Search academic texts

Information × Registration Number 2119U006537, Article popup.category Препринт Title Detection of Difficult for Understanding Medical Words using Deep Learning (AI translated) popup.author Pylieva HannaPylieva Hanna popup.publication 01-01-2019 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/1336 popup.publisher Description In the medical domain, non-specialized users often require a better understanding of medical information provided by doctors. In this work, we address this need. We introduce novel embeddings received from RNN - FrnnMUTE (French RNN Medical Understandability Text Embeddings) - and show how they help to improve identification of readability and understandability of medical words when applied as features in the classification task, reaching at maximum 87.0 F1 score. We also found out that adding pre-trained FastText word embeddings to the feature set substantially improves the performance of the classification model. For generalizability study of different models, we introduce a methodology comprising three crossvalidation scenarios which allow testing classifiers in real-world conditions: when understanding of medical words by new users is unknown or when no information about understandability of new words is provided for the model. popup.nrat_date 2025-11-05 Close