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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)
Last data update: May 07, 2024
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Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.
Uday Pratap Singh Parmar et al. Medicina (Kaunas) 2024 60(4)
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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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Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm.
Shiang-Chin Lin et al. Phys Ther 2024
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Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods.
Andrea K Bowe et al. JAMA Netw Open 2023 6(12) e2349111
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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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Predicting mental and psychomotor delay in very pre-term infants using machine learning.
Gözde M Demirci et al. Pediatr Res 2023
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Development and clinical impact assessment of a machine-learning model for early prediction of late-onset sepsis.
Merel A M van den Berg et al. Comput Biol Med 2023 163107156
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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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Machine learning to predict late respiratory support in preterm infants: a retrospective cohort study.
Tsung-Yu Wu et al. Scientific reports 2023 13(1) 2839
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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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Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods.
Bogale Dawit S et al. BMC medical informatics and decision making 2022 22(1) 341
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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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Development and Validation of Algorithms to Estimate Live Birth Gestational Age in Medicaid Analytic eXtract Data.
Zhu Yanmin et al. Epidemiology (Cambridge, Mass.) 2022 34(1) 69-79
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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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Page last reviewed:
Feb 1, 2024
Page last updated:
May 07, 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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