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作者:

Lin, Shaofu (Lin, Shaofu.) | Zhao, Yuanyuan (Zhao, Yuanyuan.) | Huang, Zhisheng (Huang, Zhisheng.)

收录:

CPCI-S EI

摘要:

As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75% and 93.33% respectively.

关键词:

Named entity recognition Conditional random field Electronic medical record for depression Time representation regular expression

作者机构:

  • [ 1 ] [Lin, Shaofu]Beijing Univ Technol, Beijing Inst Smart City, Beijing 100124, Peoples R China
  • [ 2 ] [Lin, Shaofu]Beijing Univ Technol, Fac Informat, Beijing 100124, Peoples R China
  • [ 3 ] [Zhao, Yuanyuan]Beijing Univ Technol, Fac Informat, Beijing 100124, Peoples R China
  • [ 4 ] [Huang, Zhisheng]Capital Med Univ, Adv Innovat Ctr Human Brain Protect, Beijing, Peoples R China
  • [ 5 ] [Huang, Zhisheng]Vrije Univ Amsterdam, Dept Comp Sci, NL-1081 HV Amsterdam, Netherlands

通讯作者信息:

  • [Huang, Zhisheng]Capital Med Univ, Adv Innovat Ctr Human Brain Protect, Beijing, Peoples R China;;[Huang, Zhisheng]Vrije Univ Amsterdam, Dept Comp Sci, NL-1081 HV Amsterdam, Netherlands

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来源 :

BRAIN INFORMATICS

ISSN: 0302-9743

年份: 2019

卷: 11976

页码: 149-158

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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