HealthE: Classifying Entities in Online Textual Health Advice
This work addresses the problem of classifying entities in online health advice for medical NLP systems, which is incremental as it builds on existing NER methods with a new dataset and model.
The authors tackled the under-representation of entities in medical NLP for public health texts by releasing HealthE, a new annotated dataset of 6,756 health advice entries with a more granular label space, and introduced EP S-BERT, a model that achieved a 4-point F1 increase over the nearest baseline and a 34-point F1 increase over off-the-shelf tools.
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.