iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis

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[Submitted on 12 Jun 2026]

Title:iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis

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Abstract:Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains critical for disease understanding and patient care. As such, survival models are widely used for AD risk prediction, yet they are typically static predictors with limited interpretability and no capacity for natural language reasoning. In this work, we propose iLENS, an interpretable large language model (LLM) guided framework based on mixture-of-experts (MoE) for survival prediction in AD conversion. Our approach uses LLM to synthesize structured neuroimaging measurements and unstructured information to guide expert routing. Our framework demonstrates competitive predictive performance and capability in patient subtyping. Furthermore, our framework provides transparent, biologically grounded rationales for its routing decisions, bridging the gap between high-performance survival analysis and interpretable clinical decision support.

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.08778 [cs.LG]

(or arXiv:2607.08778v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.08778

arXiv-issued DOI via DataCite

Submission history

From: Farica Zhuang [view email] [v1] Fri, 12 Jun 2026 17:11:13 UTC (346 KB)

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