Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study.

Journal: International journal of surgery (London, England)
PMID: 38265429

Abstract

BACKGROUND: Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicting four major APOs after pediatric congenital heart surgery and their clinically meaningful model interpretations.

Authors

  • Chaoyang Tong
    Department of Anesthesiology.
  • Xinwei Du
    Pediatric Thoracic and Cardiovascular Surgery, Shanghai Children's Medical Center, School of Medicine and National Children's Medical Center, Shanghai Jiao Tong University.
  • Yancheng Chen
    Alibaba Group.
  • Kan Zhang
    CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
  • Mengqin Shan
    Department of Anesthesiology.
  • Ziyun Shen
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
  • Haibo Zhang
    Department of Radiology, the Third People's Hospital of Zhongshan, Zhongshan, Guangdong 528451, China.
  • Jijian Zheng
    Department of Anesthesiology.

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