Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS Specialist Lexicon
Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS Specialist Lexicon
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DOI：
10.1197/jamia.m1695
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发表时间：
2005-05-01
影响因子：
6.4
通讯作者：
Cucina, RJ
中科院分区：
文献类型：
--
作者：
Huang, Y;Lowe, HJ;Cucina, RJ
目的：本研究的目的是开发和评估一种使用自然语言处理（NLP）从一组临床放射学报告中提取具有完整短语结构的名词短语的方法，并调查使用UMLS（R）专家词典来提高临床放射学文档中名词短语识别的效果。设计：名词短语识别（NPI）模块由句子边界检测器、在非医学领域训练的统计自然语言解析器和名词短语（NP）标记器组成。NPI模块处理了一组100份XML表示的临床放射学报告，这些报告采用Health Level 7（HL 7）（R）临床文档架构（CDA）兼容格式。计算输出进行了比较，由四名医生和一名作者的最大（最长）NP和一名作者的基础（简单）NP，分别手动标记。从UMLS专家词典中创建了一个扩展的生物医学术语词典，用于提高NPI性能。结果：测试集是50个随机选择的报告。句子边界检测器达到了99.0%的准确率和98.6%的召回率。使用UMLS专家词典前，NPI的总体最大准确率和召回率分别为78.9%和81.5%，使用后分别为82.1%和84.6%。使用UMLS专家词典前，句子边界检测的总的基本NPI准确率和召回率分别为88.2%和86.8%，使用UMLS专家词典后分别为93.1%和92.6%，减少了31.1%的假阳性和34.3%的假阴性。适应后，使用UMLS专家词典，统计解析器的NPI性能的放射学报告增加到水平相媲美的解析器的本地性能在其新闻通讯训练域和其他研究人员在一般非医疗领域的报告。
Objective: The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of clinical radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLS (R) Specialist Lexicon to improve noun phrase identification within clinical radiology documents.Design: The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented clinical radiology reports in Health Level 7 (HL7)(R) Clinical Document Architecture (CDA)compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance.Results: The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% And 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%.Conclusion: The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.