数字孪生已成为个性化医疗保健的一个有前途的范例,，可以对个人行为和健康轨迹进行建模。在认知健康, 中，早期发现轻度认知障碍(MCI) 仍然具有挑战性,，其中语言和对话模式可作为非侵入性生物标志物。在这项工作,中，我们提出了一个基于语言的数字孪生框架，该框架利用大型语言模型(LLMs)通过结合风格提示和上下文元数据来模仿老年人的对话行为。为了评估保真度和认知一致性,，我们引入了多头条件变分自动编码器(cVAE)，它联合测量重建质量并预测认知分数。 I-CONECT 数据集上的实验表明，数字孪生保留了特定于身份的特征，并实现了与真实数据, 相当的重建和 MoCA 预测误差，同时优于基线 GPT 生成的响应。这些结果凸显了基于语言的数字孪生作为一种可扩展、非侵入性的个性化和持续认知健康监测方法的潜力。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)