Shortliffe E H
Comput Programs Biomed. 1984 Feb-Apr;18(1-2):5-13. doi: 10.1016/0010-468x(84)90018-7.
It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.
有人认为，医学诊断问题从根本上来说结构不良，尤其是在早期阶段，此时对主诉的可能解释数量可能非常多。本文讨论了临床假设唤起的过程，将其与传统计算机诊断系统中使用的结构化决策方法进行了对比，并简要概述了医学人工智能（AI）程序中使用的更开放式的推理方法。还强调了在设计建议系统以建议治疗方案而非（或除了）诊断时所引入的额外复杂性。通过讨论示例系统来说明关键概念。
