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...

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Authors: Meixide, C.G., Matabuena, M., Abraham, L., Kosorok, M.R.
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/381357
Online Access: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:Open access
Keyword:Diabetes research
High-dimensional data
Interval-censoring
Survival analysis
Nonlinear regression
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spelling Neural interval-censored survival regression with feature selectionMeixide, C.G.Matabuena, M.Abraham, L.Kosorok, M.R.Diabetes researchHigh-dimensional dataInterval-censoringSurvival analysisNonlinear regressionSurvival 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.Peer reviewedJohn Wiley & SonsConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/381357https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198642338&doi=10.1002%2fsam.11704&partnerID=40&md5=854f6fd0389ed86944438f6d71d5a204reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1002/sam.11704Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3813572026-05-22T06:33:51Z
dc.title.none.fl_str_mv Neural interval-censored survival regression with feature selection
title Neural interval-censored survival regression with feature selection
spellingShingle Neural interval-censored survival regression with feature selection
Meixide, C.G.
Diabetes research
High-dimensional data
Interval-censoring
Survival analysis
Nonlinear regression
title_short Neural interval-censored survival regression with feature selection
title_full Neural interval-censored survival regression with feature selection
title_fullStr Neural interval-censored survival regression with feature selection
title_full_unstemmed Neural interval-censored survival regression with feature selection
title_sort Neural interval-censored survival regression with feature selection
dc.creator.none.fl_str_mv Meixide, C.G.
Matabuena, M.
Abraham, L.
Kosorok, M.R.
author Meixide, C.G.
author_facet Meixide, C.G.
Matabuena, M.
Abraham, L.
Kosorok, M.R.
author_role author
author2 Matabuena, M.
Abraham, L.
Kosorok, M.R.
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Diabetes research
High-dimensional data
Interval-censoring
Survival analysis
Nonlinear regression
topic Diabetes research
High-dimensional data
Interval-censoring
Survival analysis
Nonlinear regression
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 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
url 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
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1002/sam.11704
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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