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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

Li et al [70] proposed an ELMo-Lattice-LSTM-CRF model. The ELMo word dynamic representation model could learn complicated word features and the context-based changes of these features, while the lattice structure provided extra entity boundaries and other semantic information for CNER of EMRs through the Word2 Vec model and dictionaries. Li et al [41] proposed an ELMo-Bi LSTM-CRF model that improved the semantic recognition ability of the machine for text.

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

JMIR Med Inform 2023;11:e44597

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