Friedman C, Alderson P O, Austin J H, Cimino J J, Johnson S B
Columbia University, New York, NY, USA.
J Am Med Inform Assoc. 1994 Mar-Apr;1(2):161-74. doi: 10.1136/jamia.1994.95236146.
Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms.
The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain.
The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision.
Without training specific to the four diseases, recall and precision of the system (combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.
开发一种通用的自然语言处理器，该处理器可识别叙述性报告中的临床信息，并将该信息映射到包含临床术语的结构化表示中。
自然语言处理器提供三个处理阶段，所有这些阶段均由不同的知识源驱动。第一阶段进行解析。它通过使用定义语义模式和目标形式的语法来识别文本的结构。第二阶段，规范化，通过多词短语的组合映射对初始目标结构中的术语进行标准化。第三阶段，编码，将术语映射到受控词汇表。放射学是该处理器的测试领域，目标结构是该领域中表示临床信息的形式模型。
处理器对230份放射学报告的印象部分进行编码。将对所得数据库中四种疾病出现情况的自动查询结果与三位医生组成的小组的分析结果进行比较，以确定召回率和精确率。
在没有针对这四种疾病进行特定训练的情况下，系统（处理器和查询生成器的综合效果）的召回率和精确率分别为70%和87%。对查询组件进行训练可将召回率提高到85%，而精确率不变。
