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Ruyan Pang
Peking University
No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China
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SCHOLARLY PAPERS
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Scholarly Papers (1)
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1.
Using Machine Learning to Achieve Accurate Estimates of Fetal Gestational Age and Personalized Predictions of Fetal Growth
Number of pages: 42
Posted: 28 Oct 2019
Russell Fung
,
Jose Villar
,
Ali Dashti
,
Leila Cheikh Ismail
,
Eleonora Staines-Urias
,
Eric O. Ohuma
,
Laurent J. Salomon
,
Cesar G. Victora
,
Fernando C. Barros
,
Ann Lambert
,
Maria Carvalho
,
Yasmin A. Jaffer
,
Alison J. Noble
,
Michael G. Gravett
,
Manorama Purwar
, Ruyan Pang,
Enrico Bertino
,
Shama Munim
,
Aung Myat Min
,
Rose McGready
,
Shane A. Norris
,
Zulfiqar A Bhutta
,
Stephen H. Kennedy
,
Aris T. Papageorghiou
,
Abbas Ourmazd
and
International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) Group
University of Wisconsin - Milwaukee - Department of Physics, University of Oxford, University of Wisconsin - Milwaukee - Department of Physics, University of Sharjah, University of Oxford, University of Oxford, Universite Paris Descartes, Federal University of Pelotas (UFPel) - International Center for Equity in Health (ICEH), Federal University of Pelotas (UFPel) - Postgraduate Program in Epidemiology, University of Oxford, Aga Khan University, Ministry of Health (Oman), University of Oxford - Department of Engineering Science, Global Alliance to Prevent Prematurity and Stillbirth, Ketkar Hospital, Peking University, Universita di Torino, Aga Khan University, Mahidol University, Mahidol University, University of the Witwatersrand, University of Toronto - Centre for Global Child Health, University of Oxford, University of Oxford, University of Wisconsin - Milwaukee - Department of Physics and Independent
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