MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

Abstract

One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.

EMNLP 2020 Clinical NLP

References in corpus (3)

• Neural Document Embeddings for Intensive Care Patient Mortality Prediction
• A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
• Training without training data: Improving the generalizability of automated medical abbreviation disambiguation