Leveraging machine learning to study how temperament scores predict pre-term birth status
•Considered links between preterm birth status and temperament development.•Leveraged machine learning to discern birth status (full- vs pre-term) classification.•Provided a methodological demonstration of these innovative statistical techniques.•Two items most critical to accurate classification in...
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| Published in | Global pediatrics Vol. 9; p. 100220 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.09.2024
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2667-0097 2667-0097 |
| DOI | 10.1016/j.gpeds.2024.100220 |
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| Abstract | •Considered links between preterm birth status and temperament development.•Leveraged machine learning to discern birth status (full- vs pre-term) classification.•Provided a methodological demonstration of these innovative statistical techniques.•Two items most critical to accurate classification involved distress and regulation.
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.
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.
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.
Participants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity.
Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein.
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. |
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| AbstractList | 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.BackgroundPreterm 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.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.AimsThe 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.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.Study designThis 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.Participants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity.SubjectsParticipants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity.Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein.Outcome measuresInfant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein.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.Results and conclusionsAccuracy 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. 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. 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. This study represents a metanalysis conducted with multiple samples ( = 19) including preterm ( = 201) children and ( = 402) born at term, with data combined across investigations to perform classification analyses. Participants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity. Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein. 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. •Considered links between preterm birth status and temperament development.•Leveraged machine learning to discern birth status (full- vs pre-term) classification.•Provided a methodological demonstration of these innovative statistical techniques.•Two items most critical to accurate classification involved distress and regulation. 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. 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. 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. Participants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity. Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein. 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. 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. |
| ArticleNumber | 100220 |
| Author | Schoppe-Sullivan, Sarah.J. Anzman-Frasca, Stephanie Keim, Sarah.A. Thomason, Moriah.E. Lumeng, Julie.C. Coussons-Read, Mary Seamon, Erich Braungart-Rieker, Julie.M. Rennels, Jennifer.L. Christian, Lisa M. Gartstein, Maria.A. Sanborn, Sarah.M. Mattera, Jennifer.A. Stroud, Laura.R. Tarullo, Amanda.R. Lenze, Shannon.N. Leerkes, Esther.M. Grandits, Jennifer.B. Narvaez, Darcia Rodriguez, Christina.M. Kulhanek, Kirsty.M. Schetter, Christine Dunkel Kayl, Andrea.J. Saxbe, Darby.E. |
| AuthorAffiliation | j Boston University, Department of Psychological & Brain Sciences 64 Cummington Mall, Room 149 Boston, Massachusetts 02215, United States f University of Notre Dame, 390 Corbett, Notre Dame IN 46556, United States a University of Idaho Department of Design and Environments, 875 Perimeter Drive MS 2481, Moscow, Idaho 83844-2481, United States e University of Nevada, Las Vegas, 4505 S. Maryland Way, Las Vegas, NV 89154, United States t 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 b Washington State University, Department of Psychology, P.O. Box 644820, Pullman WA 99164-4820, United States g Clemson University, College of Behavioral, Social and Health Sciences, 116 Edwards Hall, Clemson South Carolina 29634, United States o Washington University School of Medicine Institute for Public Health, 660 S. Euclid, MSC 8217-0094-02, St. Louis MO 63110, United States l New Y |
| AuthorAffiliation_xml | – name: d University of North Carolina Greensboro, P.O. Box 26170, Greensboro NC 27402-6170, United States – name: p The Ohio State University Wexner Medical Center, 460 Medical Center Drive, Columbus, OH 43210, United States – name: o Washington University School of Medicine Institute for Public Health, 660 S. Euclid, MSC 8217-0094-02, St. Louis MO 63110, United States – name: l New York University, Langone One Park Ave, New York, NY 10016, United States – name: j Boston University, Department of Psychological & Brain Sciences 64 Cummington Mall, Room 149 Boston, Massachusetts 02215, United States – name: q University of Southern California, 3616 Trousdale Parkway, AHF 108, Los Angeles, CA 90089-0376, United States – name: h University of California, Los Angeles, Department of Psychology, 1285 Franz Hall, Box 951563, Los Angeles CA 90095, United States – name: g Clemson University, College of Behavioral, Social and Health Sciences, 116 Edwards Hall, Clemson South Carolina 29634, United States – name: i University of Colorado-Colorado Springs Psychology Department, Columbine Hall, 1420 Austin Bluffs Pkwy, Colorado Springs CO 80918, United States – name: s Old Dominion University, 115 Hampton Blvd, Norfolk, VA 23529, United States – name: b Washington State University, Department of Psychology, P.O. Box 644820, Pullman WA 99164-4820, United States – name: e University of Nevada, Las Vegas, 4505 S. Maryland Way, Las Vegas, NV 89154, United States – name: c 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 – name: f University of Notre Dame, 390 Corbett, Notre Dame IN 46556, United States – name: t 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 – name: n University of Michigan Medical School, Division of Developmental and Behavioral Pediatrics, 1600 Huron Parkway, Building 520, Ann Arbor, Michigan, 48109, United States – name: r Department of Psychiatry and Human Behavior Warren Alpert Medical School, Brown University, Coro West, Suite 309, 164 Summit Avenue, Providence, RI 02906, United States – name: m Colorado State University, Human Development and Family Studies, College of Health and Human Sciences, 1570 Campus Delivery, Fort Collins, CO, 80523-1501, United States – name: k The Ohio State University, 243 Psychology Building, 1835 Neil Ave, Columbus OH, 43210, United States – name: a University of Idaho Department of Design and Environments, 875 Perimeter Drive MS 2481, Moscow, Idaho 83844-2481, United States |
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| Cites_doi | 10.1148/radiology.143.1.7063747 10.1007/s10021-005-0054-1 10.1111/j.1365-2214.2009.01001.x 10.1016/S0163-6383(02)00169-8 10.1080/00223891.2013.841171 10.1007/s10994-009-5152-4 10.1016/j.infbeh.2019.101417 10.1007/BF00058655 10.1080/00223891.2016.1185612 |
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| Keywords | Quantitative methodology Preterm birth Infancy Temperament |
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| Title | Leveraging machine learning to study how temperament scores predict pre-term birth status |
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