Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

BACKGROUND: Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in elec...

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Detalles Bibliográficos
Autores principales: Abraham, Abin, Le, Brian, Kosti, Idit, Straub, Peter, Velez-Edwards, Digna R., Davis, Lea K., Newton, J. M., Muglia, Louis J., Rokas, Antonis, Bejan, Cosmin A., Sirota, Marina, Capra, John A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516830/
https://www.ncbi.nlm.nih.gov/pubmed/36167547
http://dx.doi.org/10.1186/s12916-022-02522-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516830/
https://www.ncbi.nlm.nih.gov/pubmed/36167547
http://dx.doi.org/10.1186/s12916-022-02522-x

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