A controlled trial of automated classification of negation from clinical notes.
A controlled trial of automated classification of negation from clinical notes.
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DOI：
10.1186/1472-6947-5-13
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发表时间：
2005-05-05
影响因子：
3.5
通讯作者：
Wahner-Roedler, Dietlind L
中科院分区：
文献类型：
--
作者：
Elkin, Peter L;Brown, Steven H;Wahner-Roedler, Dietlind L
背景技术背景：如果我们要理解记录的可计算含义，则在电子健康记录中识别否定是必不可少的：我们的目标是比较用于将否定分配到组成表达式中的临床概念的自动化机制与人类的否定进行比较的准确性。还执行失败分析，以识别否定识别不佳的原因（即，遗漏的概念表示，不准确的概念表示，遗漏的否定，不准确的否定识别）。方法：使用马约词汇服务器解析引擎解析41份临床文档（医学评价;有时在马约之外，这些文档被称为历史和体格检查）。使用SNOMED-C为记录中的临床概念提供概念覆盖。这些记录导致了概念的识别和否定的文本线索。这些记录由独立的医学术语学家审查，结果记录在电子表格中。在审查中出现的问题，内科系被雇用，以作出最后的determination.RESULTS：SNOMED-CT被用来提供概念覆盖的14,792个概念，在41个健康记录从约翰霍普金斯大学。其中，1，823个概念被人类审查确定为负面。否定分配的灵敏度（回忆）为97.2%（p < 0.001，Pearson卡方检验;与抛硬币相比）。否定赋值的特异性为98.8%。否定的阳性似然比为81。结论：基于文本回顾的健康档案中概念的否定自动赋值是可行的和实用的。否定的词汇分配是一个很好的测试真正的否定判断的高灵敏度，特异性和阳性似然比的测试。SNOMED-CT的总体覆盖率为88.7%。
BACKGROUND: Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation).METHODS: 41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations) were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-C was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination.RESULTS: SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall) of the assignment of negation was 97.2% (p < 0.001, Pearson Chi-Square test; when compared to a coin flip). The specificity of assignment of negation was 98.8%. The positive likelihood ratio of the negation was 81. The positive predictive value (Precision) was 91.2%CONCLUSION: Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.