Survival parametric modeling for patients with heart failure based on Kernel learning

Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by injecting non-linearity into linear models. In this...

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Veröffentlicht in:	BMC medical research methodology Jg. 25; H. 1; S. 7 - 13
Hauptverfasser:	Montaseri, Maryam, Rezaei, Mansour, Khayati, Armin, Mostafaei, Shayan, Taheri, Mohammad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 11.01.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accelerated Failure Time Model Algorithms Angioplasty Artificial intelligence Care and treatment Event history analysis Frailty Frailty Model Health Sciences Heart Failure Heart Failure - mortality Humans Machine Learning Medicine Medicine & Public Health Methods Models, Statistical Mortality Multiple Kernel Learning Prognosis Proportional Hazards Models Statistical models Statistical Theory and Methods Statistics for Life Sciences Support vector machines Survival Analysis Theory of Medicine/Bioethics Iran Heart Failure Multiple Kernel Learning Frailty Model Survival Analysis Accelerated Failure Time Model
ISSN:	1471-2288, 1471-2288
Online-Zugang:	Volltext
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