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Sequence Labeling for Extracting Relevant Pieces of Information from Raw Text Medicine Descriptions

  • Radu Razvan SlavescuEmail author
  • Constantin Masca
  • Kinga Cristina Slavescu
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 71)

Abstract

In Natural Language Processing, Named Entity Recognition aims to delimit and appropriately label the chunks of text containing a specific information. The paper presents the preliminary results we obtained by using a Conditional Random Fields approach for extracting information of interest from drug prescriptions. So far, our model was trained to extract the amount of medicine, measuring unit, frequency of administration, treatment duration and the treatment beneficiary condition. The model was trained using a corpus of drug prescriptions constructed and annotated by hand. The results obtained so far indicate the CRF model we developed performs well, scoring a 91% F1 score on the test set.

Keywords

Named entity recognition Conditional random fields Drug prescriptions Information extraction 

Notes

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Radu Razvan Slavescu
    • 1
    Email author
  • Constantin Masca
    • 1
  • Kinga Cristina Slavescu
    • 2
  1. 1.Department of Computer ScienceTechnical University of Cluj-NapocaCluj-NapocaRomania
  2. 2.Department Mother and Child“Iuliu Hatieganu” University of Medicine and PharmacyCluj-NapocaRomania

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