MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
This dataset addresses the problem of limited public data for developing NLP methods in clinical settings, benefiting researchers and practitioners in medical NLP.
The authors created MeDAL, a large medical text dataset for abbreviation disambiguation, to address the lack of public datasets for clinical NLP. Pre-training models on MeDAL improved performance and convergence speed when fine-tuning on downstream medical tasks.
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.