Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparison of rat hypertrophic scar models: Caudal tension model with superior pathological consistency.

Animal models and experimental medicine·2026
Same author

Impact of evidence-based nursing interventions on postoperative recovery and complications after endovascular aneurysm intervention: A retrospective cohort study.

Medicine·2026
Same author

Assessing the efficacy and ecological impact of a plastic-dissolving agent on microplastic removal and soil microbial communities.

Ecotoxicology and environmental safety·2026
Same author

Ambient PM<sub>2.5</sub> exposure and gestational diabetes mellitus: Evidence from an instrumental variable analysis in a prospective birth cohort study in China.

Ecotoxicology and environmental safety·2026
Same author

BK channel deficiency promotes lipolysis via an AKT-independent activation of the cAMP/PKA/HSL pathway.

Journal of lipid research·2026
Same author

Predictors of in-hospital mortality in patients with multiple wasp stings in southwest China.

Frontiers in toxicology·2026

Related Experiment Video

Updated: Nov 26, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.2K

Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.

Lejun Gong1,2, Zhifei Zhang1, Shiqi Chen1

  • 1Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanijing 210023, China.

Journal of Healthcare Engineering
|December 10, 2020
PubMed
Summary

This study introduces a deep learning model for Chinese clinical entity recognition, improving the identification of diseases, symptoms, drugs, and operations in electronic medical records.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

231

Related Experiment Videos

Last Updated: Nov 26, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.2K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

231

Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Chinese electronic medical records present unique challenges for clinical named entity recognition, including complex entities and incomplete sentences.
  • Obtaining a comprehensive corpus of Chinese electronic medical records is a significant hurdle.

Purpose of the Study:

  • To develop an effective Chinese clinical entity recognition model using deep learning pretraining.
  • To address the specific linguistic features and data scarcity issues in Chinese electronic medical records.

Main Methods:

  • Utilized domain-specific word embeddings and fine-tuned a pretrained entity recognition model.
  • Employed BiLSTM and Transformer architectures as feature extractors.
  • Identified four key clinical entity types: diseases, symptoms, drugs, and operations.

Main Results:

  • Achieved Macro-P of 75.06%, Macro-R of 76.40%, and Macro-F1 of 75.72% on the test dataset.
  • Demonstrated the effectiveness of the deep learning pretraining approach.

Conclusions:

  • The proposed Chinese clinical entity recognition model significantly enhances recognition performance.
  • Deep learning pretraining offers a viable solution for mining Chinese electronic medical records.