Detecting Drug-Drug Interaction (DDI) over the Social Media using Convolution Neural Network Deep Learning

Abstract

Drug-Drug Interaction (DDI) detection is a challenging problem for drug manufacturers, drug regulatory authorities, and medical professionals alike. It is impossible to run trials or be aware of every single case involving an entire population. Research in the use of social media data is currently gaining attention, and with the application of machine learning techniques has been successfully applied in businesses. This paper presents a project extracting DDI from biomedical text using a Convolutional Neural Network (CNN) classifier. The classifier is trained on the SemEval 2013 DDIExtraction challenge dataset and aims to automatically learn the best feature representation on the input of the given task. Different models have been proposed, which make use of position embeddings in combination with word embeddings trained on the machine learning model to learn features. Word embeddings are necessary for providing dense vector representation of words that can be trained, but a large amount of data is required to train an effective vector representation of words. To compensate for the lack shortage of data, the CNN model is trained on a pre-trained PubMed word embedding, which provides a vector dimension of size 200 for the representation of each word. This project aims to provide a trained CNN model for which vector representation of words is provided by weights that have been trained for medical text classification purposes.