  The processing of entities in natural language is essential to many medical
NLP systems. Unfortunately, existing datasets vastly under-represent the
entities required to model public health relevant texts such as health advice
often found on sites like WebMD. People rely on such information for personal
health management and clinically relevant decision making. In this work, we
release a new annotated dataset, HealthE, consisting of 6,756 health advice.
HealthE has a more granular label space compared to existing medical NER
corpora and contains annotation for diverse health phrases. Additionally, we
introduce a new health entity classification model, EP S-BERT, which leverages
textual context patterns in the classification of entity classes. EP S-BERT
provides a 4-point increase in F1 score over the nearest baseline and a
34-point increase in F1 when compared to off-the-shelf medical NER tools
trained to extract disease and medication mentions from clinical texts. All
code and data are publicly available on Github.
