Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.

Journal: Frontiers in public health
PMID: 38332940

Abstract

INTRODUCTION: Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Authors

  • Hanin B Afzal
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
  • Tasfia Jahangir
    Department of Behavioral, Social and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA, United States.
  • Yiyang Mei
    School of Law, Emory University, Atlanta, GA, United States.
  • Annabelle Madden
    Teachers College, Columbia University, New York, NY, United States.
  • Abeed Sarker
    Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
  • Sangmi Kim
    Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States.

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