用大模型指导专家路由，提升阿尔茨海默病预测的可解释性
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
- 大模型融合影像数据与文本信息，动态选择专家网络
- 在阿尔茨海默病转化预测中表现优异，能识别患者亚型
- 输出生物合理解释，适合临床决策支持场景
阿尔茨海默病（AD）是一种复杂的神经退行性疾病，全球影响数百万人。在前驱期预测疾病转化对理解病情和患者照护至关重要。目前生存分析模型多为静态预测，缺乏可解释性且无法进行自然语言推理。本文提出iLENS框架，基于混合专家（MoE）结构，由大语言模型（LLM）引导，用于AD转化的生存预测。该方法利用LLM整合结构化影像测量与非结构化信息，驱动专家路由。实验表明，iLENS具备竞争力的预测性能，并能实现患者分型；同时提供透明、生物学合理的路由依据，弥合高性能生存分析与可解释临床支持之间的差距。
原文摘要 · Abstract (English)
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.
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