Using graph learning to understand adverse pregnancy outcomes and stress pathways

To identify pathways between stress indicators and adverse pregnancy outcomes, we applied a nonparametric graph-learning algorithm, PC-KCI, to data from an observational prospective cohort study. The Measurement of Maternal Stress study (MOMS) followed 744 women with a singleton intrauterine pregnan...

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Veröffentlicht in:PloS one 2019-09, Vol.14 (9), p.e0223319-e0223319
Hauptverfasser: Mesner, Octavio, Davis, Alex, Casman, Elizabeth, Simhan, Hyagriv, Shalizi, Cosma, Keenan-Devlin, Lauren, Borders, Ann, Krishnamurti, Tamar
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Sprache:eng
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Zusammenfassung:To identify pathways between stress indicators and adverse pregnancy outcomes, we applied a nonparametric graph-learning algorithm, PC-KCI, to data from an observational prospective cohort study. The Measurement of Maternal Stress study (MOMS) followed 744 women with a singleton intrauterine pregnancy recruited between June 2013 and May 2015. Infant adverse pregnancy outcomes were prematurity (
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0223319