Dementia Insights: A Context-Based MultiModal Approach

Abstract

Dementia, a progressive neurodegenerative disorder, affects memory, reasoning, and daily functioning, creating challenges for individuals and healthcare systems. Early detection is crucial for timely interventions that may slow disease progression. Large pre-trained models (LPMs) for text and audio, such as Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and Contrastive Language-Audio Pretraining (CLAP), have shown promise in identifying cognitive impairments. However, existing studies generally rely heavily on expert-annotated datasets and unimodal approaches, limiting robustness and scalability. This study proposes a context-based multimodal method, integrating both text and audio data using the best-performing LPMs in each modality. By incorporating contextual embeddings, our method improves dementia detection performance. Additionally, motivated by the effectiveness of contextual embeddings, we further experimented with a context-based In-Context Learning (ICL) as a complementary technique. Results show that GPT-based embeddings, particularly when fused with CLAP audio features, achieve an F1-score of $83.33\%$, surpassing state-of-the-art dementia detection models. Furthermore, raw text data outperforms expert-annotated datasets, demonstrating that LPMs can extract meaningful linguistic and acoustic patterns without extensive manual labeling. These findings highlight the potential for scalable, non-invasive diagnostic tools that reduce reliance on costly annotations while maintaining high accuracy. By integrating multimodal learning with contextual embeddings, this work lays the foundation for future advancements in personalized dementia detection and cognitive health research.