Fiszman Marcelo, Shin Dongwook, Sneiderman Charles A, Jin Honglan, Rindflesch Thomas C
Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda MD 20894.
AMIA Annu Symp Proc. 2010 Nov 13;2010:227-31.
Many natural language processing systems are being applied to clinical text, yet clinically useful results are obtained only by honing a system to a particular context. We suggest that concentration on the information needed for this processing is crucial and present a knowledge intensive methodology for mapping clinical text to LOINC. The system takes published case reports as input and maps vital signs and body measurements and reports of diagnostic procedures to fully specified LOINC codes. Three kinds of knowledge are exploited: textual, ontological, and pragmatic (including information about physiology and the clinical process). Evaluation on 4809 sentences yielded precision of 89% and recall of 93% (F-score 0.91). Our method could form the basis for a system to provide semi-automated help to human coders.
许多自然语言处理系统都应用于临床文本，但只有将系统优化到特定语境才能获得临床可用的结果。我们认为专注于该处理所需的信息至关重要，并提出了一种知识密集型方法，用于将临床文本映射到LOINC。该系统将已发表的病例报告作为输入，并将生命体征、身体测量值以及诊断程序报告映射到完全指定的LOINC编码。利用了三种知识：文本知识、本体知识和语用知识（包括生理学和临床过程信息）。对4809个句子的评估得出，精确率为89%，召回率为93%（F值为）。我们的方法可为向人工编码员提供半自动帮助的系统奠定基础。 （注：原文中F-score 0.91后面少了具体数字，译文按原文翻译）
