I-CONECT

Language-based digital twins using LLMs proposed for elderly cognitive health monitoring

Researchers propose a framework that uses large language models to construct digital twins of elderly individuals by mimicking their conversational patterns and stylometric cues, enabling continuous, non-invasive monitoring for Mild Cognitive Impairment. A multi-head conditional variational autoencoder (cVAE) is introduced to evaluate fidelity and predict cognitive scores (MoCA). Experiments on the I-CONECT dataset show the approach preserves individual identity characteristics and outperforms baseline GPT-generated responses on reconstruction and cognitive score prediction. The work positions language-based digital twins as a scalable alternative to clinical cognitive assessment.