CLASSIFYING IMBALANCED DRUG-DRUG INTERACTION TECHNIQUE FROM BIOMEDICAL TEXT USING ENHANCED EMBEDDING TECHNIQUES
Author(s):
Saranya M
Keywords:
Drug-Drug Interaction, bidirectional gated recurrent unit layer, bidirectional transformer, Doc2Vec, multi-layer perceptron
Abstract
Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and healthcare. Although multiple DDI resources exist, it is becoming infeasible to maintain these up-to-date manually with the number of biomedical texts growing at a fast pace. Previous neural network based models have achieved good performance in DDIs extraction. However, most of the previous models did not make good use of the information of drug entity names, which can help to decide the relation between drugs. In this work, a novel neural network based model that employs multiple entity-aware attentions (with various entity information) to predict DDI from the biomedical literature. We use an output-modified bidirectional transformer (BioBERT) and a bidirectional gated recurrent unit layer (BiGRU) to obtain the vector representation of sentences. The vectors of drug description documents encoded by Doc2Vec are used as drug description information, which acts as an external knowledge of our model. Then we construct three different kinds of entity- aware attentions to get the sentence representations with entity information weighted, including attentions using the drug description information. The outputs of attention layers are concatenated and fed into a multi-layer perceptron (MLP) layer. Finally, we get the result by a softmax classifier. We evaluate our model on the DDI Extraction 2013 corpus benchmark dataset, using accuracy, precision, recall and F-score.
Article Details
Unique Paper ID: 156093

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 1030 - 1039
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