Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings
This work addresses the time-consuming task of pharmacovigilance monitoring for healthcare professionals, but it is incremental as it builds on existing deep learning approaches with specific enhancements.
The paper tackled the problem of detecting adverse drug reactions (ADRs) in biomedical literature by evaluating convolutional neural networks (CNNs) with biomedical word embeddings, showing improved performance over traditional and LSTM models and reducing overoptimism through de-duplication of sentences.
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 in different forms has been the subject of many recent works. One particular area that has shown promise is the use of Deep Neural Networks, in particular, Convolutional Neural Networks (CNNs), for the detection of ADR relevant sentences. Using token-level convolutions and general purpose word embeddings, this architecture has shown good performance relative to more traditional models as well as Long Short Term Memory (LSTM) models. In this work, we evaluate and compare two different CNN architectures using the ADE corpus. In addition, we show that by de-duplicating the ADR relevant sentences, we can greatly reduce overoptimism in the classification results. Finally, we evaluate the use of word embeddings specifically developed for biomedical text and show that they lead to a better performance in this task.