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

ABSTRACT:

Objective

We 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).

Methods

Patients who underwent extracorporeal circulation for ATAAD at the First Hospital of Lanzhou University from January 2016 to January 2021 were included. Unsupervised machine learning algorithm was used to stratify patients into different phenogroups according to the similarity of their clinical features and laboratory test results. The differences in the incidence of perioperative AGI and other adverse events among different phenogroups were compared. Logistic regression was used to analyze the high-risk factors for AGI in each phenogroups and random forest

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11770000 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature