Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.

ABSTRACT:

Introduction

Acute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.

Methods

A CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively a

SUBMITTER: Le S 

PROVIDER: S-EPMC8116756 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature