Leveraging machine learning to study how temperament scores predict pre-term birth status.

ABSTRACT:

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 mul

SUBMITTER: Seamon E 

PROVIDER: S-EPMC11412316 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature