Leveraging the variational Bayes autoencoder for survival analysis.
Censored data
Deep learning
Survival analysis
Time to event
Variational autoencoders
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 Oct 2024
19 Oct 2024
Historique:
received:
11
07
2024
accepted:
10
10
2024
medline:
20
10
2024
pubmed:
20
10
2024
entrez:
19
10
2024
Statut:
epublish
Résumé
Survival analysis in medical research has witnessed a growing interest in applying deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored data. Current methods make assumptions about the relations between data that may not be valid in practice. Therefore, we introduce SAVAE (Survival Analysis Variational Autoencoder). SAVAE, based on Variational Autoencoders, contributes significantly to the field by introducing a tailored Evidence Lower BOund formulation, supporting various parametric distributions for covariates and survival time (if the log-likelihood is differentiable). It offers a general method that demonstrates robustness and stability through different experiments. Our proposal effectively estimates time-to-event, accounting for censoring, covariate interactions, and time-varying risk associations. We validate our model in diverse datasets, including genomic, clinical, and demographic tabular data, with varying levels of censoring. This approach demonstrates competitive performance compared to state-of-the-art techniques, as assessed by the Concordance Index and the Integrated Brier Score. SAVAE also offers an interpretable model that parametrically models covariates and time. Moreover, its generative architecture facilitates further applications such as clustering, data imputation, and synthetic patient data generation through latent space inference from survival data. This approach fosters data sharing and collaboration, improving medical research and personalized patient care.
Identifiants
pubmed: 39427084
doi: 10.1038/s41598-024-76047-z
pii: 10.1038/s41598-024-76047-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24567Subventions
Organisme : European Union's Horizon 2020
ID : 101017549
Informations de copyright
© 2024. The Author(s).
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