Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify postoperative acute gastrointestinal injury

Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify postoperative acute gastrointestinal injury

ObjectiveWe aimed to explore the application value of unsupervised machine learning in identifying acute gastrointestinal injury (AGI) after extracorporeal circulation for acute type A aortic dissection (ATAAD).MethodsPatients who underwent extracorporeal circulation for ATAAD at the First Hospital...

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Bibliographic Details
Main Authors: Yuhu Ma, Xiaofang Yang, Chenxiang Weng, Xiaoqing Wang, Baoping Zhang, Ying Liu, Rui Wang, Zhenxing Bao, Peining Yang, Hong Zhang, Yatao Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
acute type A aortic dissection
unsupervised machine learning
phenogroups
random forest
prediction
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2024.1514751/full
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https://www.frontiersin.org/articles/10.3389/fcvm.2024.1514751/full

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