Abstract
Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge’13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models.
Highlights
Drug-drug interactions (DDIs) may occur when two or more drugs are co-administered, and the effects of the combined drugs can be increased, weakened, or harmful
The first focuses on detecting positive DDIs in all possible pairs of drugs and the second focuses on the multi-class type classifier of each positive DDI pair of one of the following four types: advice, effect, mechanism, and int
After receiving a parsed input sentence to train our model, we look up the pretrained word embedding to map each input word to real-valued vectors
Summary
Drug-drug interactions (DDIs) may occur when two or more drugs are co-administered, and the effects of the combined drugs can be increased, weakened, or harmful. It is known that such DDI events may cause preventable drug related harm [1]. Several databases such as DrugBank [2], PharmGKB [3], Drugs.com [4] and Stockley’s Drug Interactions [5] collect known adverse events caused by DDIs. Usually, human experts manually collect DDI information from various sources such as the FDA’s Adverse Event Reporting System [6]. Since there are numerous combinations of drugs available, it is difficult to collect all the DDI events of patients from reports or publications. Manually organizing DDI information in natural language into a DDI database is costly and time-consuming
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