Use of machine learning models to identify National Institutes of Health-funded cardiac arrest research.

Journal: Resuscitation
PMID: 39961491

Abstract

OBJECTIVE: To compare the performance of three artificial intelligence (AI) classification strategies against manually classified National Institutes of Health (NIH) cardiac arrest (CA) grants, with the goal of developing a publicly available tool to track CA research funding in the United States.

Authors

  • Ryan A Coute
    Department of Emergency Medicine, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, United States. Electronic address: rcoute@uabmc.edu.
  • Kameshwari Soundararajan
    Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, United States. Electronic address: ksoundararajan@uabmc.edu.
  • Michael C Kurz
    Section of Emergency Medicine, Department of Medicine, University of Chicago, Chicago, IL, United States. Electronic address: mkurz@uchicago.edu.
  • Ryan L Melvin
    a Department of Physics , Wake Forest University , Winston-Salem, NC , USA.
  • Ryan C Godwin
    Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America.

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