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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)
Last data update: May 21, 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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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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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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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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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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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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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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Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol.
Bilal Ayesha M et al. BMJ open 2022 12(4) e059033
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Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.
Sun Qi et al. Journal of healthcare engineering 2022 20229635526
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Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review.
Sharifi-Heris Zahra et al. JMIR medical informatics 2022 10(4) e33875
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Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.
Migliorelli Lucia et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2021 20213021-3024
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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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Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.
Sazawal Sunil et al. BMC pregnancy and childbirth 2021 21(1) 609
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Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation.
Sazawal Sunil et al. Journal of global health 2021 1104044
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Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis.
Stock Sarah J et al. PLoS medicine 2021 18(7) e1003686
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An annotated data set for identifying women reporting adverse pregnancy outcomes on Twitter.
Klein Ari Z et al. Data in brief 2020 Oct 32106249
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Analysis of big data for prediction of provider-initiated preterm birth and spontaneous premature deliveries and ranking the predictive features.
Khatibi Toktam et al. Archives of gynecology and obstetrics 2019 300(6) 1565-1582
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Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.
Fung Russell et al. The Lancet. Digital health 2020 Jul 2(7) e368-e375
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Page last reviewed:
Feb 1, 2024
Page last updated:
May 21, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
,
National Center on Birth Defects and Developmental Disabilities
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