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. 2022 Jun 2;31(1):243–253. doi: 10.1055/s-0042-1742510

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IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ )

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.

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Table 1 — Summary of bedside NLP application studies. BART = Bidirectional Auto-Regressive Transformer, BERT = Bidirectional Encoder Representations of Transformers, CNN = Convolutional Neural Network, EL = Entity Linking, GBDT = Gradient Boosting Decision Tree, LSTM = Long Short-Term Memory, NER = Named Entity Recognition, NN = Neural Network, RCT = Randomized Controlled Trial, RoBERTa = Robustly Optimized BERT Pretraining Approach, SVM = Support Vector Machine, T5 = Text-to-Text Transfer Transformer