Expert-augmented machine learning for predicting extubation readiness in the pediatric intensive care unit.

Journal: BMC medical informatics and decision making
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Determining extubation readiness in pediatric intensive care units (PICU) is challenging. We used expert-augmented machine learning (EAML), a method that combines machine learning with human expert knowledge, to predict successful extubation.

Authors

  • Jean Digitale
    Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. Jean.digitale@ucsf.edu.
  • Deborah Franzon
    Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, CA, USA.
  • Jin Ge
    Department of Medicine, Division of Gastroenterology and Hepatology, University of California-San Francisco, San Francisco, California, USA.
  • Charles McCulloch
    Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
  • Mark J Pletcher
    Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
  • Efstathios D Gennatas
    Department of Radiation Oncology, University of California, San Francisco, CA 94115.

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