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Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB

Reproductive and Child Health

Last data update: Jul 02, 2022. (Total: 48006 Documents since 2012)
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Records 1 - 11 (of 11 Records)

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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
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
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
A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia.
Wang Guan et al. Frontiers in cardiovascular medicine 2021 8736491
Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms.
Li Yi-Xin et al. Pregnancy hypertension 2021 26102-109
Perinatal health predictors using artificial intelligence: A review.
Ramakrishnan Rema et al. Women's health (London, England) 2021 1717455065211046132
APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation.
Pietsch Maximilian et al. Medical image analysis 2021 72102145
The prediction of preeclampsia: the way forward.
Myatt Leslie et al. American journal of obstetrics and gynecology 2021
Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis.
Sufriyana Herdiantri et al. JMIR medical informatics 2020 Nov 8(11) e16503
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
Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes.
Bodnar Lisa M et al. The American journal of clinical nutrition 2020 Feb
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