Machine learning for survival analysis: a comparative study on intensive care unit (ICU) patient data and simulations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61384399%3A31140%2F25%3A00062155" target="_blank" >RIV/61384399:31140/25:00062155 - isvavai.cz</a>
Result on the web
<a href="https://annals-csis.org/Volume_43/drp/pdf/6352.pdf" target="_blank" >https://annals-csis.org/Volume_43/drp/pdf/6352.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15439/2025F6352" target="_blank" >10.15439/2025F6352</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning for survival analysis: a comparative study on intensive care unit (ICU) patient data and simulations
Original language description
Main topics of the document: survival analysis; censored data; Cox proportional hazards model; penalized Cox regression; CoxBoost; machine learning; random survival forests; predictive modeling; explainable artificial intelligence; model interpretability
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10101 - Pure mathematics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)
ISBN
978-83-973291-6-4
ISSN
2300-5963
e-ISSN
2300-5963
Number of pages
6
Pages from-to
647-652
Publisher name
FedCSIS
Place of publication
Polsko
Event location
Krakov
Event date
Sep 14, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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