A simple algorithm for identifying negated findings and diseases in discharge summaries
A simple algorithm for identifying negated findings and diseases in discharge summaries
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DOI：
10.1006/jbin.2001.1029
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发表时间：
2001-10-01
影响因子：
4.5
通讯作者：
Buchanan, BG
中科院分区：
文献类型：
--
作者：
Chapman, WW;Bridewell, W;Buchanan, BG
医疗记录中的叙述性报告包含丰富的信息，可以增强结构化数据，用于管理患者信息和预测疾病趋势。相关的否定在文本中很明显，但通常不会在结构化数据库中索引。本文报告的研究目的是测试一种简单的算法，用于确定叙述性医学报告中提到的发现或疾病是否存在。我们开发了一个简单的正则表达式算法，称为NegEx，它实现了几个表示否定的短语，过滤掉包含错误地显示为否定短语的短语的句子，并限制否定短语的范围。我们将NegEx与具有有限否定短语集和更简单范围概念的基线算法进行了比较。在对医生索引的出院摘要中1000个句子中的1235个发现和疾病进行的测试中，NegEx的特异性为94.5%（基线为85.3%），阳性预测值为84.5%（基线为68.4%），同时保持77.8%的合理敏感性（基线为88.3%）。我们的结论是，只需很少的实现工作，用于确定是否存在发现或疾病的简单正则表达式算法就可以从出院摘要中识别出大部分相关的阴性。(C) 2001 Elsevier Science（美国）
Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries. (C) 2001 Elsevier Science (USA).