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An Automated Approach for Clinical Quantitative Information Extraction from Chinese Electronic Medical Records

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Book cover Health Information Science (HIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11148))

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Abstract

Clinical quantitative information commonly exists in electronic medical records (EMRs) and is essential for recording patients’ lab test or other characteristics in clinical notes. This study proposes an automated approach for extracting quantitative information from Chinese free-text EMR data including admission records, progress notes and ward-inspection records. The approach leverages pattern-learning combining with rule-based strategy to identify and extract clinical quantitative expressions. The experiments are based on 1,359 de-identified EMRs from the burn department of a domestic Grade-A Class-three hospital. The evaluation results present that our approach achieves a precision of 96.1%, a recall of 90.9%, and an F1-measure of 92.9%, demonstrating its effectiveness in clinical quantitative information extraction from EMR text.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61772146), Innovative School Project in Higher Education of Guangdong Province (No. YQ2015062), and Guangzhou Science Technology and Innovation Commission (No. 201803010063).

Author information

Authors and Affiliations

  1. School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China

    Shanshan Liu, Xiaoyi Pan, Boyu Chen & Dongfa Gao

  2. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

Authors
  1. Shanshan Liu
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  2. Xiaoyi Pan
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  3. Boyu Chen
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  4. Dongfa Gao
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  5. Tianyong Hao
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Corresponding authors

Correspondence to Dongfa Gao or Tianyong Hao .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. James Cook University, Cairns, QLD, Australia

    Ickjai Lee

  3. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Footscray, VIC, Australia

    Hua Wang

  6. James Cook University, Cairns, QLD, Australia

    Wei Xiang

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Liu, S., Pan, X., Chen, B., Gao, D., Hao, T. (2018). An Automated Approach for Clinical Quantitative Information Extraction from Chinese Electronic Medical Records. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-01078-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01077-5

  • Online ISBN: 978-3-030-01078-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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