Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data

Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data

Abstract Objective The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients. Methods We enrolled 10,321 patients with sepsis older than...

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Bibliographic Details
Main Authors: Jing Fu, Aifeng He, Lulu Wang, Xia Li, Jiangquan Yu, Ruiqiang Zheng
Format: Article
Language:English
Published: BMC 2025-04-01
Series:BMC Infectious Diseases
Subjects:
Machine learning
Delirium
Sepsis
Nomogram
MIMIC-IV database
Online Access:https://doi.org/10.1186/s12879-025-10982-8
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https://doi.org/10.1186/s12879-025-10982-8

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