Abstract
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.
Keywords
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Acknowledgment
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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El-allaly, Ed., Sarrouti, M., En-Nahnahi, N., El Alaoui, S.O. (2019). Adverse Drug Reaction Mentions Extraction from Drug Labels: An Experimental Study. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-11884-6_21
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