期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 86, 期 -, 页码 15-24出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2018.08.005
关键词
DDI extraction; Long short-term memory network; Attention model
The simultaneous administration of multiple drugs increases the probability of interaction among them, as one drug may affect the activities of others. This interaction among drugs may have a positive or negative impact on the therapeutic outcomes. Thus, identification of unknown drug-drug interactions (DDIs) is of significant concern for improving the safety and efficacy of drug consumption. Although multiple DDI resources exist, it is becoming infeasible to maintain these up-to-date manually with the number of biomedical texts growing at a fast pace. Most existing methods model DDI extraction as a classification problem and rely mainly on handcrafted features, and certain features further depend on domain-specific tools. Recently, neural network models using latent features have been demonstrated to yield similar or superior performance compared to existing models. In this study, we present three long short-term memory (LSTM) network models, namely B-LSTM, AB-LSTM, and Joint AB-LSTM. All three models use word and position embedding as latent features; thus, they do not rely on explicit feature engineering. Furthermore, the use of a bidirectional LSTM (Bi-LSTM) network allows for extraction of implicit features from an entire sentence. The two models AB-LSTM and Joint AB-LSTM also apply attentive pooling in the Bi-LSTM layer output in order to assign weights to features. Our experimental results on the SemEval-2013 DDI extraction dataset indicate that the Joint AB-LSTM model produces reasonable performance (F-score: 69.39%) even with the simple architecture.
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