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Abbreviation disambiguation is important for automated clinical noteprocessing due to the frequent use of abbreviations in clinical settings.Current models for automated abbreviation disambiguation are restricted by thescarcity and imbalance of labeled training data, decreasing theirgeneralizability to orthogonal sources. In this work we propose a novel dataaugmentation technique that utilizes information from related medical concepts,which improves our model's ability to generalize. Furthermore, we show thatincorporating the global context information within the whole medical note (inaddition to the traditional local context window), can significantly improvethe model's representation for abbreviations. We train our model on a publicdataset (MIMIC III) and test its performance on datasets from different sources(CASI, i2b2). Together, these two techniques boost the accuracy of abbreviationdisambiguation by almost 14% on the CASI dataset and 4% on i2b2.

Training without training data: Improving the generalizability of automated medical abbreviation disambiguation