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Monitoring the biomedical literature for cases of Adverse Drug Reactions(ADRs) is a critically important and time consuming task in pharmacovigilance.The development of computer assisted approaches to aid this process indifferent forms has been the subject of many recent works. One particular areathat has shown promise is the use of Deep Neural Networks, in particular,Convolutional Neural Networks (CNNs), for the detection of ADR relevantsentences. Using token-level convolutions and general purpose word embeddings,this architecture has shown good performance relative to more traditionalmodels as well as Long Short Term Memory (LSTM) models. In this work, weevaluate and compare two different CNN architectures using the ADE corpus. Inaddition, we show that by de-duplicating the ADR relevant sentences, we cangreatly reduce overoptimism in the classification results. Finally, we evaluatethe use of word embeddings specifically developed for biomedical text and showthat they lead to a better performance in this task.

Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings