Drug drug interaction extraction from the literature using a recursive neural network

Sangrak Lim, Kyubum Lee, Jaewoo Kang, Jinn-Moon Yang
2018 PLoS ONE  
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
more » ... 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. OPEN ACCESS Citation: Lim S, Lee K, Kang J (2018) Drug drug interaction extraction from the literature using a recursive neural network. PLoS ONE 13(1): e0190926. https://doi.org/10.
doi:10.1371/journal.pone.0190926 pmid:29373599 pmcid:PMC5786304 fatcat:vjd6wcwf4bhdhj6pa7k7cqpkue