An adverse drug effect mentions extraction method based on weighted online recurrent extreme learning machine. (July 2019) - El-allaly, Ed-drissiya; Sarrouti, Mourad; En-Nahnahi, Noureddine; Ouatik El Alaoui, Said
Highlights: We design a weighted online recurrent extreme learning machine based method to identify ADE mentions from biomedical texts. We propose a character-level embedding with a modified OR-ELM to capture morphological features. We evaluate the impact of five well-known segments to encode multi-word mentions. The proposed method outperforms the state-of-the-art ones on the ADE corpus. Graphical abstract: Abstract: Background and objective: Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has attracted significant attention from the pharmacovigilance and biomedical text mining communities. Indeed, deep learning based methods have recently been employed to solve this issue with great success. However, they fail to effectively identify the boundary of mentions. In this paper, we propose a weighted online recurrent extreme learning machine (WOR-ELM) based method to overcome this drawback. Methods: The proposed method for ADE mentions extraction from biomedical texts is divided into two stages: span detection and ADE mentions classification. At the first stage, we identify the boundary of the mentions irrespective of their types with a WOR-ELM in a given sentence. At the second stage, another WOR-ELM is used to classify the identified mentions to the appropriate type. Both stages use the concatenation of character-level and word-level embeddings as features. The character-level embedding is obtained using a modified online recurrent extreme learning machine, whereas the word-level embedding is obtained from a pre-trained model. Results: Several experiments were carried out on a well-known ADE corpus to evaluate the effectiveness and demonstrate the usefulness of the proposed method. The obtained results show that our method achieves an F-score of 87.5%, which outperforms the current state-of-the-art methods. Conclusions: Our research results indicate that the proposed method for adverse drug effect mentions extraction from text can significantly improve performance over existing methods. Our experiments show the effectiveness of incorporating word-level and character level embeddings as features for WOR-ELM. They also illustrate the benefits of using IOU segment to represent ADE mentions.