Lewin H C
Clinical Decision Making Group, Massachusetts Institute of Technology.
Proc Annu Symp Comput Appl Med Care. 1991:644-8.
Causal models have been used, with considerable success, to reason in the medical domain. While these systems typically have a robust reasoning mechanism and knowledge base about their specific area of expertise, their ability to satisfactorily explain their results in a meaningful, coherent and concise manner has been less impressive then their diagnostic capabilities. This paper describes a program, HF-Explain, that generates natural language explanations of one such system--the Heart Failure Program. HF-Explain, is loosely based on work done by McKeown in the Text system, using augmented transition networks (ATN) as a formalism to guide the explanation process. The result is a coherent, concise, accurate and rich explanation of Heart Failure Programs' diagnostic hypotheses.
因果模型已被用于医学领域的推理，并取得了相当大的成功。虽然这些系统通常在其特定专业领域拥有强大的推理机制和知识库，但它们以有意义、连贯且简洁的方式令人满意地解释其结果的能力，却不如其诊断能力那么令人印象深刻。本文描述了一个程序HF-Explain，它能为一个这样的系统——心力衰竭程序生成自然语言解释。HF-Explain大致基于麦基翁在文本系统中所做的工作，使用扩充转移网络（ATN）作为一种形式主义来指导解释过程。结果是对心力衰竭程序的诊断假设进行了连贯、简洁、准确且丰富的解释。
