Medical Information Extraction from Social Media

Abstract

Medical social media plays a crucial role in several applications such as studying the unintended effects of a drug (pharmacovigilance), hiring potential participants for a clinical trial, promoting a drug, monitor public health and healthcare delivery. In this thesis, we first address the problem of medical persona classification which refers to computationally identifying the medical persona associated with a particular medical social media post. We formulate this as a supervised multi-class text classification task and propose a neural model for it. In order to minimize the human labeling effort, we propose a distant supervision based approach to heuristically obtain labeled examples which can be used for training the model. newline newlineWe also address the task of medical concept normalization, which aims to map concept mentions such as quotnot able to sleepquot to their corresponding medical concepts such as quotInsomniaquot. We propose neural models which are capable of mapping any concept mention to its corresponding medical concept in standard medical vocabularies such as SNOMED CT. There are several challenges associated with existing methods for normalizing medical concept mentions. First, creating training data is effort intensive. Secondly, existing models fail to map a mention to target concepts which were not encountered during the training phase. Thirdly, current models have to be retrained from scratch whenever new concepts are added to the target lexicon. We propose a neural model which overcomes these limitations. newline newlineLastly, we address the task of medical text simplification. Most medical information on the web is tailored to an expert audience, due to which people with inadequate health literacy often find it difficult to access, comprehend, and act upon this information. Medical text simplification aims to alleviate this problem by computationally simplifying medical text. We propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text.