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A Bootstrapping Approach to Symptom Entity Extraction on Chinese Electronic Medical Records

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD 2016, CCL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10035))

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Abstract

Symptom entities are widely distributed in Chinese electronic medical records. Previous approaches on symptom entity extraction usually extract continuous strings as symptom entities and require massive human efforts on corpus annotation. We describe the symptom entity as two-tuples of <subject, lesion> and design a soft pattern matching method to locate them in sentences in the EMR. Our bootstrapping approach which only requires a few annotated symptom tuples and it allows iterative extraction from mass electronic medical record databases without human supervision. Furthermore, the described method annotates symptom entities in EMR by the extracted tuples. Starting with 60 annotated entities, our approach reached an F value of 81.40 % in the extraction task of 3,150 entities from 992 sets of electronic medical records.

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Authors and Affiliations

  1. Web Intelligence Lab, Research Center of Language Technology, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Tianyi Qin & Yi Guan

Authors
  1. Tianyi Qin
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  2. Yi Guan
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Corresponding author

Correspondence to Tianyi Qin .

Editor information

Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Maosong Sun

  2. Fudan University , Shanghai, China

    Xuanjing Huang

  3. Dalian University of Technology , Dalian, China

    Hongfei Lin

  4. Tsinghua University , Beijing, China

    Zhiyuan Liu

  5. Tsinghua University , Beijing, China

    Yang Liu

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© 2016 Springer International Publishing AG

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Qin, T., Guan, Y. (2016). A Bootstrapping Approach to Symptom Entity Extraction on Chinese Electronic Medical Records. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_34

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  • DOI: https://doi.org/10.1007/978-3-319-47674-2_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47673-5

  • Online ISBN: 978-3-319-47674-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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