Self-fulfilling prophecies and machine learning in resuscitation science.

Journal: Resuscitation
PMID: 36306959

Abstract

INTRODUCTION: Growth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increases the chance that the outcome occurs.

Authors

  • Maria De-Arteaga
    Information, Risk and Operations Management Department, McCombs School of Business, University of Texas at Austin, Austin, TX, USA.
  • Jonathan Elmer
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Neurology Division, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: elmerjp@upmc.edu.

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