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

Polymer of Intrinsic Microporosity-Derived Artificial SEI With Electronegative Sub-1-nm Channels for Robust Li-Metal Anodes.

Angewandte Chemie (International ed. in English)·2026
Same author

Effect of 37°C Pre-Warmed Versus Room Temperature Gadoxetic Acid Disodium Contrast Medium on Patient Comfort and Image Quality in Liver MRI: A Prospective, Double-Blind, Self-Controlled Before-and-After Study.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Mechanism of claudin-2 in RTECs apoptosis after renal obstruction.

Urolithiasis·2026
Same author

Multiomics prediction and immunogenic validation of personalized neoantigens in cholangiocarcinoma patients.

Journal of translational medicine·2026
Same author

Mitigating Intraoperative Fatigue in Surgeons at High Altitude: A Stepped-Wedge Cluster Randomized Trial.

High altitude medicine & biology·2026
Same author

Minimizing Implant Rejections through Low-Inflammatory and Immune-Regulatory Biointerfaces.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: Nov 14, 2025

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

8.5K

Predicting Recurrence for Patients With Ischemic Cerebrovascular Events Based on Process Discovery and Transfer

Haifeng Xu, Jianfei Pang, Weiliang Zhang

    IEEE Journal of Biomedical and Health Informatics
    |March 11, 2021
    PubMed
    Summary

    Predicting long-term ischemic cerebrovascular event (ICE) recurrence is challenging due to limited hospital data. This study introduces a novel framework using process mining and transfer learning to identify high-risk patients for intervention.

    More Related Videos

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    43.1K
    Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
    09:52

    Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

    Published on: January 15, 2017

    17.5K

    Related Experiment Videos

    Last Updated: Nov 14, 2025

    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

    8.5K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    43.1K
    Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
    09:52

    Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

    Published on: January 15, 2017

    17.5K

    Area of Science:

    • Medical Informatics
    • Machine Learning in Healthcare
    • Clinical Data Analysis

    Background:

    • Ischemic cerebrovascular events (ICE) recurrence leads to significant mortality and disability.
    • Traditional machine learning models struggle with predicting ICE recurrence due to insufficient labeled hospital follow-up data.

    Purpose of the Study:

    • To develop a novel framework for predicting long-term ICE recurrence risk after hospital discharge.
    • To identify high-risk patients for timely intervention by overcoming data limitations.

    Main Methods:

    • Utilized process mining on clinical guidelines to discover process models and extract control flow as patient characteristics.
    • Employed transfer learning, specifically instance filter and weight-based methods, using in-hospital data (target domain) and national stroke screening data (source domain).
    • Validated the framework on 205 tertiary hospital cases and 2954 screening cohort cases (2015-2017).

    Main Results:

    • The proposed framework demonstrated improved performance for three instance-based transfer learning algorithms.
    • Successfully leveraged process mining and transfer learning to enhance the prediction of ICE recurrence risk.
    • Addressed the limitation of insufficient labeled follow-up data in clinical settings.

    Conclusions:

    • The developed framework offers a comprehensive and efficient approach for predicting long-term ICE recurrence.
    • Process mining and transfer learning integration can effectively mitigate data scarcity issues in predicting recurrent cerebrovascular events.
    • This method facilitates proactive patient management and intervention for individuals at high risk of ICE recurrence.