Incorporating machine learning and statistical methods to address maternal healthcare disparities in US: A systematic review.

Journal: International journal of medical informatics
PMID: 40245723

Abstract

BACKGROUND: Maternal health disparities are recognized as a significant public health challenge, with pronounced disparities evident across racial, socioeconomic, and geographic dimensions. Although healthcare technologies have advanced, these disparities remain primarily unaddressed, indicating that enhanced analytical approaches are needed.

Authors

  • Hala Al Sliti
    School of Systems Science and Industrial Engineering, Watson College of Engineering and Applied Science, SUNY Binghamton, Vestal, NY, United States. Electronic address: halslit1@binghamton.edu.
  • Ashaar Ismail Rasheed
    School of Systems Science and Industrial Engineering, Watson College of Engineering and Applied Science, SUNY Binghamton, Vestal, NY, United States.
  • Saumya Tripathi
    Department of Social Work, SUNY Binghamton, 67 Washington St Binghamton, NY 13902, United States.
  • Stephanie Tulk Jesso
    School of Systems Science and Industrial Engineering, Watson College of Engineering and Applied Science, SUNY Binghamton, Vestal, NY, United States.
  • Sreenath Chalil Madathil
    School of Systems Science and Industrial Engineering, Watson College of Engineering and Applied Science, SUNY Binghamton, Vestal, NY, United States.

Keywords

External Resources

Popular Topics
Recent Journals