Steimann F
Artif Intell Med. 1996 Aug;8(4):343-57. doi: 10.1016/0933-3657(95)00040-2.
Applying the methods of Artificial Intelligence to clinical monitoring requires some kind of signal-to-symbol conversion as a prior step. Subsequent processing of the derived symbolic information must also be sensitive to history and development, as the failure to address temporal relationships between findings invariably leads to inferior results. DIAMON-1, a framework for the design of diagnostic monitors, provides two methods for the interpretation of time-varying data: one for the detection of trends based on classes of courses, and one for the tracking of disease histories modelled through deterministic automata. Both methods make use of fuzzy set theory taking account of the elasticity of medical categories and allowing discrete disease models to mirror the patient's continuous progression through the stages of illness.
将人工智能方法应用于临床监测需要某种信号到符号的转换作为前期步骤。对派生的符号信息进行后续处理时还必须对病史和病情发展敏感，因为未能处理各项检查结果之间的时间关系必然会导致较差的结果。DIAMON-1是一种诊断监测器设计框架，它提供了两种解释随时间变化数据的方法：一种是基于病程类别检测趋势，另一种是通过确定性自动机对疾病史进行跟踪。这两种方法都利用了模糊集理论，考虑到医学类别的弹性，并允许离散疾病模型反映患者在疾病各阶段的连续进展情况。
