Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics

Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics

BackgroundInflammatory bowel disease (IBD) poses significant mortality risks for critically ill patients requiring intensive care unit (ICU) admission, driven by complications such as malnutrition, thromboembolism, and multi-organ dysfunction. Current prognostic tools for mortality prediction in thi...

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Bibliographic Details
Main Authors: Zhenze Zhang, Caiqing Zhao, Yijun Zhou, Ling Yao, Peng Liu, Ziling Fang, Nian Fang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Medicine
Subjects:
machine learning
mortality prediction
critical care
nomogram
inflammatory bowel disease
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1624899/full
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https://www.frontiersin.org/articles/10.3389/fmed.2025.1624899/full

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