Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study
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Title
Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study
Authors
Keywords
-
Journal
BMC Pregnancy and Childbirth
Volume 21, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-03-12
DOI
10.1186/s12884-021-03658-z
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Note: Only part of the references are listed.- Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015
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- (2016) S. Mastrodima et al. ULTRASOUND IN OBSTETRICS & GYNECOLOGY
- Prediction of stillbirth from placental growth factor at 19-24 weeks
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- Predicting stillbirth in a low resource setting
- (2016) Gbenga A. Kayode et al. BMC Pregnancy and Childbirth
- Stillbirth and the small fetus: use of a sex-specific versus a non-sex-specific growth standard
- (2015) A S Trudell et al. Journal of Perinatology
- Maternal Education and Stillbirth
- (2012) Nathalie Auger et al. EPIDEMIOLOGY
- Major risk factors for stillbirth in high-income countries: a systematic review and meta-analysis
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- (2009) Gary L Darmstadt et al. BMC Pregnancy and Childbirth
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