Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients - Top-Institutions - MDSpire

Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients

  • By

  • Houming Zhao

  • Lu Tang

  • Zhuoran Li

  • Xintao Li

  • Tongyu Jia

  • Jin Luo

  • Yujie Dong

  • Shangwei Li

  • Xin Ma

  • Peng Zhang

  • September 15, 2025

  • 0 min

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Top Institutions in Urology

Brief introduction explaining scope and methodology.

  • #1

    National Institutes of Health (NIH) - National Cancer Institute
    National Institutes of Health (NIH) - National Cancer Institute

    Bethesda, MD

    Key Differentiators

    • Endocrine Oncology
    • Surgical Oncology
    • Computational Medicine

  • #2

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Endocrine Surgery
    • Anesthesiology
    • Machine Learning in Medicine

  • #3

    Chinese PLA General Hospital (First Medical Center)
    Chinese PLA General Hospital (First Medical Center)

    Beijing, China

    Key Differentiators

    • Endocrine Surgery
    • Anesthesiology
    • Clinical Data Science

  • #4

    Cleveland Clinic
    Cleveland Clinic

    Cleveland, OH

    Key Differentiators

    • Endocrine Surgery
    • Anesthesiology
    • Biomedical Informatics

  • #5

    Massachusetts General Hospital (MGH)
    Massachusetts General Hospital (MGH)

    Boston, MA

    Key Differentiators

    • Endocrinology
    • Anesthesiology
    • Data Science

Original Source(s)

Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients

  • by Houming Zhao, Lu Tang, Zhuoran Li, Xintao Li, Tongyu Jia, Jin Luo, Yujie Dong, Shangwei Li, Xin Ma, Peng Zhang

  • September 15, 2025

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