Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.

Journal: BMC medical informatics and decision making
PMID: 40247291

Abstract

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to create a model for the early and precise prediction of SIRS in AP.

Authors

  • Rufa Zhang
    Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China.
  • Shiqi Zhu
    Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China.
  • Li Shi
    Department of Integrated Chinese and Western Medicine, Second Xiangya Hospital, Central South University, Changsha 410011, China.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Xiaodan Xu
    Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, No.1 Shuyuan Street, Changshu, Jiangsu, 215500, China. xuxiaodan20@126.com.
  • Bo Xiang
    Department of Pediatric Surgery, West China hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, Sichuan, China. xb_scu.edu@hotmail.com.
  • Min Wang
    National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou 325035, China.

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