Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

Journal: Clinical nutrition (Edinburgh, Scotland)
PMID: 34906845

Abstract

BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (ML) models to predict the malnutrition status of children with CHD. We used explainable ML methods to provide insight into the model's predictions and outcomes.

Authors

  • Hui Shi
    Department of Rheumatology and Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Dong Yang
    College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology Xi'an 710021 China yangdong@sust.edu.cn.
  • Kaichen Tang
    Guangzhou AID Cloud Technology, No. 68 Huacheng Avenue, Tianhe District, Guangzhou, China.
  • Chunmei Hu
    Cardiac Intensive Care Unit, The Heart Center, Guangzhou Women and Children Medical Center, Guangzhou Medical University, No.9 Jinsui Road, Zhujiang Newtown, Tianhe District, Guangzhou 510623, China.
  • Lijuan Li
  • Linfang Zhang
    Cardiac Intensive Care Unit, The Heart Center, Guangzhou Women and Children Medical Center, Guangzhou Medical University, No.9 Jinsui Road, Zhujiang Newtown, Tianhe District, Guangzhou 510623, China.
  • Ting Gong
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Yanqin Cui
    Cardiac Intensive Care Unit, The Heart Center, Guangzhou Women and Children Medical Center, Guangzhou Medical University, No.9 Jinsui Road, Zhujiang Newtown, Tianhe District, Guangzhou 510623, China; Department of Pediatric Surgery, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China. Electronic address: cuiyanqin@gwcmc.org.

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