IDEAS home Printed from https://ideas.repec.org/a/igg/jswis0/v19y2023i1p1-14.html
   My bibliography  Save this article

A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM

Author

Listed:
  • Xuewei Lai

    (College of Information and Media, Sanmenxia Polytechnic, Sanmenxia City, China)

  • Qingqing Jie

    (Laboratory Medicine Department, Sanmenxia Central Hospital, Sanmenxia City, China)

Abstract

Aiming at the problems of missing local context features, single word vector representation, and low entity recognition accuracy, a method for e-medical recording with named entity recognition, which is based on BERT and model fusion, is proposed. First, with the model of BERT for pre-training, the preceding and following contextual information is fused for the enhancement of word semantic representation and alleviation of the problem of polysemy; second, the network of bi-directional long-short term memory is for obtaining the sequence feature matrix, generation of optimal sequence in global sense achieved through the conditional random field model; finally, data enhancement is used to alleviate the class imbalance and improve the model ability in generalization. Results of the experiments find model proposal measured by F1 on CCKS21 data set reaches 0.8548, which is 0.51% and 0.08% higher than models with ID-CNNs-CRF and multi-task RNN. This demonstrates the excellent performance of the method proposed in this paper in improving named entity recognition.

Suggested Citation

  • Xuewei Lai & Qingqing Jie, 2023. "A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-14, January.
    • Handle: RePEc:igg:jswis0:v:19:y:2023:i:1:p:1-14
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.333711
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jswis0:v:19:y:2023:i:1:p:1-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.