Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

BackgroundHeart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated...

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Bibliographic Details
Main Authors: Shengxian Peng, Jian Huang, Xiaozhu Liu, Jiewen Deng, Chenyu Sun, Juan Tang, Huaqiao Chen, Wenzhai Cao, Wei Wang, Xiangjie Duan, Xianglin Luo, Shuang Peng
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
MIMIC-IV
interpretable machine learning
neural networks
heart failure
hypertension
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2022.994359/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2022.994359/full

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