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

• Main Authors: Erich Seamon (Author), Jennifer.A. Mattera (Author), Sarah.A. Keim (Author), Esther.M. Leerkes (Author), Jennifer.L. Rennels (Author), Andrea.J. Kayl (Author), Kirsty.M. Kulhanek (Author), Darcia Narvaez (Author), Sarah.M. Sanborn (Author), Jennifer.B. Grandits (Author), Christine Dunkel Schetter (Author), Mary Coussons-Read (Author), Amanda.R. Tarullo (Author), Sarah.J. Schoppe-Sullivan (Author), Moriah.E. Thomason (Author), Julie.M. Braungart-Rieker (Author), Julie.C. Lumeng (Author), Shannon.N. Lenze (Author), Lisa M. Christian (Author), Darby.E. Saxbe (Author), Laura.R. Stroud (Author), Christina.M. Rodriguez (Author), Stephanie Anzman-Frasca (Author), Maria.A. Gartstein (Author)
• Format: Book
• Published: Elsevier, 2024-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

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 stud...