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

Autor:	Seamon E; University of Idaho Department of Design and Environments, 875 Perimeter Drive MS 2481, Moscow, Idaho 83844-2481, United States., Mattera JA; Washington State University, Department of Psychology, P.O. Box 644820, Pullman WA 99164-4820, United States., Keim SA; Nationwide Children's Hospital & The Ohio State University, Center for Biobehavioral Health, Abigail Wexner Research Institute 700 Children's Drive, Columbus OH 43205, United States., Leerkes EM; University of North Carolina Greensboro, P.O. Box 26170, Greensboro NC 27402-6170, United States., Rennels JL; University of Nevada, Las Vegas, 4505 S. Maryland Way, Las Vegas, NV 89154, United States., Kayl AJ; University of Nevada, Las Vegas, 4505 S. Maryland Way, Las Vegas, NV 89154, United States., Kulhanek KM; University of Nevada, Las Vegas, 4505 S. Maryland Way, Las Vegas, NV 89154, United States., Narvaez D; University of Notre Dame, 390 Corbett, Notre Dame IN 46556, United States., Sanborn SM; Clemson University, College of Behavioral, Social and Health Sciences, 116 Edwards Hall, Clemson South Carolina 29634, United States., Grandits JB; Clemson University, College of Behavioral, Social and Health Sciences, 116 Edwards Hall, Clemson South Carolina 29634, United States., Schetter CD; University of California, Los Angeles, Department of Psychology, 1285 Franz Hall, Box 951563, Los Angeles CA 90095, United States., Coussons-Read M; University of Colorado-Colorado Springs Psychology Department, Columbine Hall, 1420 Austin Bluffs Pkwy, Colorado Springs CO 80918, United States., Tarullo AR; Boston University, Department of Psychological & Brain Sciences 64 Cummington Mall, Room 149 Boston, Massachusetts 02215, United States., Schoppe-Sullivan SJ; The Ohio State University, 243 Psychology Building, 1835 Neil Ave, Columbus OH, 43210, United States., Thomason ME; New York University, Langone One Park Ave, New York, NY 10016, United States., Braungart-Rieker JM; Colorado State University, Human Development and Family Studies, College of Health and Human Sciences, 1570 Campus Delivery, Fort Collins, CO, 80523-1501, United States., Lumeng JC; University of Michigan Medical School, Division of Developmental and Behavioral Pediatrics, 1600 Huron Parkway, Building 520, Ann Arbor, Michigan, 48109, United States., Lenze SN; Washington University School of Medicine Institute for Public Health, 660 S. Euclid, MSC 8217-0094-02, St. Louis MO 63110, United States., Christian LM; The Ohio State University Wexner Medical Center, 460 Medical Center Drive, Columbus, OH 43210, United States., Saxbe DE; University of Southern California, 3616 Trousdale Parkway, AHF 108, Los Angeles, CA 90089-0376, United States., Stroud LR; Department of Psychiatry and Human Behavior Warren Alpert Medical School, Brown University, Coro West, Suite 309, 164 Summit Avenue, Providence, RI 02906, United States., Rodriguez CM; Old Dominion University, 115 Hampton Blvd, Norfolk, VA 23529, United States., Anzman-Frasca S; University at Buffalo Jacobs School of Medicine and Biomedical Sciences Division of Behavioral Medicine, G56 Farber Hall, 3435 Main Street, Buffalo New York 14214, United States., Gartstein MA; Washington State University, Department of Psychology, P.O. Box 644820, Pullman WA 99164-4820, United States.
Jazyk:	angličtina
Zdroj:	Global pediatrics [Glob Pediatr] 2024 Sep; Vol. 9. Date of Electronic Publication: 2024 Jul 22.
DOI:	10.1016/j.gpeds.2024.100220
Abstrakt:	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. Subjects: Participants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity. Outcome Measures: Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein. Results and Conclusions: Accuracy estimates were generally similar regardless of the comparison groups. Results indicated a slightly higher accuracy and efficiency for IBQR-VSF item-based models vs. factor-level models. Divergent patterns of feature importance (i.e., the extent to which a factor/item contributed to classification) were observed for the two comparison groups (chronological age vs. adjusted age) using factor-level scores; however, itemized models indicated that the two most critical items were associated with effortful control and negative emotionality regardless of comparison group. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Databáze:	MEDLINE
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