Extracting medical entities from social media

Abstract

Accurately extracting medical entities from social media is challenging because people use informal language with different expressions for the same concept, and they also make spelling mistakes. Previous work either focused on specific diseases (e.g., depression) or drugs (e.g., opioids) or, if working with a wide-set of medical entities, only tackled individual and small-scale benchmark datasets (e.g., AskaPatient). In this work, we first demonstrated how to accurately extract a wide variety of medical entities such as symptoms, diseases, and drug names on three benchmark datasets from varied social media sources, and then also validated this approach on a large-scale Reddit dataset.