Journal of biomedical informatics
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Identifying medical persona from a social media post is critical for drug marketing, pharmacovigilance and patient recruitment. Medical persona classification aims to computationally model the medical persona associated with a social media post. We present a novel deep learning model for this task which consists of two parts: Convolutional Neural Networks (CNNs), which extract highly relevant features from the sentences of a social media post and average pooling, which aggregates the sentence embeddings to obtain task-specific document embedding. ⋯ We eliminate the need for manual labeling by employing a distant supervision based method to obtain labeled examples for training the models. We thoroughly analyze our model to discover cues that are indicative of a particular persona. Particularly, we use first derivative saliency to identify the salient words in a particular social media post.