Clinical Abbreviation Disambiguation Using Deep Contextualized Representation.

The objective of this study is to develop a method for clinical abbreviation disambiguation using deep contextualized representation and cluster analysis. We employed the pre-trained BioELMo language model to generate the contextualized word vector for abbreviations within each instance. Then principal component analysis was conducted on word vectors to reduce the dimension. K-Means cluster analysis was conducted for each abbreviation and the sense for a cluster was assigned based on the majority vote of annotations. Our method achieved an average accuracy of around 95% in 74 abbreviations. Simulation showed that each cluster required the annotation of 5 samples to determine its sense.