Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210491535> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4210491535 endingPage "e34" @default.
- W4210491535 startingPage "e28" @default.
- W4210491535 abstract "Abstract Background 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. Objectives Disambiguating clinical abbreviations is an essential task in information extraction from medical texts. Deep contextualized representations models showed promising results in most word sense disambiguation tasks. In this work, we propose a one-fits-all classifier to disambiguate clinical abbreviations with deep contextualized representation from pretrained language models like Bidirectional Encoder Representation from Transformers (BERT). Methods A set of experiments with different pretrained clinical BERT models were performed to investigate fine-tuning methods on the disambiguation of clinical abbreviations. One-fits-all classifiers were used to improve disambiguating rare clinical abbreviations. Results One-fits-all classifiers with deep contextualized representations from Bioclinical, BlueBERT, and MS_BERT pretrained models improved the accuracy using the University of Minnesota data set. The model achieved 98.99, 98.75, and 99.13%, respectively. All the models outperform the state-of-the-art in the previous work of around 98.39%, with the best accuracy using the MS_BERT model. Conclusion Deep contextualized representations via fine-tuning of pretrained language modeling proved its sufficiency on disambiguating clinical abbreviations; it could be robust for rare and unseen abbreviations and has the advantage of avoiding building a separate classifier for each abbreviation. Transfer learning can improve the development of practical abbreviation disambiguation systems." @default.
- W4210491535 created "2022-02-08" @default.
- W4210491535 creator A5009969418 @default.
- W4210491535 creator A5010249809 @default.
- W4210491535 date "2022-02-01" @default.
- W4210491535 modified "2023-09-30" @default.
- W4210491535 title "Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques" @default.
- W4210491535 cites W128190844 @default.
- W4210491535 cites W2001004455 @default.
- W4210491535 cites W2009790391 @default.
- W4210491535 cites W2076063813 @default.
- W4210491535 cites W2082673056 @default.
- W4210491535 cites W2095588973 @default.
- W4210491535 cites W2136847440 @default.
- W4210491535 cites W2345195116 @default.
- W4210491535 cites W2396881363 @default.
- W4210491535 cites W2436001372 @default.
- W4210491535 cites W2621398870 @default.
- W4210491535 cites W2886646161 @default.
- W4210491535 cites W2900049381 @default.
- W4210491535 cites W2911489562 @default.
- W4210491535 cites W2962739339 @default.
- W4210491535 cites W2966522979 @default.
- W4210491535 cites W2979860911 @default.
- W4210491535 cites W3005576497 @default.
- W4210491535 cites W3007595536 @default.
- W4210491535 cites W3212156961 @default.
- W4210491535 cites W4285719527 @default.
- W4210491535 doi "https://doi.org/10.1055/s-0042-1742388" @default.
- W4210491535 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35104909" @default.
- W4210491535 hasPublicationYear "2022" @default.
- W4210491535 type Work @default.
- W4210491535 citedByCount "6" @default.
- W4210491535 countsByYear W42104915352022 @default.
- W4210491535 countsByYear W42104915352023 @default.
- W4210491535 crossrefType "journal-article" @default.
- W4210491535 hasAuthorship W4210491535A5009969418 @default.
- W4210491535 hasAuthorship W4210491535A5010249809 @default.
- W4210491535 hasBestOaLocation W42104915351 @default.
- W4210491535 hasConcept C108583219 @default.
- W4210491535 hasConcept C111919701 @default.
- W4210491535 hasConcept C118505674 @default.
- W4210491535 hasConcept C119857082 @default.
- W4210491535 hasConcept C137293760 @default.
- W4210491535 hasConcept C148524875 @default.
- W4210491535 hasConcept C154945302 @default.
- W4210491535 hasConcept C204321447 @default.
- W4210491535 hasConcept C41008148 @default.
- W4210491535 hasConcept C95623464 @default.
- W4210491535 hasConceptScore W4210491535C108583219 @default.
- W4210491535 hasConceptScore W4210491535C111919701 @default.
- W4210491535 hasConceptScore W4210491535C118505674 @default.
- W4210491535 hasConceptScore W4210491535C119857082 @default.
- W4210491535 hasConceptScore W4210491535C137293760 @default.
- W4210491535 hasConceptScore W4210491535C148524875 @default.
- W4210491535 hasConceptScore W4210491535C154945302 @default.
- W4210491535 hasConceptScore W4210491535C204321447 @default.
- W4210491535 hasConceptScore W4210491535C41008148 @default.
- W4210491535 hasConceptScore W4210491535C95623464 @default.
- W4210491535 hasIssue "S 01" @default.
- W4210491535 hasLocation W42104915351 @default.
- W4210491535 hasLocation W42104915352 @default.
- W4210491535 hasLocation W42104915353 @default.
- W4210491535 hasOpenAccess W4210491535 @default.
- W4210491535 hasPrimaryLocation W42104915351 @default.
- W4210491535 hasRelatedWork W3014300295 @default.
- W4210491535 hasRelatedWork W3164822677 @default.
- W4210491535 hasRelatedWork W4223943233 @default.
- W4210491535 hasRelatedWork W4225161397 @default.
- W4210491535 hasRelatedWork W4250304930 @default.
- W4210491535 hasRelatedWork W4312200629 @default.
- W4210491535 hasRelatedWork W4360585206 @default.
- W4210491535 hasRelatedWork W4364306694 @default.
- W4210491535 hasRelatedWork W4380075502 @default.
- W4210491535 hasRelatedWork W4380086463 @default.
- W4210491535 hasVolume "61" @default.
- W4210491535 isParatext "false" @default.
- W4210491535 isRetracted "false" @default.
- W4210491535 workType "article" @default.