IDEAS home Printed from https://ideas.repec.org/a/igg/jkss00/v12y2021i2p69-87.html
   My bibliography  Save this article

Learning Pattern Relation-Based Hyperbolic Embedding for Adverse Drug Reaction Extraction

Author

Listed:
  • Siriwon Taewijit

    (Sirindhorn International Institute of Technology, Thammasat University, Thailand)

  • Thanaruk Theeramunkong

    (Sirindhorn International Institute of Technology, Thammasat University, Thailand)

Abstract

Hyperbolic embedding has been recently developed to allow us to embed words in a Cartesian product of hyperbolic spaces, and its efficiency has been proved in several works of literature since the hierarchical structure is the natural form of texts. Such a hierarchical structure exhibits not only the syntactic structure but also semantic representation. This paper presents an approach to learn meaningful patterns by hyperbolic embedding and then extract adverse drug reactions from electronic medical records. In the experiments, the public source of data from MIMIC-III (Medical Information Mart for Intensive Care III) with over 58,000 observed hospital admissions of the brief hospital course section is used, and the result shows that the approach can construct a set of efficient word embeddings and also retrieve texts of the same relation type with the input. With the Poincaré embeddings model and its vector sum (PC-S), the authors obtain up to 82.3% in the precision at ten, 85.7% in the mean average precision, and 93.6% in the normalized discounted cumulative gain.

Suggested Citation

  • Siriwon Taewijit & Thanaruk Theeramunkong, 2021. "Learning Pattern Relation-Based Hyperbolic Embedding for Adverse Drug Reaction Extraction," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 12(2), pages 69-87, April.
  • Handle: RePEc:igg:jkss00:v:12:y:2021:i:2:p:69-87
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKSS.2021040105
    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:jkss00:v:12:y:2021:i:2:p:69-87. 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.