Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

Journal: BMC medical informatics and decision making
Published Date: May 27, 2025

Abstract

BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU).

Authors

  • Shuxing Wei
    Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China.
  • Hongmeng Dong
    Emergency Medicine Clinical Research Center, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing Chaoyang Hospital, Affiliated to Capital Medical University, Beijing, 100020, China.
  • Weidong Yao
    Department of Anesthesiology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Xiya Wang
    Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China.
  • Wenqing Ji
    Emergency Medicine Clinical Research Center, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing Chaoyang Hospital, Affiliated to Capital Medical University, Beijing, 100020, China.
  • Yongsheng Zhang
    Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, China.
  • Shubin Guo
    Emergency Medicine Clinical Research Center, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing Chaoyang Hospital, Affiliated to Capital Medical University, Beijing, 100020, China. shubin007@yeah.net.

Keywords

External Resources

Popular Topics
Recent Journals