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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.26014 |
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| _version_ | 1866914123902615552 |
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| author | Lee, Hyeonjun Shin, Hyungseob Nam, Gunhee Lee, Hyeonsoo |
| author_facet | Lee, Hyeonjun Shin, Hyungseob Nam, Gunhee Lee, Hyeonsoo |
| contents | Survival analysis is a task to model the time until an event of interest occurs, widely used in clinical and biomedical research. A key challenge is to model patient heterogeneity while also adapting risk predictions to both individual characteristics and temporal dynamics. We propose a dual mixture-of-experts (MoE) framework for discrete-time survival analysis. Our approach combines a feature-encoder MoE for subgroup-aware representation learning with a hazard MoE that leverages patient features and time embeddings to capture temporal dynamics. This dual-MoE design flexibly integrates with existing deep learning based survival pipelines. On METABRIC and GBSG breast cancer datasets, our method consistently improves performance, boosting the time-dependent C-index up to 0.04 on the test sets, and yields further gains when incorporated into the Consurv framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis Lee, Hyeonjun Shin, Hyungseob Nam, Gunhee Lee, Hyeonsoo Machine Learning Artificial Intelligence Survival analysis is a task to model the time until an event of interest occurs, widely used in clinical and biomedical research. A key challenge is to model patient heterogeneity while also adapting risk predictions to both individual characteristics and temporal dynamics. We propose a dual mixture-of-experts (MoE) framework for discrete-time survival analysis. Our approach combines a feature-encoder MoE for subgroup-aware representation learning with a hazard MoE that leverages patient features and time embeddings to capture temporal dynamics. This dual-MoE design flexibly integrates with existing deep learning based survival pipelines. On METABRIC and GBSG breast cancer datasets, our method consistently improves performance, boosting the time-dependent C-index up to 0.04 on the test sets, and yields further gains when incorporated into the Consurv framework. |
| title | Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26014 |