Last Posted: Feb 25, 2023
- 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
- 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
- Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants.
Khurshid Faiza et al. Frontiers in pediatrics 2021 9759776
- Evidence Used to Update the List of Underlying Medical Conditions Associated with Higher Risk for Severe COVID-19
CDC Science Brief, October 14, 2021
- Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning.
Greenbury Sam F et al. Scientific reports 2021 11(1) 7178
- Bronchopulmonary dysplasia predicted at birth by artificial intelligence.
Verder Henrik et al. Acta paediatrica (Oslo, Norway : 1992) 2020 Jun
- Genomics, microbiomics, proteomics, and metabolomics in bronchopulmonary dysplasia.
Lal Charitharth Vivek et al. Seminars in perinatology 2018 Nov (7) 425-431
- Using clinical and genetic data to predict pulmonary hypertension in bronchopulmonary dysplasia.
Trittmann J K et al. Acta paediatrica (Oslo, Norway : 1992) 2018 Sep
- Exome sequencing identifies gene variants and networks associated with extreme respiratory outcomes following preterm birth.
Hamvas Aaron et al. BMC genetics 2018 19(1) 94
- Bronchopulmonary Dysplasia
From NHLBI health topic site
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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- Page last updated:Jun 05, 2023
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