Joint Models for Extracting Adverse Drug Events from Biomedical Text

Extracting adverse drug events receives much research attention in the biomedical community. Previous work adopts pipeline models, firstly recognizing drug/disease entity mentions and then identifying adverse drug events from drug/disease pairs. In this paper, we investigate joint models for simultaneously extracting drugs, diseases and adverse drug events. Compared with pipeline models, joint models have two main advantages. First, they make use of information integration to facilitate performance improvement; second, they reduce error propagation in pipeline methods. We compare a discrete model and a deep neural model for extracting drugs, diseases and adverse drug events jointly. Experimental results on a standard ADE corpus show that the discrete joint model outperforms a state-of-the-art baseline pipeline significantly. In addition, when discrete features are replaced by neural features, the recall is further improved.