Montaseri Maryam, Rezaei Mansour, Khayati Armin, Mostafaei Shayan, Taheri Mohammad
School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Social Development and Health Promotion Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
BMC Med Res Methodol. 2025 Jan 11;25(1):7. doi: 10.1186/s12874-024-02455-4.
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.
事件发生时间数据在医学应用中非常常见。针对此类数据已经开发了回归模型，特别是在生存分析领域。通过将非线性因素引入线性模型，核函数被用于处理更为复杂和海量的医学数据。在本研究中，提出了一种多核学习（MKL）方法，以在加速失效时间（AFT）模型下优化生存结果，AFT模型是比例风险（PH）脆弱模型的一种有用替代方法。换句话说，已经为临床数据提出了一种生存参数回归框架，以使用梯度下降优化策略将核学习与AFT模型有效集成。该方法涉及应用四种不同的参数模型，并使用19种不同的核函数进行评估，以提取最佳拟合方案。这最终形成了一种通过MKL组合这些核函数的复杂策略。由于脆弱模型和MKL具有共同的基本属性，我们对它们进行了比较。使用一致性指数（C指数）和布里尔评分（B评分）对模型进行评估。每个模型都在一个案例研究和一个复制/独立数据集上进行了测试。结果表明，核化提高了模型的性能，特别是通过为MKL组合选定的核函数。
