Home > Archive > 2021 > Volume 11 Number 4 (July 2021) >
IJMLC 2021 Vol.11(4): 267-273 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.4.1046

Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text

Wen-Juan Hou and Bamfa Ceesay

Abstract—Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their efficacy or cause adverse effects. Recent research trends have shown several adaptation of recurrent neural networks (RNNs) from text. In this study, we highlight significant challenges of using RNNs in biomedical text processing and propose automatic extraction of DDIs aiming at overcoming some challenges. Our results show that the system is competitive against other systems for the task of extracting DDIs.

Index Terms—Drug–drug interaction, deep learning, embedding, machine learning.

The authors are with the Department of Computer Science and Information Engineering, National Taiwan Normal University. No. 88, Tingzhou Road, Sec. 4, Taipei 116, Taiwan (e-mail: emilyhou@csie.ntnu.edu.tw, bmfceesay@csie.ntnu.edu.tw).


Cite: Wen-Juan Hou and Bamfa Ceesay, "Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text," International Journal of Machine Learning and Computing vol. 11, no. 4, pp. 267-273, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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