Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study.

ABSTRACT:

Background

Precise prediction of contrast media-induced acute kidney injury (CIAKI) is an important issue because of its relationship with poor outcomes.

Objective

Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography (CT).

Methods

A total of 14,185 patients who were administered intravenous contrast media for CT at the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine of ≥0.3 mg/dL within 2 days or ≥50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural netw

SUBMITTER: Yun D 

PROVIDER: S-EPMC8520134 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature