An Interpretable Machine Learning Model for Predicting In-hospital Death Risk in Patients with Cardiac Arrest: Based on US Medical Information Mart for Intensive Care Database Ⅳ 2.0

An Interpretable Machine Learning Model for Predicting In-hospital Death Risk in Patients with Cardiac Arrest: Based on US Medical Information Mart for Intensive Care Database Ⅳ 2.0

Objective  To develop and validate an interpretable machine learning model based on clinical characteristics to predict the risk of in-hospital death in patients with cardiac arrest.  Methods  First clinical data of cardiac arrest patients admitted to ICU within 24 h and outcomes during hospitalizat...

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Bibliographic Details
Main Authors: GONG Huanhuan, KE Xiaowei, WANG Aimin, LI Xiangmin
Format: Article
Language:zho
Published: Editorial Office of Medical Journal of Peking Union Medical College Hospital 2023-05-01
Series:Xiehe Yixue Zazhi
Subjects:
cardiac arrest
prognostic prediction
machine learning
shap algorithms
medical information mart for intensive care database
Online Access:https://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2022-0733

Internet

https://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2022-0733

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