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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Vydáno v:	BMC medical research methodology Ročník 25; číslo 1; s. 7 - 13
Hlavní autoři:	Montaseri, Maryam, Rezaei, Mansour, Khayati, Armin, Mostafaei, Shayan, Taheri, Mohammad
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 11.01.2025 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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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Abstract	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.
AbstractList	Abstract 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. 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. Keywords: Survival Analysis, Frailty Model, Accelerated Failure Time Model, Heart Failure, Multiple 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 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. 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.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.
ArticleNumber	7
Audience	Academic
Author	Montaseri, Maryam Taheri, Mohammad Khayati, Armin Rezaei, Mansour Mostafaei, Shayan
Author_xml	– sequence: 1 givenname: Maryam surname: Montaseri fullname: Montaseri, Maryam email: Maryam.Montaseri@Kums.ac.ir organization: School of Health, Kermanshah University of Medical Sciences – sequence: 2 givenname: Mansour surname: Rezaei fullname: Rezaei, Mansour organization: Social Development and Health Promotion Research Center, Health Institute, Kermanshah University of Medical Sciences – sequence: 3 givenname: Armin surname: Khayati fullname: Khayati, Armin organization: Computer Sci. & Eng. Department, School of Elec. & Comp. Eng, Shiraz University – sequence: 4 givenname: Shayan surname: Mostafaei fullname: Mostafaei, Shayan organization: Department of Medical Epidemiology and Biostatistics, Karolinska Institute – sequence: 5 givenname: Mohammad surname: Taheri fullname: Taheri, Mohammad organization: Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University
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Cites_doi	10.1111/biom.12098 10.1002/sim.6415 10.15587/2313-8416.2014.27968 10.1001/jama.1982.03320430047030 10.1146/annurev.publhealth.18.1.105 10.1142/9789812776303_0007 10.1109/IEMBS.2009.5334847 10.1371/journal.pone.0210602 10.1213/ANE.0000000000003653 10.1161/CIRCULATIONAHA.119.041297 10.1016/j.jacc.2020.11.010 10.1111/j.1541-0420.2010.01544.x 10.1109/TPAMI.2011.153 10.34172/aim.2021.119 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 10.1101/2022.02.18.481114 10.1016/j.artmed.2021.102077 10.1002/sim.4154 10.1093/ehjqcco/qcx031 10.1007/11559887_3 10.1007/978-1-4419-6646-9 10.5244/C.30.98 10.1109/ICDM.2008.50 10.1186/1471-2105-9-292 10.1145/2661829.2662065 10.1109/TNNLS.2015.2420611 10.1186/1471-2261-14-126 10.1186/1471-2288-10-40 10.1007/978-3-319-89725-7_7 10.1371/journal.pone.0181001 10.1002/sim.9717 10.1201/b14978 10.21037/atm.2016.08.45 10.1109/TCBB.2007.070208 10.1111/biom.13880 10.1002/sim.4780111409 10.18637/jss.v011.i09 10.1002/sim.7681 10.1007/s10985-020-09509-x 10.1214/009053607000000677 10.18637/jss.v090.i07 10.1201/9781003282525 10.32614/CRAN.package.bigSurvSGD 10.1088/1742-6596/974/1/012008
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Keywords	Heart Failure Multiple Kernel Learning Frailty Model Survival Analysis Accelerated Failure Time Model
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Snippet	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.... Abstract Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival...
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SubjectTerms	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
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Title	Survival parametric modeling for patients with heart failure based on Kernel learning
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