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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

https://doi.org/10.2196/44597
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition
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Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition
inferredcites →https://doi.org/10.1007/978-981-15-1956-7_14openalex-snapshot
Overview of CCKS 2018 Task 1: Named Entity Recognition in Chinese Electronic Medical Records
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Improving LSTM CRFs using character-based compositions for Korean named entity recognition
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Simple methods to overcome the limitations of general word representations in natural language processing tasks
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Character convolutions for Arabic Named Entity Recognition with Long Short-Term Memory Networks
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Improving the performance of dictionary-based approaches in protein name recognition
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Biomedical named entity recognition using two-phase model based on SVMs
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Feature selection techniques for maximum entropy based biomedical named entity recognition
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Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study
inferredcites →https://doi.org/10.1016/j.jbi.2017.05.002openalex-snapshot
Character-level neural network for biomedical named entity recognition
inferredcites →https://doi.org/10.1016/j.jbi.2019.103133openalex-snapshot
Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition
inferredcites →https://doi.org/10.1016/j.jbi.2019.103285openalex-snapshot
A two-stage deep learning approach for extracting entities and relationships from medical texts
inferredcites →https://doi.org/10.1016/j.jbi.2019.103289openalex-snapshot
Chinese clinical named entity recognition with radical-level feature and self-attention mechanism
inferredcites →https://doi.org/10.1016/j.jbi.2020.103435openalex-snapshot
Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge
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Chinese medical named entity recognition based on multi-granularity semantic dictionary and multimodal tree
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Incorporating multi-level CNN and attention mechanism for Chinese clinical named entity recognition
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Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research
inferredcites →https://doi.org/10.1080/13696998.2020.1717500openalex-snapshot
Treatment patterns and direct medical costs of metastatic colorectal cancer patients: a retrospective study of electronic medical records from urban China
inferredcites →https://doi.org/10.1093/bioinformatics/btz504openalex-snapshot
Towards reliable named entity recognition in the biomedical domain
inferredcites →https://doi.org/10.1093/bioinformatics/btz682openalex-snapshot
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
inferredcites →https://doi.org/10.1093/jamia/ocw180openalex-snapshot
Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts
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Enhancing clinical concept extraction with contextual embeddings
inferredcites →https://doi.org/10.1109/access.2018.2886311openalex-snapshot
GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures
inferredcites →https://doi.org/10.1109/access.2019.2920734openalex-snapshot
Named Entity Recognition From Biomedical Texts Using a Fusion Attention-Based BiLSTM-CRF
inferredcites →https://doi.org/10.1109/bibm.2018.8621360openalex-snapshot
Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
inferredcites →https://doi.org/10.1109/icbbe.2011.5779984openalex-snapshot
Named Entity Recognition from Biomedical Text Using SVM
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Pre-Training With Whole Word Masking for Chinese BERT
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inferredcites →https://doi.org/10.1145/3302425.3302465openalex-snapshot
A BiLSTM-CRF Method to Chinese Electronic Medical Record Named Entity Recognition
inferredcites →https://doi.org/10.1155/2017/9451342openalex-snapshot
Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews
inferredcites →https://doi.org/10.1186/1471-2105-7-s5-s11openalex-snapshot
NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition
inferredcites →https://doi.org/10.1186/1472-6947-15-s1-s9openalex-snapshot
Developing a hybrid dictionary-based bio-entity recognition technique
inferredcites →https://doi.org/10.1186/s12911-019-0980-zopenalex-snapshot
Improving clinical named entity recognition in Chinese using the graphical and phonetic feature
inferredcites →https://doi.org/10.1186/s12911-020-01241-8openalex-snapshot
Korean clinical entity recognition from diagnosis text using BERT
inferredcites →https://doi.org/10.1186/s12911-022-02059-2openalex-snapshot
Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT
inferredcites →https://doi.org/10.1504/ijdmb.2015.067954openalex-snapshot
Named entity recognition and classification in biomedical text using classifier ensemble
inferredcites →https://doi.org/10.18653/v1/n18-1202openalex-snapshot
Deep Contextualized Word Representations
inferredcites →https://doi.org/10.18653/v1/p19-1356openalex-snapshot
What Does BERT Learn about the Structure of Language?
inferredcites →https://doi.org/10.21437/interspeech.2012-65openalex-snapshot
LSTM neural networks for language modeling
inferredcites →https://doi.org/10.2196/19848openalex-snapshot
Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations
inferredcites →https://doi.org/10.2307/416104openalex-snapshot
The origin and early development of the Chinese writing system. By William G. Boltz. (American Oriental series, 78.) New Haven, CT: American Oriental Society, 1994. Pp. ix, 205. $47.50.
inferredcites →https://doi.org/10.3115/1073083.1073163openalex-snapshot
Named entity recognition using an HMM-based chunk tagger
inferredcites →https://doi.org/10.3115/1118958.1118965openalex-snapshot
Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain
inferredcites →https://doi.org/10.3115/v1/p15-2098openalex-snapshot
Radical Embedding: Delving Deeper to Chinese Radicals
inferredcites →https://doi.org/10.3934/mbe.2020197openalex-snapshot
A self-attention based neural architecture for Chinese medical named entity recognition
inferredcites →https://doi.org/10.4230/lipics.cp.2025.31openalex-snapshot
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
inferredcites →https://doi.org/10.4230/oasics.slate.2021.8openalex-snapshot
Bidirectional LSTM-CRF Models for Sequence Tagging

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multi-step Transfer Learning in Natural Language Processing for the Health Domain
inferred← citeshttps://doi.org/10.1109/ei259745.2023.10513309openalex-snapshot
A Semantic Understanding Method for Patent Text Based on Large Language Model
inferred← citeshttps://doi.org/10.1186/s12911-025-03257-4openalex-snapshot
A framework for normalized extraction of fine-grained traditional Chinese medicine symptom entities and relations
inferred← citeshttps://doi.org/10.1371/journal.pone.0311278openalex-snapshot
Target detection of helicopter electric power inspection based on the feature embedding convolution model
inferred← citeshttps://doi.org/10.2196/59782openalex-snapshot
Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study
inferred← citeshttps://doi.org/10.2196/preprints.51625openalex-snapshot
Practice of Improving Data Quality Based on the Intensity of Antimicrobial Drug Use (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.54346openalex-snapshot
Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study (Preprint)
inferred← citeshttps://doi.org/10.3389/frai.2024.1454945openalex-snapshot
Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation
inferred← citeshttps://doi.org/10.3390/ani15060822openalex-snapshot
Chinese Named Entity Recognition for Dairy Cow Diseases by Fusion of Multi-Semantic Features Using Self-Attention-Based Deep Learning
inferred← citeshttps://doi.org/10.3390/app14188471openalex-snapshot
Chinese Medical Named Entity Recognition Based on Context-Dependent Perception and Novel Memory Units
inferred← citeshttps://doi.org/10.36548/jaicn.2024.4.002openalex-snapshot
Detection of Twitter Fake News using Efficient Soft-Capsule and Improved BiGRU Architecture
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Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare
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