An LLM -based Temporal-spatial Data Generation and Fusion Approach for Early Detection of Late Onset A lzheimer’s Disease ( LOAD ) Stagings Especially in C hinese and E nglish-speaking Populations (2025)

Abstract: Alzheimer’s Disease (AD), the 7th leading cause of death globally, demands scalable methods for early detection. While speech-based diagnostics offer promise, existing approaches struggle with temporal-spatial (T-S) challenges in capturing subtle linguistic shifts across different disease stages (temporal) and in adapting to cross-linguistic variability (spatial). This study introduces a novel Large Language Model (LLM)-driven T-S fusion framework that integrates multilingual LLMs, contrastive learning, and interpretable marker discovery to revolutionize Late Onset AD (LOAD) detection. Our key innovations include: (1) T-S Data Imputation: Leveraging LLMs to generate synthetic speech transcripts across different LOAD stages (NC, Normal Control; eMCI, early Mild Cognitive Impairment; lMCI, late Mild Cognitive Impairment; AD) and languages (Chinese, English, Spanish), addressing data scarcity while preserving clinical relevance (expert validation: 86% agreement with LLM-generated labels). (2) T-S Transformer with Contrastive Learning: A multilingual model that disentangles stage-specific (temporal) and language-specific (spatial) patterns, achieving a notable improvement of 10.9–24.7% in F1-score over existing baselines. (3) Cross-Linguistic Marker Discovery: Identifying language-agnostic markers and language-specific patterns to enhance interpretability for clinical adoption. By unifying temporal LOAD stages and spatial diversity, our framework achieves state-of-the-art performance in early LOAD detection while enabling cross-linguistic diagnostics. This study bridges NLP and clinical neuroscience, demonstrating LLMs’ potential to amplify limited biomedical data and advance equitable healthcare AI.

## introduction alzheimer's disease (ad) is the 7th leading cause of death worldwide. 95% of ad cases occur af-* corresponding authors. low-cost and non-invasive approaches to load detection and monitoring, especially via speech tests, offer promising solutions for populationbased screening @xcite . previous studies have demonstrated the utility of speechbased neurodegenerative detection @xcite , with cognitivelinguistic markers emerging as potentially effective markers to detect load in an early stage @xcite . however, despite this promise, current methods face three major limitations that arise from both the temporal (t) dimension (different load stages) and the spatial (s) dimension (different language populations with distinct linguistic, cultural, and demographic factors). first, t-s resource constraints: the available speech datasets (e.g., de-mentiabank @xcite ) remain small, fragmented, and predominantly in english, with insufficient samples for the early load stages, especially emci and lmci @xcite . this data scarcity reduces the robustness of aidriven approaches and constrains cross-linguistic generalizability. second, t-s diagnostic gaps: while ai methods achieve high accuracy in distinguishing ad from healthy controls, performance drops significantly for earlier stages where subtle cognitive-linguistic changes are most critical for timely intervention @xcite . moreover, most transformer-based diagnostic models remain language-specific, limiting applicability to diverse populations @xcite . third, t-s speech marker gaps: although linguistic markers have been studied @xcite , how speech markers differ across load stages and whether they are shared or language-specific remain to be explored. to overcome these challenges, this study proposes an innovative large language model (llm)driven t-s fusion approach to massively increase and fuse the t-s dimensions of a dataset comprising speech transcripts foot_0 from individuals with load, and to identify the salient speech markers that characterize fine-grained load stages, including nc, emci, lmci, and ad, especially in mandarin chinese foot_1 and english populations. the key innovations of this study include: (1) leveraging multilingual llms for large-scale t-s load data imputation across various load stages and languages, forming a unified t-s imputed dataset; (2) training a t-s transformer model enhanced with contrastive learning to better distinguish load stages and language groups in latent space; and (3) extracting speech markers that highlight cross-linguistic and stage-specific characteristics of load development. ## methodology our proposed methodology consists of three components: (1) llm-based t-s speech data generation and fusion. we leverage multilingual llms to impute missing speech samples in both the temporal and spatial dimensions. temporally, the imputation spans discrete load stages (nc, emci, lmci, ad), while spatially it extends across language populations (english: united states; chinese: taiwan; spanish: europe). the fused dataset integrates these temporally and spatially enriched samples into a unified corpus for downstream model training. (2) t-s transformer model with contrastive learning. we construct a transformer-based model, following a bert-style architecture with multihead self-attention layers, that projects all samples into a shared embedding space. contrastive learning is applied at the embedding level, encouraging proximity between samples of the same load stage and language while enforcing separation across different stages and languages. (3) salient speech marker identification. we identify salient speech markers by applying shapley additive explanations (shap) to the trained t-s transformer. shap quantifies the contribution of individual features to model predictions, allowing us to interpret which speech markers most strongly influence the classification of load stages across language populations. ## discussion and conclusion in this study, we introduce an innovative framework that leverages llms for the early detection of load across chinese, english, and spanishspeaking populations. the primary aim is to address the significant challenges posed by temporal (different load stages) and spatial (different language populations) dimensions in current diagnostic methods, which often lack scalability and precision. the main challenges include the scarcity of longitudinal datasets that capture subtle linguistic shifts across different stages of load, and the difficulty of adapting models to diverse linguistic and cultural contexts, particularly in low-resource languages such as chinese. these constraints hinder effective early-stage detection, which is critical for timely intervention and treatment. to overcome these challenges, our study introduces several key innovations. first, we apply llms for t-s data imputation, generating synthetic speech transcripts validated with 86% expert agreement, thereby addressing data scarcity. second, we develop a t-s transformer model enhanced with contrastive learning, which significantly improves the model's ability to distinguish stagespecific and language-specific patterns, achieving f1-score gains of 10.9-24.7% over existing baselines. lastly, we identify both cross-linguistic and language-specific speech markers, enhancing the interpretability and clinical utility of our approach. our t-s fusion load detection framework has outperformed baseline models such as biobert and clinicalbert by 10.9-24.7% in f1 score. salient speech markers include increased use of fillers and action verbs in chinese, reliance on concrete nouns and disfluencies in english, and a shift from abstract to generic terms in spanish as cognitive decline progresses. future work can integrate complementary signals such as genetic markers @xcite and explore advanced spatial-temporal imputation strategies from other domains, such as air pollution data reconstruction @xcite , to further enhance the robustness and generalizability of t-s fusion for load detection. the impacts of this work are multifaceted. clinically, it provides a scalable, non-invasive tool for early load detection, which is essential for timely intervention. methodologically, it demonstrates the potential of llms to enrich limited biomedical datasets, supporting more equitable diagnostics across languages and settings. this study underscores the importance of integrating linguistic diversity into ai-driven healthcare solutions, enhancing both diagnostic accuracy and cross-cultural applicability. by bridging natural language processing and clinical neuroscience, our framework offers a promising approach to early alzheimer's detection, paving the way for personalized medicine and continuous monitoring of neurodegenerative disease stages over time.