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Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation

Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation

Wang et al [72] proposed a model for NER based on the LSTM-CRF model by storing and merging characters, words, and other features. However, as the text embedding process of this method is more complicated, it is necessary to create dictionaries of characters and words to obtain multigranularity text features at first and then store and merge the obtained features using a tree structure to achieve text embedding.

Weijie Wang, Xiaoying Li, Huiling Ren, Dongping Gao, An Fang

JMIR Med Inform 2023;11:e44597