发表机构
University of Pennsylvania; Yale University（宾夕法尼亚大学; 耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对阿尔茨海默病前驱期转化预测问题，提出iLENS框架，基于专家混合模型，利用大语言模型合成信息指导专家路由，具备竞争力的预测性能，能为决策提供透明且基于生物学的原理。
AI 中文摘要
阿尔茨海默病（AD）是一种复杂的神经退行性疾病，持续影响全球数百万人。在前驱期预测AD转化对于疾病理解和患者护理至关重要。生存模型广泛用于AD风险预测，但通常是静态预测器，解释性有限且无自然语言推理能力。本文提出iLENS，一种基于专家混合（MoE）的可解释大语言模型（LLM）引导框架，用于AD转化的生存预测。该方法利用LLM合成结构化神经影像测量和非结构化信息来指导专家路由。框架在患者亚型分类中展现出有竞争力的预测性能和能力，还为路由决策提供透明、基于生物学的原理，弥合了高性能生存分析与可解释临床决策支持之间的差距。
英文摘要
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.
