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Using Machine Learning to Achieve Accurate Estimates of Fetal Gestational Age and Personalized Predictions of Fetal Growth

By Russell Fung1, Jose Villar, Ali Dashti2, 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 Ourmazd1, International Fetal And Newborn Gro Group

1. University of Wisconsin-Milwaukee 2. University of Wisconsin Milwaukee

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Category

BioXFEL Publications

Published on

Type

journal-article

Author

Russell Fung and Jose Villar and Ali Dashti and Leila Cheikh Ismail and Eleonora Staines-Urias and Eric O. Ohuma and Laurent J. Salomon and Cesar G. Victora and Fernando C. Barros and Ann Lambert and Maria Carvalho and Yasmin A. Jaffer and Alison J. Noble and Michael G. Gravett and Manorama Purwar and Ruyan Pang and Enrico Bertino and Shama Munim and Aung Myat Min and Rose McGready and Shane A. Norris and Zulfiqar A Bhutta and Stephen H. Kennedy and Aris T. Papageorghiou and Abbas Ourmazd and International Fetal and Newborn Gro Group

Citation

Fung, R. et al., 2019. Using Machine Learning to Achieve Accurate Estimates of Fetal Gestational Age and Personalized Predictions of Fetal Growth. SSRN Electronic Journal. Available at: http://dx.doi.org/10.2139/ssrn.3471997.

DOI

http://dx.doi.org/10.2139/ssrn.3471997

Funding

NSF-STC Biology with X-ray Lasers (NSF-1231306)