Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.

ABSTRACT:

Objective

A clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML).

Methods

Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under C

SUBMITTER: Xinsai L 

PROVIDER: S-EPMC9535339 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature