Combining Traditional Statistical Methods with Explainable Machine Learning for Assessing Recurrence Risk of Atrial Fibrillation Following Pulsed Field Ablation: A Hypothesis-Generating Study from a Single Center - Top-Institutions - MDSpire

Combining Traditional Statistical Methods with Explainable Machine Learning for Assessing Recurrence Risk of Atrial Fibrillation Following Pulsed Field Ablation: A Hypothesis-Generating Study from a Single Center

  • By

  • Haoqing Ren

  • Hengli Lai

  • March 13, 2026

  • 0 min

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Top Institutions in Cardiology

Brief introduction explaining scope and methodology.

  • #1

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Cardiac Electrophysiology
    • Atrial Fibrillation Ablation
    • Machine Learning in Cardiology

  • #2

    Cleveland Clinic
    Cleveland Clinic

    Cleveland, OH

    Key Differentiators

    • Cardiac Electrophysiology
    • Atrial Fibrillation Ablation
    • Clinical Cardiology Research

  • #3

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Cardiac Electrophysiology
    • Biomedical Informatics
    • Machine Learning in Medicine

  • #4

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Cardiac Electrophysiology
    • Artificial Intelligence in Medicine
    • Cardiovascular Research

  • #5

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Cardiac Electrophysiology
    • Clinical Cardiology
    • Data Science in Medicine

Original Source(s)

Combining Traditional Statistical Methods with Explainable Machine Learning for Assessing Recurrence Risk of Atrial Fibrillation Following Pulsed Field Ablation: A Hypothesis-Generating Study from a Single Center

  • by Haoqing Ren, Hengli Lai

  • March 13, 2026

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