Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study - Top-Institutions - MDSpire

Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study

  • By

  • Anoeska Schipper

  • Peter Belgers

  • Rory David O'Connor

  • Lieke van de Wouw

  • Luc Builtjes

  • Joeran S Bosma

  • Ron Kusters

  • Steef Kurstjens

  • Matthieu Rutten

  • Bram van Ginneken

  • April 30, 2026

  • 0 min

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Top Institutions in Emergency Medicine

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Emergency Medicine
    • Clinical Informatics
    • Artificial Intelligence

  • #2

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Emergency Medicine
    • Biomedical Informatics
    • Machine Learning

  • #3

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Emergency Medicine
    • Biomedical Informatics
    • Artificial Intelligence

  • #4

    University of Amsterdam - Amsterdam UMC
    University of Amsterdam - Amsterdam UMC

    Amsterdam, North Holland

    Key Differentiators

    • Emergency Medicine
    • Clinical Informatics
    • Natural Language Processing

  • #5

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Emergency Medicine
    • Health Informatics
    • Machine Learning

Original Source(s)

Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study

  • by Anoeska Schipper, Peter Belgers, Rory David O'Connor, Lieke van de Wouw, Luc Builtjes, Joeran S Bosma, Ron Kusters, Steef Kurstjens, Matthieu Rutten, Bram van Ginneken

  • April 30, 2026

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