[2603.22158] Multimodal Survival Analysis with Locally Deployable Large Language Models

Computer Science > Machine Learning arXiv:2603.22158 (cs) [Submitted on 23 Mar 2026] Title:Multimodal Survival Analysis with Locally Deployable Large Language Models Authors:Moritz Gögl, Christopher Yau View a PDF of the paper titled Multimodal Survival Analysis with Locally Deployable Large Language Models, by Moritz G\"ogl and 1 other authors View PDF HTML (experimental) Abstract:We study multimodal survival analysis integrating clinical text, tabular covariates, and genomic profiles using locally deployable large language models (LLMs). As many institutions face tight computational and privacy constraints, this setting motivates the use of lightweight, on-premises models. Our approach jointly estimates calibrated survival probabilities and generates concise, evidence-grounded prognosis text via teacher-student distillation and principled multimodal fusion. On a TCGA cohort, it outperforms standard baselines, avoids reliance on cloud services and associated privacy concerns, and reduces the risk of hallucinated or miscalibrated estimates that can be observed in base LLMs. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.22158 [cs.LG]   (or arXiv:2603.22158v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.22158 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Moritz Gögl [view email] [v1] Mon, 23 Mar 2026 16:21:37 UTC (192 KB) Full-text links: Access Paper: V...