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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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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 23, 2024
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Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.
Qiu-Yan Yu et al. Front Big Data 2024 71291196
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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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Image analysis-based machine learning for the diagnosis of retinopathy of prematurity: A meta-analysis and systematic review.
Yihang Chu et al. Ophthalmol Retina 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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The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.
Elif Keles et al. NPJ Digit Med 2023 6(1) 220
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Establishment of a model for predicting preterm birth based on the machine learning algorithm.
Yao Zhang et al. BMC Pregnancy Childbirth 2023 23(1) 779
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Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.
Neal G Ravindra et al. NPJ Digit Med 2023 6(1) 171
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An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning.
Braden W Eberhard et al. medRxiv 2023
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Machine learning and disease prediction in obstetrics.
Zara Arain et al. Curr Res Physiol 2023 6100099
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Epidemiologic evaluation of retinopathy of prematurity severity in a large telemedicine program in India using artificial intelligence.
Mallory A deCampos-Stairiker et al. Ophthalmology 2023
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End-to-end learning with interpretation on electrohysterography data to predict preterm birth.
A M Fischer et al. Computers in biology and medicine 2023 158106846
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Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database.
Jue Seong Lee et al. PloS one 2023 18(3) e0283959
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A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology.
Sagar N Malani et al. Cureus 2023 15(2) e34891
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Electronic medical records driven big data analytics in retinal diseases, report number 1: non-oncological retinal diseases in children and adolescents in India.
Komal Agarwal et al. International ophthalmology 2023
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Associations of Preterm Birth with Dental and Gastrointestinal Diseases: Machine Learning Analysis Using National Health Insurance Data.
In-Seok Song et al. International journal of environmental research and public health 2023 20(3)
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Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.
Eilts Sonja K et al. JAMA network open 2023 6(1) e2251512
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Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.
Ramanathan Ashwin et al. Eye (London, England) 2022
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Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.
Cole Emily et al. Ophthalmology science 2022 2(4) 100165
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Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.
Liu Zheng et al. Frontiers in pediatrics 2022 10899954
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Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.
Yang Qiuyu et al. Acta obstetricia et gynecologica Scandinavica 2022
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Bedside tracking of functional autonomic age in preterm infants.
Iyer Kartik K et al. Pediatric research 2022
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Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015.
Wong Kingsley et al. Scientific reports 2022 12(1) 19153
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Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels.
Hanif Adam et al. Ophthalmology science 2022 2(2) 100122
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External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations.
Coyner Aaron S et al. JAMA ophthalmology 2022
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The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study.
Zhang Yichao et al. JMIR medical informatics 2022 10(6) e33835
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Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework.
van Boven Menne R et al. Pediatrics 2022
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Associations between ambient temperature and risk of preterm birth in Sweden: A comparison of analytical approaches.
de Bont Jeroen et al. Environmental research 2022 113586
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Prediction of preterm birth using artificial intelligence: a systematic review.
Akazawa Munetoshi et al. Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology 2022 1-7
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Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks.
Li Peng et al. Translational vision science & technology 2022 11(5) 17
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Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review.
McAdams Ryan M et al. Journal of perinatology : official journal of the California Perinatal Association 2022
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 23, 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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