Interpretable gradient boosting machine model for predicting in-hospital mortality in sepsis-induced myocardial injury: a multicenter development, validation, and web-based clinical implementation - Top-Institutions - MDSpire

Interpretable gradient boosting machine model for predicting in-hospital mortality in sepsis-induced myocardial injury: a multicenter development, validation, and web-based clinical implementation

  • By

  • Lina Chen

  • Qianru Yuan

  • Yitong Ma

  • July 1, 2026

  • 0 min

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Top Institutions in Cardiology

Brief introduction explaining scope and methodology.

  • #1

    Medical Information Mart for Intensive Care-IV (MIMIC-IV)
    Medical Information Mart for Intensive Care-IV (MIMIC-IV)

    Boston, Massachusetts

    Key Differentiators

    • Intensive Care
    • Data Science

  • #2

    Beth Israel Deaconess Medical Center
    Beth Israel Deaconess Medical Center

    Boston, Massachusetts

    Key Differentiators

    • Cardiology
    • Critical Care

  • #3

    eICU Collaborative Research Database (eICU)
    eICU Collaborative Research Database (eICU)

    Boston, Massachusetts

    Key Differentiators

    • Intensive Care
    • Data Science

  • #4

    First Affiliated Hospital of Xinjiang Medical University
    First Affiliated Hospital of Xinjiang Medical University

    Urumqi, Xinjiang

    Key Differentiators

    • Cardiology
    • Critical Care

  • #5

    Massachusetts Institute of Technology
    Massachusetts Institute of Technology

    Cambridge, Massachusetts

    Key Differentiators

    • Engineering
    • Data Science

Original Source(s)

Interpretable gradient boosting machine model for predicting in-hospital mortality in sepsis-induced myocardial injury: a multicenter development, validation, and web-based clinical implementation

  • by Lina Chen, Qianru Yuan, Yitong Ma

  • July 1, 2026

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