A Lucrative Model for Identifying Potential Adverse Effects from Biomedical Texts by Augmenting BERT and ELMo

Abstract

This study copes with extracting adverse effects (AEs) from biomedical texts. An adverse effect is a noxious, unintended, and undesired effect caused by the administration of an external entity such as medication, dietary supplement, radiotherapy, and others. A binary classifier is proposed to filter out irrelevant texts from AE assertive texts and a sequence labeling model for extracting the AE mentions. Both models are built by consolidating the cutting-edge deep learning technologies: Bidirectional Encoder Representations from Transformers (BERT), Embeddings from Language Models (ELMo), and Bidirectional Gated Recurrent Units. The performances of our models are evaluated on an Adverse Drug Effects dataset constructed by sampling from Medline case studies. Both models perform significantly better than previously published models with an F1 score of 0.906 for binary classification and an approximate match F1 score of 0.925 for text labeling. The proposed models can be adapted to any tasks with similar interests.