Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning

Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning

Abstract Objective To construct a highly accurate and interpretable feeding intolerance (FI) risk prediction model for preterm newborns based on machine learning (ML) to assist medical staff in clinical diagnosis. Methods In this study, a sample of 350 hospitalized preterm newborns were retrospectiv...

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Bibliographic Details
Main Authors: Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Preterm newborn
Feeding intolerance
Machine learning
Model construction
Online Access:https://doi.org/10.1186/s12911-024-02751-5
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https://doi.org/10.1186/s12911-024-02751-5

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