Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

ABSTRACT:

Introduction

This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions.

Methods

Patients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree model

SUBMITTER: Amritphale A 

PROVIDER: S-EPMC8190015 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature