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

Hybrid multilayer perceptron models optimized by evolutionary algorithms for urban air quality forecasting: a case study of Shiraz, Iran.

Scientific reports·2026
Same author

Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability.

Journal of public health research·2026
Same author

A Verifiable Framework for Brain Tumor Classification: Combining Vision Transformers, Class-Weighted Learning, and SMT-Based Formal Decision Traces.

Diagnostics (Basel, Switzerland)·2026
Same author

Prediction of landslide susceptibility through ANN models optimized by evolutionary algorithms.

Scientific reports·2026
Same author

Interpretable Adaptive Graph Fusion Network for Mortality and Complication Prediction in ICUs.

Diagnostics (Basel, Switzerland)·2025
Same author

Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models.

Biomedicines·2025

Related Experiment Video

Updated: Aug 5, 2025

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

2.8K

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data.

Mehmet Akif Cifci1,2,3, Sadiq Hussain4, Peren Jerfi Canatalay5

  • 1The Institute of Computer Technology, Tu Wien University, 1040 Vienna, Austria.

Diagnostics (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study introduces a new method using Generative Adversarial Networks (GANs) to improve the extraction of cancer information from electronic health records. The approach enhances data for better analysis of tumor characteristics.

Keywords:
electronic medical recordsjoint extractionmedical event extractiontransfer learning

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Related Experiment Videos

Last Updated: Aug 5, 2025

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

2.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Oncology Data Extraction

Background:

  • Electronic health records (EHRs) are widely used, increasing the need for automated information extraction.
  • Extracting specific oncological medical events from EHRs presents unique challenges due to data complexity.
  • Accurate extraction of tumor characteristics is crucial for cancer research and patient care.

Purpose of the Study:

  • To develop a novel approach for enhancing the extraction of tumor-related medical events from EHRs.
  • To improve the transfer learning capabilities of models for diverse oncological data using Generative Adversarial Networks (GANs).
  • To address the challenges in accurately identifying and extracting critical tumor information.

Main Methods:

  • A two-stage process involving data pre-processing (cleansing, normalization) and model training.
  • Utilizing Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation.
  • Applying pseudo-data generation algorithms to enhance model transfer learning for various tumor types.

Main Results:

  • Demonstrated promising results in extracting key information such as primary tumor site size, tumor size, and metastatic site details.
  • Successfully augmented data using GANs and pseudo-data generation, improving model performance.
  • Validated the approach on the i2b2/UTHealth 2010 dataset.

Conclusions:

  • The proposed method effectively extracts vital oncological medical event information from EHRs.
  • GAN-based data augmentation and pseudo-data generation significantly improve model performance for tumor-related data.
  • This approach holds substantial implications for advancing healthcare and medical research through enhanced EHR data utilization.