Evaluation of coronary heart disease risk prediction based on simple physical examination parameters by machine learning model: a retrospective cohort model development and validation study - Top-Institutions - MDSpire

Evaluation of coronary heart disease risk prediction based on simple physical examination parameters by machine learning model: a retrospective cohort model development and validation study

  • By

  • Hui Xiong

  • Xiang Cao

  • Xiao Han

  • Jia-Xing Zhang

  • Jia-Rui Zhuang

  • Shuai He

  • Min Zhu

  • Ji Li

  • Wei Qin

  • April 30, 2026

  • 0 min

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Top Institutions in Neurology

Brief introduction explaining scope and methodology.

  • #1

    Framingham Heart Study, Boston University
    Framingham Heart Study, Boston University

    Boston, MA

    Key Differentiators

    • Cardiology
    • Cardiovascular Epidemiology
    • Machine Learning in Medicine

  • #2

    Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine

    Baltimore, MD

    Key Differentiators

    • Cardiology
    • Biomedical Informatics
    • Epidemiology

  • #3

    Cleveland Clinic
    Cleveland Clinic

    Cleveland, OH

    Key Differentiators

    • Cardiology
    • Cardiovascular Imaging
    • Clinical Research

  • #4

    Massachusetts General Hospital, Harvard Medical School
    Massachusetts General Hospital, Harvard Medical School

    Boston, MA

    Key Differentiators

    • Cardiology
    • Biomedical Data Science
    • Preventive Medicine

  • #5

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Cardiology
    • Artificial Intelligence in Medicine
    • Epidemiology

Original Source(s)

Evaluation of coronary heart disease risk prediction based on simple physical examination parameters by machine learning model: a retrospective cohort model development and validation study

  • by Hui Xiong, Xiang Cao, Xiao Han, Jia-Xing Zhang, Jia-Rui Zhuang, Shuai He, Min Zhu, Ji Li, Wei Qin

  • April 30, 2026

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