@inproceedings{f5e32a520e36423f8c9b6124e45f9035,
title = "A Predictive Model for Acute Admission in Aged Population",
abstract = "Acute hospital admission among the elderly population is very common and have a high impact on the health services and the community, as well as on the individuals. Several studies have focused on the possible risk factors, however, predicting who is at risk for acute hospitalization associated with disease and symptoms is still an open research question. In this study, we investigate the use of machine learning algorithms for predicting acute admission in older people based on admission data from individual citizens 70 years and older who were hospitalized in the acute medical unit of Svendborg Hospital in Denmark.",
author = "Marjan Mansourvar and Karen Andersen-Ranberg and N{\o}hr, \{Christian Gradhandt\} and Wiil, \{Uffe Kock\}",
year = "2018",
doi = "10.3233/978-1-61499-852-5-96",
language = "English",
isbn = "978-1-61499-851-8",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "96--100",
editor = "Adrien Ugon and Karlsson, \{Daniel \} and Klein, \{Gunnar O. \} and Moen, \{Anne \}",
booktitle = "Building Continents of Knowledge in Oceans of Data : The Future of Co-Created eHealth",
address = "Netherlands",
note = "Medical Informatics Europe, MIE 2018 ; Conference date: 24-04-2018 Through 26-04-2018",
}