Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks

As one of the most important medical field subjects, adverse drug reaction seriously affects the patient’s life, health, and safety. Although many methods have been proposed, there are still plenty of important adverse drug reactions unknown, due to the complexity of the detection process. Social media, such as medical forums and social networking services, collects a large amount of drug use information from patients, and so is important for adverse drug reaction mining. However, most of the existing studies only involved a single source of data. This study automatically crawls the information published by users of the MedHelp Medical Forum. Then combining it with disease-related user posts which obtained from Twitter. We combine different word embeddings and utilize a multi-channel convolutional neural network to deal with the challenge that encountered in data representation of multiple sources, and further identify text containing adverse drug reaction information. In particular, in this process, to enable the model to take advantage of the morphological and shape information of words, we use a convolutional channel to learn the features from character-level embeddings of words. The experiment results show that the proposed method improved the representation of words and thus effectively detects adverse drug reactions from text.