Neural interval-censored survival regression with feature selection

• URL: http://arxiv.org/abs/2206.06885v3
• Date: Thu, 22 Aug 2024 16:48:12 GMT
• Title: Neural interval-censored survival regression with feature selection
• Authors: Carlos García Meixide, Marcos Matabuena, Louis Abraham, Michael R. Kosorok,
• Abstract summary: We introduce a novel predictive framework tailored for interval-censored regression tasks rooted in Accelerated Failure Time (AFT) models. Our results outperform traditional AFT algorithms, particularly in scenarios featuring non-linear relationships.
• Score: 1.933856957193398
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine. This prominence is due to the increasing prevalence of large and high-dimensional datasets, such as omics and medical image data. However, the literature on non-linear regression algorithms and variable selection techniques for interval-censoring is either limited or non-existent, particularly in the context of neural networks. Our objective is to introduce a novel predictive framework tailored for interval-censored regression tasks, rooted in Accelerated Failure Time (AFT) models. Our strategy comprises two key components: i) a variable selection phase leveraging recent advances on sparse neural network architectures, ii) a regression model targeting prediction of the interval-censored response. To assess the performance of our novel algorithm, we conducted a comprehensive evaluation through both numerical experiments and real-world applications that encompass scenarios related to diabetes and physical activity. Our results outperform traditional AFT algorithms, particularly in scenarios featuring non-linear relationships.

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