用语言建模老人认知状态，实现无创持续监测
Language-Based Digital Twins for Elderly Cognitive Assistance
- 基于大模型与语言特征构建老人对话数字孪生体
- 在I-CONECT数据集上重建误差和认知评分预测误差接近真实数据
- 适合做老龄化认知健康追踪的科研与临床人员参考
数字孪生技术为个性化医疗提供了新范式，可建模个体行为与健康轨迹。在认知健康领域，轻度认知障碍（MCI）的早期检测仍具挑战性，而语言与对话模式是非侵入性生物标志物。本文提出一种基于语言的数字孪生框架，利用大语言模型（LLMs）结合风格化特征与上下文元数据，模拟老年人的对话行为。为评估保真度与认知一致性，引入多头条件变分自编码器（cVAE），联合衡量重构质量与认知评分预测能力。在I-CONECT数据集上的实验表明，该数字孪生体能保留个体特异性特征，重构误差与MoCA评分预测误差均与真实数据相当，且优于基线GPT生成响应。结果表明，语言驱动的数字孪生具备可扩展、非侵入的潜力，适用于个性化、持续性的认知健康监测。
原文摘要 · Abstract (English)
Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories. In cognitive health, early detection of Mild Cognitive Impairment (MCI) remains challenging, where language and conversational patterns serve as non-invasive biomarkers. In this work, we propose a language-based digital twin framework that leverages large language models (LLMs) to mimic the conversational behavior of elderly individuals by incorporating stylometric cues and contextual metadata. To evaluate fidelity and cognitive consistency, we introduce a multi-head conditional variational autoencoder (cVAE) that jointly measures reconstruction quality and predicts cognitive scores. Experiments on the I-CONECT dataset show that the digital twin preserves identity-specific characteristics and achieves reconstruction and MoCA prediction errors comparable to real data, while outperforming baseline GPT-generated responses. These results highlight the potential of language-based digital twins as a scalable and non-invasive approach for personalized and continuous cognitive health monitoring.
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