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 |
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Format: | Artikel |
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 ( |
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ISSN: | 1932-6203 1932-6203 |
DOI: | 10.1371/journal.pone.0223319 |