[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82039-en":3,"doc-seo-82039-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82039,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","iLENS Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis","Alzheimer’s Disease (AD) conversion during the prodromal stage requires accurate, interpretable survival modeling for early intervention and patient care. iLENS introduces an interpretable large language model–guided mixture-of-experts framework for AD survival prediction and inpatient subtyping. The system uses an LLM to synthesize structured neuroimaging measurements and unstructured information to drive expert routing. It delivers competitive predictive performance while providing transparent, biologically grounded rationales that bridge high-performance survival analysis and interpretable clinical decision support.","iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis  \nFarica Zhuang1 , Seong Woo Han1 , Zixuan Wen1 , Shu Yang1 , Yize Zhao2 , Li Shen1 ,  \n1University of Pennsylvania, 2 Yale University  \narXiv :2607 .08778v1 [ cs .LG] 12 Jun 2026  \nAbstract  \nAlzheimer’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-ofexperts (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 inpatient 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.  \n1 Introduction  \nSurvival analysis, or time-to-event prediction, isan important component of precision medicine. Inneurodegenerative disorders, such as Alzheimer’s Disease (AD), predicting risk scores for conversion to AD and identifying specific risk profiles are essential for early intervention and resource allocation (Sperling et al., 2011 ; Nakagawa et al., 2020 ; Mirabnahrazam et al., 2023) . Classical survival methods and modern deep learning models have improved predictive accuracy, yet they remain primarily as predictors for numerical outcome risk scores (Fox and Weisberg, 2002 ; Nagpal et al., 2021 ; Katzman et al., 2018) . In clinical settings, however, the ability to identify patient subgroups or clusters based on risk profiles is valuable, as such subtypes can reveal heterogeneous disease progression patterns and support more interpretable prog-  \nnostic decision-making. This is particularly important in AD, where patients often exhibit diverse trajectories of neurodegeneration and biomarker burden (Goyal et al., 2018) . To bridge this gap, we introduce iLENS, an interpretable LLM-guided MoE for neuroimaging survival analysis. Moving beyond static encoders, we investigate whether language models can serve as semantic routing controllers within an expert-based survival clustering framework. This design provides two-layer interpretability. First, we provide natural language interpretability based on each patient’s structured neuroimaging biomarker measurements to justify the model’s clinically-grounded expert routing. Second, we maintain clustering interpretability and uncover the underlying survival subtypes. We evaluate our framework using real-world longitudinal neuroimaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (Petersen et al., 2010) to predict and subtype AD conversion.  \n2 Related Work  \nSurvival Prediction and Subtype Clustering.  \nTraditional survival models often focus on risk score predictions for a given outcome (Fox and Weisberg, 2002 ; Katzman et al., 2018 ; Nagpal et al., 2021) . However, a crucial goal of survival prediction in medical applications is also to identify patient subgroups with similar survival patterns and risk profiles, known as subtyping (e.g., high risk v.s. low risk) (Abbasi et al., 2024) . This helps with treatment assignment, resource allocation, and disease understanding (Carobbio et al., 2020 ; Glare et al., 2003 ; Binder and Schumacher, 2008) . Hence, neural survival clustering (NSC) models were introduced as a new class of survival models that not only predict individualized risk, but also perform explicit subtype discovery (Jeanselme et al., 2022 ; Hou et al., 20","cbCaitvNTPyHZJFL","https://ap.wps.com/l/cbCaitvNTPyHZJFL","pdf",623070,2,1,13,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does iLENS address in Alzheimer’s Disease prediction?\",\"answer\":\"iLENS targets predicting AD conversion during the prodromal stage and addressing the interpretability limits of typical survival models used for risk prediction.\"},{\"question\":\"How does iLENS use a large language model in the mixture-of-experts framework?\",\"answer\":\"An LLM synthesizes structured neuroimaging measurements and unstructured information to guide expert routing, acting as a semantic controller for expert selection.\"},{\"question\":\"What does iLENS provide besides survival risk prediction?\",\"answer\":\"iLENS supports biologically grounded, transparent rationales for routing decisions and uncovers interpretable survival subtypes for patient 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