Drug-Drug interaction extraction using a position and similarity fusion-based attention mechanism.

Taking multiple drugs at the same time can increase or decrease each drug's effectiveness or cause side effects. These drug-drug interactions (DDIs) may lead to an increase in the cost of medical care or even threaten patients' health and life. Thus, automatic extraction of DDIs is an important research field to improve patient safety. In this work, a deep neural network model is presented for extracting DDIs from medical texts. This model utilizes a novel attention mechanism for improving the discrimination of important words from others, based on the word similarities and their relative position with respect to candidate drugs. This approach is applied for calculating the attention weights for the outputs of a bi-directional long short-term memory (Bi-LSTM) model in the deep network structure before detecting the type of DDIs. The proposed method was tested on the standard DDI Extraction 2013 dataset and according to experimental results was able to achieve an F1-Score of 78.30 which is comparable to the best results reported for the state-of-the-art methods. A detailed study of the proposed method and its components is also provided.