DeepCADRME: A deep neural model for complex adverse drug reaction mentions extraction

• Published in: Pattern recognition letters 2021-03, Vol.143, p.27-35
• Main Authors: El-allaly, Ed-drissiya, Sarrouti, Mourad, En-Nahnahi, Noureddine, Ouatik El Alaoui, Said
• Format: Article
• Language: English
• Subjects: Adverse drug reaction; Complex mentions; Deep bidirectional transformer; Knowledge management; N-level model; N-level tagging sequence; Natural language processing; Pharmacovigilance
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2020.12.013

•We propose a deep neural model for complex ADR mentions extraction called DeepCADRME.•DeepCADRME casts the problem as an N-level tagging sequence.•It integrates an N-level model based on deep bidirectional transformer.•The obtained results are significant compared with state-of-the-art systems. Extracting mentions of Adverse Drug Reaction (ADR) from biomedical texts, aiming to support pharmacovigilance and drug safety surveillance, remains a challenging task as many ADR mentions are nested, discontinuous and overlapping. To solve these issues, in this paper, we propose a deep neural model for Complex Adverse Drug Reaction Mentions Extraction, called DeepCADRME. It first transforms the ADR mentions extraction problem as an N-level tagging sequence. Then, it feeds the sequences to an N-level model based on contextual embeddings where the output of the pre-trained model of the current level is used to build a new deep contextualized representation for the next level. This allows the DeepCADRME system to transfer knowledge between levels. Experimental results performed on the TAC 2017 ADR dataset, show the effectiveness of DeepCADRME which leads to a new state-of-the-art performance by reaching a F1 of 85.35% and 85.41% with and without mention types, respectively. The evaluation results also highlight the benefits of exploring language model to effectively extract different types of ADR mentions.