Method of Detection of Negative Terminology in Chinese Electronic Medical Record_Journal of Biomedical Engineering

Authors：

ZHANGYuanpeng , DONGJiancheng , QIANDanmin , JIANGKui , CHENYalan ,  WANGLi

Department of Medical Informatics, Nantong University, Nantong 226001, China;

Corresponding author：

WANGLi, Email: wangli@ntu.edu.cn

Keywords：

word co-occurrence; maximum matching method; mutual information; negation term detection

DOI：

10.7507/1001-5515.20150015

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The method for detecting the negative terms in Chinese electronic medical record (EMR) is useful in providing evidence for constructing concept index. In this respect, we adopted an improved method which combined maximum matching with mutual information in order to extract terms in EMRs. This method can overcome the influence of overlay ambiguity. In addition, for the determination of negative semantic, we also adopted an improved method which combined rule-based method with word co-occurrence. This new method can reduce the probability of appearance of false positive terms caused by punctuation input errors. The result showed that the negative predictive value is 7.85% higher than the rule-based method.

Citation： ZHANGYuanpeng, DONGJiancheng, QIANDanmin, JIANGKui, CHENYalan, WANGLi. Method of Detection of Negative Terminology in Chinese Electronic Medical Record. Journal of Biomedical Engineering, 2015, 32(1): 82-85. doi: 10.7507/1001-5515.20150015 Copy

1.	MUTALIK P G, DESHPANDE A, NADKARNI P M. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS[J]. J Am Med Inform Assoc, 2001, 8(6): 598-609.
2.	CHAPMAN W W, BRIDEWELL W, HANBURY P, et al. A simple algorithm for identifying negated findings and diseases in discharge summaries[J]. J Biomed Inform, 2001, 34(5): 301-310.
3.	HUANG Y, LOWE H J.A novel hybrid approach to automated negation detection in clinical radiology reports[J]. J Am Med Inform Assoc, 2007, 14(3): 304-311.
4.	CRUZ DÍAZ N P, MAÑA LÓPEZ M J, VÁZQUEZ J M, et al. A machine-learning approach to negation and speculation detection in clinical texts[J]. J Am Soc Inf Sci Tec, 2012, 63(7): 1398-1410.
5.	李昊旻, 李莹, 段会龙, 等.中文病历文档术语提取和否定检出方法[J].中国生物医学工程学报, 2008, 27(5): 79-84, 97.
6.	范雪莉, 冯海泓, 原猛.基于互信息的主成分分析特征选择算法[J].控制与决策, 2013, 28(6): 915-919.
7.	赵文清, 侯小可.基于词共现图的中文微博新闻话题识别[J].智能系统学报, 2012, 7(5): 444-449.

1. MUTALIK P G, DESHPANDE A, NADKARNI P M. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS[J]. J Am Med Inform Assoc, 2001, 8(6): 598-609.
2. CHAPMAN W W, BRIDEWELL W, HANBURY P, et al. A simple algorithm for identifying negated findings and diseases in discharge summaries[J]. J Biomed Inform, 2001, 34(5): 301-310.
3. HUANG Y, LOWE H J.A novel hybrid approach to automated negation detection in clinical radiology reports[J]. J Am Med Inform Assoc, 2007, 14(3): 304-311.
4. CRUZ DÍAZ N P, MAÑA LÓPEZ M J, VÁZQUEZ J M, et al. A machine-learning approach to negation and speculation detection in clinical texts[J]. J Am Soc Inf Sci Tec, 2012, 63(7): 1398-1410.
5. 李昊旻, 李莹, 段会龙, 等.中文病历文档术语提取和否定检出方法[J].中国生物医学工程学报, 2008, 27(5): 79-84, 97.
6. 范雪莉, 冯海泓, 原猛.基于互信息的主成分分析特征选择算法[J].控制与决策, 2013, 28(6): 915-919.
7. 赵文清, 侯小可.基于词共现图的中文微博新闻话题识别[J].智能系统学报, 2012, 7(5): 444-449.

Journal of Biomedical Engineering

Method of Detection of Negative Terminology in Chinese Electronic Medical Record

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