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瑞典临床文本中的否定检测:NegEx对瑞典语的适应性调整。

Negation detection in Swedish clinical text: An adaption of NegEx to Swedish.

作者信息

Skeppstedt Maria

机构信息

Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, SE-164 40 Kista, Sweden.

出版信息

J Biomed Semantics. 2011;2 Suppl 3(Suppl 3):S3. doi: 10.1186/2041-1480-2-S3-S3. Epub 2011 Jul 14.

DOI:10.1186/2041-1480-2-S3-S3
PMID:21992616
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3194175/
Abstract

BACKGROUND

Most methods for negation detection in clinical text have been developed for English text, and there is a need for evaluating the feasibility of adapting these methods to other languages. A Swedish adaption of the English rule-based negation detection system NegEx, which detects negations through the use of trigger phrases, was therefore evaluated.

RESULTS

The Swedish adaption of NegEx showed a precision of 75.2% and a recall of 81.9%, when evaluated on 558 manually classified sentences containing negation triggers, and a negative predictive value of 96.5% when evaluated on 342 sentences not containing negation triggers.

CONCLUSIONS

The precision was significantly lower for the Swedish adaptation than published results for the English version, but since many negated propositions were identified through a limited set of trigger phrases, it could nevertheless be concluded that the same trigger phrase approach is possible in a Swedish context, even though it needs to be further developed.

AVAILABILITY

The triggers used for the evaluation of the Swedish adaption of NegEx are available at http://people.dsv.su.se/~mariask/resources/triggers.txt and can be used together with the original NegEx program for negation detection in Swedish clinical text.

摘要

背景

大多数临床文本中的否定检测方法是针对英文文本开发的,因此需要评估将这些方法应用于其他语言的可行性。因此,对基于英文规则的否定检测系统NegEx的瑞典语改编版本进行了评估,该系统通过使用触发短语来检测否定。

结果

在对558个包含否定触发词的人工分类句子进行评估时,NegEx的瑞典语改编版本的精确率为75.2%,召回率为81.9%;在对342个不包含否定触发词的句子进行评估时,其阴性预测值为96.5%。

结论

瑞典语改编版本的精确率明显低于英文版已发表的结果,但由于许多否定命题是通过有限的一组触发短语识别出来的,因此可以得出结论,即使需要进一步开发,在瑞典语环境中采用相同的触发短语方法也是可行的。

可用性

用于评估NegEx瑞典语改编版本的触发词可在http://people.dsv.su.se/~mariask/resources/triggers.txt获取,并可与原始NegEx程序一起用于瑞典语临床文本中的否定检测。

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本文引用的文献

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ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.语境:一种从临床报告中确定否定、体验者和时间状态的算法。
J Biomed Inform. 2009 Oct;42(5):839-51. doi: 10.1016/j.jbi.2009.05.002. Epub 2009 May 10.
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Machine learning and rule-based approaches to assertion classification.用于断言分类的机器学习和基于规则的方法。
J Am Med Inform Assoc. 2009 Jan-Feb;16(1):109-15. doi: 10.1197/jamia.M2950. Epub 2008 Oct 24.
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A novel hybrid approach to automated negation detection in clinical radiology reports.一种用于临床放射学报告中自动否定检测的新型混合方法。
J Am Med Inform Assoc. 2007 May-Jun;14(3):304-11. doi: 10.1197/jamia.M2284. Epub 2007 Feb 28.
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A controlled trial of automated classification of negation from clinical notes.一项关于临床记录中否定词自动分类的对照试验。
BMC Med Inform Decis Mak. 2005 May 5;5:13. doi: 10.1186/1472-6947-5-13.
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Implementation and evaluation of a negation tagger in a pipeline-based system for information extract from pathology reports.基于管道系统的病理学报告信息提取中否定标记器的实现与评估。
Stud Health Technol Inform. 2004;107(Pt 1):663-7.
6
A simple algorithm for identifying negated findings and diseases in discharge summaries.一种用于识别出院小结中否定性检查结果和疾病的简单算法。
J Biomed Inform. 2001 Oct;34(5):301-10. doi: 10.1006/jbin.2001.1029.
7
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Proc AMIA Symp. 2001:105-9.
8
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J Am Med Inform Assoc. 2001 Nov-Dec;8(6):598-609. doi: 10.1136/jamia.2001.0080598.