Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses.

Journal: Advances in neonatal care : official journal of the National Association of Neonatal Nurses

PMID: 38775675

Abstract

BACKGROUND: Early-life pain is associated with adverse neurodevelopmental consequences; and current pain assessment practices are discontinuous, inconsistent, and highly dependent on nurses' availability. Furthermore, facial expressions in commonly used pain assessment tools are not associated with brain-based evidence of pain.

Authors

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.
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.
Ralph Joseph
Priyansh Dadar

KaviGlobal, 1250 Grove St, Suite 300, Barrington, IL, USA. Electronic address: Priyansh.Dadar@kaviglobal.com.
Naomi Kaduwela

Keywords

Facial Expression Female Humans Infant, Newborn Intensive Care Units, Neonatal Male Neonatal Nursing Nurses, Neonatal Pain Pain Measurement Reproducibility of Results Retrospective Studies Supervised Machine Learning

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View on PubMed Access via DOI PubMed (38775675)

Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses.

Abstract

Authors

Keywords

External Resources

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