Original title: Machine Learning Methods for Mortality Prediction in Patients with ST Elevation Myocardial Infarction
Authors: Vomlel, Jiří ; Kružík, H. ; Tůma, P. ; Přeček, J. ; Hutyra, M.
Document type: Papers
Conference/Event: The Ninth Workshop on Uncertainty Processing, Mariánské Lázně (CZ), 2012-09-12 / 2012-09-15
Year: 2012
Language: eng
Abstract: ST Elevation Myocardial Infarction (STEMI) is the leading cause of death in developed countries. The objective of our research is to design and verify a predictive model of hospital mortality in STEMI based on clinical data about patients that could serve as a benchmark for evaluation of healthcare providers. In this paper we present results of an experimental evaluation of different machine learning methods on a real data about 603 patients from University Hospital in Olomouc.
Keywords: Acute Myocardial Infarction; Machine Learning; Mortality Prediction
Project no.: GA201/08/0539 (CEP)
Funding provider: GA ČR
Host item entry: Proceedings of The Ninth Workshop on Uncertainty Processing, ISBN 978-80-245-1885-5

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2012/MTR/vomlel-machine learning methods for mortality prediction in patients with st elevation myocardial infarction.pdf
Original record: http://hdl.handle.net/11104/0211570

Permalink: http://www.nusl.cz/ntk/nusl-126813


The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers