Drug-drug interaction extraction from biomedical literature using support vector machine and long short term memory networks

Since Drug-drug interactions (DDIs) can cause adverse effects when patients take two or more drugs and therefore increase health care costs, the extraction of DDIs is an important research area in patient safety. To improve the performance of Drug-drug interaction extraction (DDIE), we present a novel two-stage method in this paper. It first identifies the positive instances using a feature based binary classifier, and then a Long Short Term Memory (LSTM) based classifier is used to classify the positive instances into specific category. The experimental results show that the two-stage method has many advantages over one-stage ones, and among the factors related to LSTM, we find that the two layer bidirectional LSTM embedded with word, distance and Part-of-Speech obtains the highest F-score of 69.0%, which is state-of-the-art.