架内处理所有类型的医学推理。基于一个商业可用系统和几个大型原型系统的经验，CPNs现在似乎是其他方法的一个有吸引力的替代方案。CPN是一个领域的内涵模型，因此在概念上它比基于规则或基于逻辑的系统更接近定性推理系统和模拟系统。网络中贝叶斯推理的最新进展产生了计算效率高的方法。所使用的推理方法遵循概率论的基本公理，并为不确定情况下的因果和诊断（演绎和归纳）推理提供了一个合理的框架。原型的经验表明，有可能将决策理论用作测试计划和治疗计划的合理方法。知识在CPNs中获取和表示的方式使得以例如生理模型的形式表达“深层知识”变得容易，并且学习工具使得从专家意见平稳过渡到基于经验数据的统计成为可能。
