Neural interval-censored survival regression with feature selection
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 nonlinear regres...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/381357 |
| Acceso en línea: | http://hdl.handle.net/10261/381357 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198642338&doi=10.1002%2fsam.11704&partnerID=40&md5=854f6fd0389ed86944438f6d71d5a204 |
| Access Level: | acceso abierto |
| Palabra clave: | Diabetes research High-dimensional data Interval-censoring Survival analysis Nonlinear regression |
| Sumario: | 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 nonlinear regression algorithms and variable selection techniques for interval-censoring is either limited or nonexistent, 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 nonlinear relationships. © 2024 The Author(s). Statistical Analysis and Data Mining: The ASA Data Science Journal published by Wiley Periodicals LLC. |
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