Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies.

Journal: Computer methods and programs in biomedicine
PMID: 39226843

Abstract

BACKGROUND: This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally.

Authors

  • Ying Dai
    School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai ,200092, China.
  • Alison M Buttenheim
    School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA.
  • Jennifer A Pinto-Martin
    School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Peggy Compton
    School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA.
  • Sara F Jacoby
    School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA.
  • Jianghong Liu
    School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA. Electronic address: jhliu@nursing.upenn.edu.

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