Application of a human-centered design for embedded machine learning model to develop data labeling software with nurses: Human-to-Artificial Intelligence (H2AI).

Journal: International journal of medical informatics
PMID: 38199191

Abstract

BACKGROUND: Nurses are essential for assessing and managing acute pain in hospitalized patients, especially those who are unable to self-report pain. Given their role and subject matter expertise (SME), nurses are also essential for the design and development of a supervised machine learning (ML) model for pain detection and clinical decision support software (CDSS) in a pain recognition automated monitoring system (PRAMS). Our first step for developing PRAMS with nurses was to create SME-friendly data labeling software.

Authors

  • Naomi A Kaduwela
    KaviGlobal, 1250 Grove St, Suite 300, Barrington, IL, USA. Electronic address: Naomi.kaduwela@kaviglobal.com.
  • Susan Horner
    Ann & Robert H. Lurie Children's Hospital of Chicago, 255 E. Chicago Ave, Box 101, Chicago, IL, USA. Electronic address: SHorner@luriechildrens.org.
  • Priyansh Dadar
    KaviGlobal, 1250 Grove St, Suite 300, Barrington, IL, USA. Electronic address: Priyansh.Dadar@kaviglobal.com.
  • Renee C B Manworren
    Ann & Robert H. Lurie Children's Hospital of Chicago, 255 E. Chicago Ave, Box 101, Chicago, IL, USA; Northwestern University Feinberg School of Medicine, Department of Pediatrics, 255 E. Chicago Ave, Chicago, IL, USA. Electronic address: Renee.Manworren@northwestern.edu.

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