A novel hybrid approach to automated negation detection in clinical radiology reports
A novel hybrid approach to automated negation detection in clinical radiology reports
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DOI：
10.1197/jamia.m2284
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发表时间：
2007-05-01
影响因子：
6.4
通讯作者：
Lowe, Henry J.
中科院分区：
文献类型：
--
作者：
Huang, Yang;Lowe, Henry J.
目的：否定在临床文档中很常见，是自动标引系统精度差的重要原因。先前的研究表明，如果暗示否定的词（否定信号）与否定词相距超过几个词，那么否定词可能很难识别。我们描述了一种新的混合方法，正则表达式匹配与语法分析相结合，以解决上述限制，在自动检测否定临床放射学reports.Design：否定分类的基础上否定信号的语法类别，否定模式，使用正则表达式匹配。否定的条款，然后使用相应的否定grammar.Measurements的解析树中定位：否定及其相应的语法和词汇模式的分类，通过手工检查30放射学报告，并验证了一组470放射学报告。随机抽取120份放射学报告作为检验集，由4名医师采用改良的德尔菲设计构建金标准。在120份报告的测试集中，共有2,976个名词短语，其中287个被正确识别为否定（真阳性），沿着有23个未检测到的真阴性（假阴性）和4个错误阴性（假阳性）。混合方法识别否定短语的灵敏度为92.6%（95% CI 90.9-93.4%），阳性预测值为98.6%（95% CI 96.9-99.4%），特异性99.87%（95% CI 99.7-99.9%）。这种新的混合方法可以准确地定位否定的概念，在临床放射学报告中，不仅在接近，而且在距离，否定信号。
Objective: Negation is common in clinical documents and is an important source of poor precision in automated indexing systems. Previous research has shown that negated terms may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in clinical radiology reports.Design: Negations are classified based upon the syntactical categories of negation signals, and negation patterns, using regular expression matching. Negated terms are then located in parse trees using corresponding negation grammar.Measurements: A classification of negations and their corresponding syntactical and lexical patterns were developed through manual inspection of 30 radiology reports and validated on a set of 470 radiology reports. Another 120 radiology reports were randomly selected as the test set on which a modified Delphi design was used by four physicians to construct the gold standard.Results: In the test set of 120 reports, there were a total of 2,976 noun phrases, of which 287 were correctly identified as negated (true positives), along with 23 undetected true negations (false negatives) and 4 mistaken negations (false positives). The hybrid approach identified negated phrases with sensitivity of 92.6% (95% CI 90.9-93.4%), positive predictive value of 98.6% (95% CI 96.9-99.4%), and specificity of 99.87% (95% CI 99.7-99.9%).Conclusion: This novel hybrid approach can accurately locate negated concepts in clinical radiology reports not only when in close proximity to, but also at a distance from, negation signals.