Predicting Readmission Following Hospital Treatment for Patients with Alcohol Related Diagnoses in an Australian Regional Health District
Publication Name
Studies in Health Technology and Informatics
Abstract
This study aims to investigate the prediction of hospital readmission of alcohol use disorder patients within 28 days of discharge and compare the performance of six machine learning methods i.e., random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM.
Open Access Status
This publication may be available as open access
Volume
290
First Page
1072
Last Page
1073