Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis

📝 Original Info

• Title: Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis
• ArXiv ID: 2510.26014
• Date: 2025-10-29
• Authors: ** 정보 없음 (제공된 텍스트에 저자 정보가 포함되지 않음) **

📝 Abstract

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.

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