Adverse Drug Reaction Mentions Extraction from Drug Labels: An Experimental Study

  • Ed-drissiya El-allalyEmail author
  • Mourad Sarrouti
  • Noureddine En-Nahnahi
  • Said Ouatik El Alaoui
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)


Adverse Drug Reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are a serious health problem and a leading cause of death. Therefore, it is of vital importance to identify ADRs properly and in a timely manner from drug labels. In this paper, we explore both machine learning and deep learning approaches in extracting adverse reaction mentions and modifier terms such as negation, severity, and drug class from drug labels. We investigated Conditional Random Fields (CRF) as a machine learning method, and both Recurrent Neural Network (RNN) and Bidirectional Recurrent Neural Network (Bi-RNN) as deep learning methods. These methods are widely used in biomedical named entity recognition. Experimental evaluations performed on the publicly available datasets SPL-ADR-200db, provided by the TAC 2017 ADRs challenge, show that Bi-RNN achieves good performances compared with RNN and CRF. Bi-RNN outperforms RNN and CRF by an average of 4% and 4.7% in terms of F1-score, respectively.


Adverse Drug Reaction Recurrent Neural Network Bidirectional Recurrent Neural Network Conditional Random Fields Biomedical Named Entity Recognition Natural Language Processing 



The authors would like to thank the TAC 2017 ADRs challenge [6] organizers who provided the datasets used in this study for evaluating ADR mentions extraction methods.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ed-drissiya El-allaly
    • 1
    Email author
  • Mourad Sarrouti
    • 1
  • Noureddine En-Nahnahi
    • 1
  • Said Ouatik El Alaoui
    • 1
  1. 1.Laboratory of Informatics and Modeling, FSDMSidi Mohammed Ben Abdellah UniversityFezMorocco

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