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

Journal: BMC medical informatics and decision making
PMID: 39558307

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.

Authors

  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.
  • Xingwang Peng
    Changjiang Road Community Health Service Center, Zhangmiao Street, Baoshan District, Shanghai, China.
  • Ziyu Peng
    Department of Child and Adolescent Health, School of Public Health, Bengbu Medical University, Bengbu, Anhui, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.
  • Lianguo Fu
    Department of Child and Adolescent Health, School of Public Health, Bengbu Medical University, Bengbu, Anhui, China. lianguofu@bbmu.edu.cn.

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