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

Background Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population’s general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between...

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Main Authors:	Fung, R, Villar, J, Dashti, A, Ismail, LC, Staines-Urias, E, Ohuma, EO, Salomon, LJ, Victora, CG, Barros, FC, Lambert, A, Carvalho, M, Jaffer, YA, Noble, JA, Gravett, MG, Purwar, M, Pang, R, Bertino, E, Munim, S, Min, AM, McGready, R, Norris, SA, Bhutta, ZA, Kennedy, SH, Papageorghiou, AT, Ourmazd, A, Norris, S, Abbott, SE, Abubakar, A, Acedo, J, Ahmed, I, Al-Aamri, F, Al-Abduwani, J, Al-Abri, J, Alam, D, Albernaz, E, Algren, H, Al-Habsi, F, Alija, M, Al-Jabri, H, Al-Lawatiya, H, Al-Rashidiya, B, Altman, DG, Al-Zadjali, WK, Andersen, HF, Aranzeta, L, Baricco, M, Barsosio, H, Batiuk, C, Ash, S
Format:	Journal article
Published:	Elsevier 2020

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

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