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Public Health Genomics and Precision Health Knowledge Base (v9.0)
PHGKB
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Public Health Genomics Branch
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Reproductive and Child Health
Last data update: Apr 25, 2024
. (Total: 63675 Documents since 2012)
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Prediction of pregnancy-related complications in women undergoing assisted reproduction, using machine learning methods.
Chen Wang et al. Fertil Steril 2024
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Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data.
Deming Kong et al. Front Pediatr 2024 121330420
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Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia Risk.
Braden W Eberhard et al. medRxiv 2024
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Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.
Wasif Khan et al. PLoS One 2023 18(12) e0293925
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An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning.
Braden W Eberhard et al. medRxiv 2023
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Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.
Becker Martin et al. Frontiers in pediatrics 2022 10933266
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New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.
Hackelöer Max et al. Archives of gynecology and obstetrics 2022 1-15
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Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.
Li Shilong et al. NPJ digital medicine 2022 5(1) 68
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Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data
S Li et al, NPJ Digital Medicine, June 6, 2022
A Doubly Robust Machine Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia.
Bodnar Lisa M et al. American journal of epidemiology 2022
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Early gestational profiling of oxidative stress and angiogenic growth mediators as predictive, preventive and personalised (3P) medical approach to identify suboptimal health pregnant mothers likely to develop preeclampsia.
Anto Enoch Odame et al. The EPMA journal 2021 12(4) 517-534
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A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia.
Wang Guan et al. Frontiers in cardiovascular medicine 2021 8736491
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Perinatal health predictors using artificial intelligence: A review.
Ramakrishnan Rema et al. Women's health (London, England) 2021 1717455065211046132
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The prediction of preeclampsia: the way forward.
Myatt Leslie et al. American journal of obstetrics and gynecology 2021
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Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort.
Sufriyana Herdiantri et al. JMIR medical informatics 2020 Mar
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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