Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.

Journal: BMC gastroenterology

PMID: 40033198

Abstract

BACKGROUND: Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model to calculate the risk of mortality in patients with AP admitted in intensive care unit (ICU) and compared it with existing scoring systems.

Authors

Meng Jiang

Affiliated Hospital of Nanjing University of TCM, Jiangsu Provincial Hospital of TCM, Nanjing 210029,China.
Xiao-Peng Wu

State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Xing-Chen Lin

Emergency and Trauma Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, P.R. China.
Chang-Li Li

Department of Geratology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, 11 Lingjiaohu Avenue, Wuhan, 430015, PR China.

Keywords

Acute Disease Adult Aged APACHE Female Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Pancreatitis Risk Assessment ROC Curve Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (40033198)

Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.

Abstract

Authors

Keywords

External Resources

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