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

The identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes.