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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Bibliographic Details
Published in:BMC medical research methodology Vol. 25; no. 1; pp. 7 - 13
Main Authors: Montaseri, Maryam, Rezaei, Mansour, Khayati, Armin, Mostafaei, Shayan, Taheri, Mohammad
Format: Journal Article
Language:English
Published: London BioMed Central 11.01.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
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
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Summary: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 study, a Multiple Kernel Learning (MKL) method has been proposed to optimize survival outcomes under the Accelerated Failure Time (AFT) model, a useful alternative to the Proportional Hazards (PH) frailty model. In other words, a survival parametric regression framework has been presented for clinical data to effectively integrate kernel learning with AFT model using a gradient descent optimization strategy. This methodology involves applying four different parametric models, evaluated using 19 distinct kernels to extract the best fitting scenario. This culminated in a sophisticated strategy that combined these kernels through MKL. We conducted a comparison between the Frailty model and MKL due to their shared fundamental properties. The models were assessed using the Concordance index (C-index) and Brier score (B-score). Each model was tested on both a case study and a replicated/independent dataset. The outcomes showed that kernelization enhances the performance of the model, especially by combining selected kernels for MKL.
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ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-024-02455-4