Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction

• URL: http://arxiv.org/abs/2602.19987v1
• Date: Mon, 23 Feb 2026 15:53:25 GMT
• Title: Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction
• Authors: Ha-Anh Hoang Nguyen, Tri-Duc Phan Le, Duc-Hoang Pham, Huy-Son Nguyen, Cam-Van Thi Nguyen, Duc-Trong Le, Hoang-Quynh Le,
• Abstract summary: CURE is a framework that advances counterfactual survival modeling via comprehensive multimodal embedding and latent retrieval.<n> Experimental results on METABRIC and TCGA-LUAD datasets demonstrate that proposed CURE model consistently outperforms strong baselines in survival analysis.
• Score: 1.5713805841057418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper tackles the problem of time-to-event counterfactual survival prediction, aiming to optimize individualized survival outcomes in the presence of heterogeneity and censored data. We propose CURE, a framework that advances counterfactual survival modeling via comprehensive multimodal embedding and latent subgroup retrieval. CURE integrates clinical, paraclinical, demographic, and multi-omics information, which are aligned and fused through cross-attention mechanisms. Complex multi-omics signals can be adaptively refined using a mixture-of-experts architecture, emphasizing the most informative omics components. Building upon this representation, CURE implicitly retrieves patient-specific latent subgroups that capture both baseline survival dynamics and treatment-dependent variations. Experimental results on METABRIC and TCGA-LUAD datasets demonstrate that proposed CURE model consistently outperforms strong baselines in survival analysis, evaluated using the Time-dependent Concordance Index ($C^{td}$) and Integrated Brier Score (IBS). These findings highlight the potential of CURE to enhance multimodal understanding and serve as a foundation for future treatment recommendation models. All code and related resources are publicly available to facilitate the reproducibility https://github.com/L2R-UET/CURE.

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