Machine Learning Methods for Mortality Prediction in Patients with ST Elevation Myocardial Infarction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F12%3A00380997" target="_blank" >RIV/67985556:_____/12:00380997 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/12:00041209
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Machine Learning Methods for Mortality Prediction in Patients with ST Elevation Myocardial Infarction
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GA201%2F08%2F0539" target="_blank" >GA201/08/0539: Conditional independence structures: graphical and algebraic approaches</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of The Ninth Workshop on Uncertainty Processing
ISBN
978-80-245-1885-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
204-213
Publisher name
Faculty of Management, University of Economics, Prague
Place of publication
Prague
Event location
Mariánské Lázně
Event date
Sep 12, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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