Patil R S
Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge 02139.
Comput Methods Programs Biomed. 1987 Sep-Oct;25(2):117-23. doi: 10.1016/0169-2607(87)90047-2.
Over the last decade substantial advances have been made in the use of causal pathophysiological knowledge in artificial intelligence-based programs for medical diagnosis. Various forms of causal representations have been used. They include probabilistic models, quantitative models, qualitative models, and models that describe causal relations at multiple levels of detail. This paper briefly analyses these methods using three representative systems. Outstanding problems and possible direction in further exploitation of causal reasoning for medical decision-support systems are also discussed.
在过去十年中，基于人工智能的医学诊断程序在运用因果病理生理学知识方面取得了重大进展。人们使用了各种形式的因果表示法，包括概率模型、定量模型、定性模型以及在多个详细程度级别描述因果关系的模型。本文使用三个具有代表性的系统简要分析了这些方法，还讨论了医学决策支持系统在进一步利用因果推理方面存在的突出问题和可能的方向。
