2023
DOI: 10.3390/diagnostics13243627
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Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model

So Jin Yoon,
Donghyun Kim,
Sook Hyun Park
et al.

Abstract: Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 VLBW infants, born between 2013 and 2017 in four hospitals, were included. PGF was defined as a decrease in z-score between birth and discharge that was greater than 1.28. Feature selection and addition were performed to improve the accurac… Show more

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