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An interpretable machine learning model for predicting NICU admission in preterm infants: a single-center retrospective cohort study

   
@article{TP153066,
	author = {Zhanying Ma and Jianzhi Zhang and Hong Ma and Yonghong Sun and Yue Yang and Yaqiong Yu},
	title = {An interpretable machine learning model for predicting NICU admission in preterm infants: a single-center retrospective cohort study},
	journal = {Translational Pediatrics},
	volume = {15},
	number = {4},
	year = {2026},
	keywords = {},
	abstract = {Admission to the neonatal intensive care unit (NICU) is a critical event for preterm infants, with significant implications for resource allocation and parental counseling. However, existing prediction tools are often limited by low accuracy or lack of interpretability. This study aimed to develop an interpretable machine learning (ML) model for predicting NICU admission in preterm infants using readily available prenatal and intrapartum features, with a focus on both the overall cohort and the clinically challenging subgroup of late preterm infants (34–37 weeks).},
	issn = {2224-4344},	url = {https://tp.amegroups.org/article/view/153066}
}