Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

2020-07-25 public and global health Title + abstract only

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Accurate estimates of gestational age at birth are important for preterm birth surveillance but can be challenging to reliably acquire in low and middle income countries. Our objective was to develop machine learning models to accurately estimate gestational age shortly after birth using clinical and metabolic data. We derived and internally validated three models using ELASTIC NET multivariable linear regression in heel prick blood samples and clinical data from a retrospective cohort of newbor...

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

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