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Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques

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URI: https://hdl.handle.net/10016/38282
ISSN: 00261270 (ISSN)
DOI: https://doi.org/10.1055/s-0042-1742388
UXXI: AR/0000031755
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disambiguating_MIM_2022.pdf (876.29 KB)
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2022-02-01
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Thieme
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Abbreviations are considered an essential part of the clinical narrative; they are used not only to save time and space but also to hide serious or incurable illnesses. Misreckoning interpretation of the clinical abbreviations could affect different aspects concerning patients themselves or other services like clinical support systems. There is no consensus in the scientific community to create new abbreviations, making it difficult to understand them. Disambiguate clinical abbreviations aim to predict the exact meaning of the abbreviation based on context, a crucial step in understanding clinical notes
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natural language processing, clinical abbreviations, pretrained language model, word sense disambiguation, electronic health record
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Jaber, A., Martínez, P. Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques. Methods of Information Medicine 2022; 61:e28-e34. https://doi.org/10.1055/s-0042-1742388
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