Predictive Interpretability of Machine Learning Models for In-Hospital Bleeding Risk Following PCI in Diabetic Patients with Acute Coronary Syndrome: A Retrospective Analysis - Top-Institutions - MDSpire

Predictive Interpretability of Machine Learning Models for In-Hospital Bleeding Risk Following PCI in Diabetic Patients with Acute Coronary Syndrome: A Retrospective Analysis

  • By

  • Huasheng Lv

  • Ruotong Cao

  • Yuchen Zhang

  • Fengyu Sun

  • Yitong Ma

  • Xinrong Zhou

  • February 20, 2026

  • 0 min

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Top Institutions in Cardiology

Brief introduction explaining scope and methodology.

  • #1

    Cleveland Clinic
    Cleveland Clinic

    Cleveland, OH

    Key Differentiators

    • Cardiology
    • Interventional Cardiology
    • Clinical Data Science

  • #2

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Cardiology
    • Endocrinology
    • Biomedical Informatics

  • #3

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Cardiology
    • Diabetes Research
    • Data Science

  • #4

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Cardiology
    • Diabetes
    • Artificial Intelligence in Medicine

  • #5

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Cardiology
    • Diabetes
    • Clinical Informatics

Original Source(s)

Predictive Interpretability of Machine Learning Models for In-Hospital Bleeding Risk Following PCI in Diabetic Patients with Acute Coronary Syndrome: A Retrospective Analysis

  • by Huasheng Lv, Ruotong Cao, Yuchen Zhang, Fengyu Sun, Yitong Ma, Xinrong Zhou

  • February 20, 2026

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