A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.

Journal: BMC medical informatics and decision making
Published Date: Mar 22, 2018

Abstract

BACKGROUND: Temporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts.

Authors

  • Tianyong Hao
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China. haoty@gdufs.edu.cn.
  • Xiaoyi Pan
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.
  • Zhiying Gu
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.
  • Yingying Qu
    School of Business, Guangdong University of Foreign Studies, Guangzhou, China. jessie.qu@gdufs.edu.cn.
  • Heng Weng
    Department of Big Medical Data, Health Construction Administration Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. ww128@qq.com.

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