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 Experiment Video

Updated: Apr 18, 2026

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
06:16

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

3.2K

A leakage-controlled machine learning framework for postprandial triglyceride phenotyping using synthetic clinical

Nattakitti Piyavechvirat1, Yi-Jheng Huang1,2, Qazi Mazhar Ul Haq3,4

  • 1International Bachelor Program in Informatics, Yuan Ze University, Taouan, Taiwan.

Scientific Reports
|April 16, 2026
PubMed
Summary

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

AI-Driven fetal distress monitoring SDN-IoMT networks.

PloS one·2025
Same author

Hierarchical graph-based integration network for propaganda detection in textual news articles on social media.

Scientific reports·2025
Same author

DeepCGAN: early Alzheimer's detection with deep convolutional generative adversarial networks.

Frontiers in medicine·2024
Same author

A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition.

Sensors (Basel, Switzerland)·2022
Same author

Continual Learning Strategy in One-Stage Object Detection Framework Based on Experience Replay for Autonomous Driving Vehicle.

Sensors (Basel, Switzerland)·2020
Same author

Geometric and Textural Blending for 3D Model Stylization.

IEEE transactions on visualization and computer graphics·2017

Whole blood viscosity (WBV) did not predict short-term triglyceride response in a synthetic cohort. This study highlights a privacy-preserving machine learning framework for metabolic research, emphasizing the need for real-world validation.

Area of Science:

  • Cardiovascular disease research
  • Metabolic health and disease
  • Biomedical data science

Background:

  • Whole blood viscosity (WBV) is linked to cardiometabolic risk.
  • The association between WBV and short-term postprandial triglyceride (TG) response is not well understood.
  • A novel, privacy-preserving machine learning (ML) framework was developed to investigate this relationship.

Purpose of the Study:

  • To assess the predictive value of WBV for short-term postprandial TG response.
  • To demonstrate a rigorously leakage-controlled ML framework for evaluating physiological associations.
  • To explore candidate physiological associations in a synthetic cohort.

Main Methods:

  • Utilized a statistically reconstructed, de-identified synthetic cohort.
Keywords:
BioinformaticsCalibration analysisCardiometabolic riskExplainable artificial intelligence (XAI)Leakage-controlled modelingMachine learningPostprandial metabolismTriglyceride responseWhole blood viscosity

More Related Videos

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

8.9K

Related Experiment Videos

Last Updated: Apr 18, 2026

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
06:16

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

3.2K
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

8.9K
  • Implemented a leakage-controlled ML pipeline with fold-specific preprocessing and probability calibration.
  • Employed nested cross-validation, bootstrap uncertainty estimation, and SHAP for interpretability.

    • Main Results:

    • WBV showed negligible association with postprandial TG response in correlation and modeling analyses.
    • Fasting triglycerides (TG) demonstrated stable monotonic effects and clear phenotype discrimination.
    • A logistic regression model achieved stable discrimination (AUROC 0.91) with consistent calibration.

    Conclusions:

    • WBV lacks reproducible predictive value for short-term postprandial TG response within this synthetic framework.
    • The study validates a privacy-preserving, calibration-aware ML workflow for metabolic research.
    • External validation in real-world cohorts is essential to confirm these findings.