At EMNLP 2020 (Empirical Methods in Natural Language Processing) Conference, a Montreal-based research team introduced a large medical text dataset designed to improve medical abbreviation disambiguation.
Correct terminology and related deep learning models for various tasks have a significant role in medicine and healthcare. However, there has been a lack of publicly available pre-training data in this field due to privacy restrictions and an overabundance of non-standard abbreviations. The patient-safety organization, Institute for Safe Medical Practices (ISMP), has listed more than 55,000 medical abbreviations that may not be interpreted correctly.
The researchers from McGill University, Facebook CIFAR AI Chair, and Mila – Quebec Artificial Intelligence Institute recently introduced MeDAL. MeDAL: Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding helps resolve all the contradictory, ambiguous, and potentially dangerous abbreviations in the medical and healthcare field. An example of what it does is shown below.