September 2024: Our work that connects machine learning with pre-term birth status outcomes is now published in the journal, Global Pediatrics. This work is in conjunction with over 20 differing researchers worldwide.
Background: Preterm birth (birth at <37 completed weeks gestation) is a significant public heatlh concern worldwide. Important health, and developmental consequences of preterm birth include altered temperament development, with greater dysregulation and distress proneness. Aims: The present study leveraged advanced quantitative techniques, namely machine learning approaches, to discern the contribution of narrowly defined and broadband temperament dimensions to birth status classification (full-term vs. preterm). Along with contributing to the literature addressing temperament of infants born preterm, the present study serves as a methodological demonstration of these innovative statistical techniques. Study design: This study represents a metanalysis conducted with multiple samples (N = 19) including preterm (n = 201) children and (n = 402) born at term, with data combined across investigations to perform classification analyses.
Seamon et al, “Leveraging machine learning to study how temperament scores predict pre-term birth status” Global Pediatrics September 2024 https://doi.org/10.1016/j.gpeds.2024.100220