Enriching Pre-Trained Language Model with Multi-Task Learning and Context for Medical Concept Normalization

Abstract

Herein, we focus on the problem of automatically medical concept normalization in social media posts. Specifically, the task is to map medical mentions within social media texts to the suitable concepts in a reference knowledge base. We propose a new medical concept normalization model using multi-task learning. The model uses BioBERT to encode mentions and their contexts, and classifies their concept IDs and types of mention. We evaluate our approach on two datasets and achieve new state-of-the-art performance.