Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a singl...

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Detalles Bibliográficos
Autores principales: Malacova, Eva, Tippaya, Sawitchaya, Bailey, Helen D., Chai, Kevin, Farrant, Brad M., Gebremedhin, Amanuel T., Leonard, Helen, Marinovich, Michael L., Nassar, Natasha, Phatak, Aloke, Raynes-Greenow, Camille, Regan, Annette K., Shand, Antonia W., Shepherd, Carrington C. J., Srinivasjois, Ravisha, Tessema, Gizachew A., Pereira, Gavin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093523/
https://www.ncbi.nlm.nih.gov/pubmed/32210300
http://dx.doi.org/10.1038/s41598-020-62210-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093523/
https://www.ncbi.nlm.nih.gov/pubmed/32210300
http://dx.doi.org/10.1038/s41598-020-62210-9

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