Predicting mother and newborn skin-to-skin contact using a machine learning approach.

Journal: BMC pregnancy and childbirth

PMID: 39966775

Abstract

BACKGROUND: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. The current study was designed to use machine learning (ML) to identify the predictors of SSC.

Authors

Sanaz Safarzadeh

Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Nastaran Safavi Ardabili

Department of Midwifery, Ardabil Branch, Islamic Azad University, Ardabil, Iran.
Mohammadsadegh Vahidi Farashah

Amirkabir University of Technology, Tehran, Iran.
Nasibeh Roozbeh

Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Fatemeh Darsareh

Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran. famadarsareh@yahoo.com.

Keywords

Adult Female Humans Infant, Newborn Kangaroo-Mother Care Method Machine Learning Mother-Child Relations Mothers Pregnancy

External Resources

View on PubMed Access via DOI PubMed (39966775)

Predicting mother and newborn skin-to-skin contact using a machine learning approach.

Abstract

Authors

Keywords

External Resources

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