A multimodal embedding model for sepsis data representation - Top-Institutions - MDSpire

A multimodal embedding model for sepsis data representation

  • By

  • Tuo Liu

  • Yonglin Li

  • Hongyi Chen

  • Naiqing Li

  • Yan Zhang

  • Xuanqi Huang

  • Jin Wang

  • Rui Chen

  • Yuping Zeng

  • Yuntao Liu

  • Danwen Zheng

  • Darong Wu

  • Changdong Wang

  • Tao Yu

  • Xiaotu Xi

  • Zhongde Zhang

  • February 23, 2026

  • 0 min

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Top Institutions in Infectious Disease

Brief introduction explaining scope and methodology.

  • #1

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Critical Care Medicine
    • Biomedical Informatics
    • Machine Learning

  • #2

    Massachusetts General Hospital / Harvard Medical School
    Massachusetts General Hospital / Harvard Medical School

    Boston, MA

    Key Differentiators

    • Critical Care Medicine
    • Biomedical Informatics
    • Artificial Intelligence

  • #3

    University of Pittsburgh Medical Center (UPMC)
    University of Pittsburgh Medical Center (UPMC)

    Pittsburgh, PA

    Key Differentiators

    • Critical Care Medicine
    • Biomedical Informatics
    • Data Science

  • #4

    University of California, San Francisco (UCSF)
    University of California, San Francisco (UCSF)

    San Francisco, CA

    Key Differentiators

    • Critical Care Medicine
    • Biomedical Informatics
    • Machine Learning

  • #5

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Critical Care Medicine
    • Biomedical Informatics
    • Clinical Research

Original Source(s)

A multimodal embedding model for sepsis data representation

  • by Tuo Liu, Yonglin Li, Hongyi Chen, Naiqing Li, Yan Zhang, Xuanqi Huang, Jin Wang, Rui Chen, Yuping Zeng, Yuntao Liu, Danwen Zheng, Darong Wu, Changdong Wang, Tao Yu, Xiaotu Xi, Zhongde Zhang

  • February 23, 2026

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