Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets

Understand

Medical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text.

• Most of the existing methods adopt a three-step approach of (1) detecting mentions, (2) generating a list of candidate concepts, and finally (3) picking the best concept among them.
• In this paper, we probe into alleviating the problem of overgeneration of candidate concepts in the candidate generation module, the most under-studied component of medical entity linking.
• For this, we present MedType, a fully modular system that prunes out irrelevant candidate concepts based on the predicted semantic type of an entity mention.