This paper is in the following e-collection/theme issue:

Machine Learning (1465) Artificial Intelligence (1371) Clinical Informatics (1022)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44597, first published .
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

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

Authors of this article:

Weijie Wang1 Author Orcid Image ;   Xiaoying Li1 Author Orcid Image ;   Huiling Ren1 Author Orcid Image ;   Dongping Gao1 Author Orcid Image ;   An Fang1 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

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