Foundation models (FMs) have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical imaging, genetic biomarkers, and time series from electronic health records, the potential of FMs for patient behavior monitoring through wearable devices remains underexplored. These datasets are inherently heterogeneous, multisource, and often exhibit high rates of missing data, posing unique challenges. This paper introduces a novel FM based on a modified vector quantized variational autoencoder (VQ-VAE), specifically designed to process real-world data from wearable devices. We demonstrate that our pretrained FM, trained on a broad cohort of psychiatric patients, performs downstream tasks via its latent representation without fine-tuning on a held-out cohort of suicidal patients. To illustrate this, we develop a probabilistic change-point detection algorithm for suicide detection and demonstrate the FM's effectiveness in predicting emotional states. Our results show that the discrete latent structure of the VQ-VAE outperforms a state-of-the-art Informer architecture in unsupervised suicide detection, while matching its performance in supervised emotion prediction when the latent dimensionality is increased, though at the cost of reduced unsupervised accuracy. This trade-off highlights the need for future FMs to integrate hybrid discrete-continuous structures for balanced performance across tasks.

翻译：基础模型（FMs）已在多个领域取得显著成功，但其在医疗健康领域的应用仍较为有限。尽管在医学影像、遗传生物标志物以及电子健康记录的时间序列分析方面已取得重大进展，但利用可穿戴设备进行患者行为监测的基础模型潜力尚未得到充分探索。此类数据集具有固有的异质性、多源性，且通常存在较高的数据缺失率，带来了独特的挑战。本文提出了一种基于改进的向量量化变分自编码器（VQ-VAE）的新型基础模型，专门用于处理来自可穿戴设备的真实世界数据。我们证明，在广泛的精神病患者队列上预训练的基础模型，能够通过其潜在表征在未经过微调的情况下，对独立保留的自杀患者队列执行下游任务。为验证此点，我们开发了一种用于自杀检测的概率性变点检测算法，并展示了该模型在预测情绪状态方面的有效性。结果表明，在无监督自杀检测任务中，VQ-VAE的离散潜在结构优于当前最先进的Informer架构；而在增加潜在维度后，其有监督情绪预测性能与Informer相当，但会以无监督检测精度下降为代价。这种权衡凸显了未来基础模型需要整合离散-连续混合结构，以实现跨任务的平衡性能。