用向量量化模型分析手机穿戴设备数据，无需微调就能预测自杀风险和情绪状态。
A Vector-Quantized Foundation Model for Patient Behavior Monitoring
- 基于改进的向量量化变分自编码器处理多源异构设备数据。
- 在无微调情况下，对不同临床人群实现自杀风险与情绪预测。
- 揭示离散与连续潜在结构的权衡，适合跨任务建模研究者参考。
基础模型已在多个领域取得显著成功，但在医疗健康领域的应用仍受限。尽管医学影像、基因生物标志物及电子健康记录的时间序列已取得进展，通过个人数字设备进行患者行为监测的基础模型潜力尚未充分探索。此类设备生成的数据具有天然异构性、多源性，且常存在高缺失率，带来独特挑战。本文提出一种基于改进向量量化变分自编码器的新型基础模型，专为处理智能手机与可穿戴设备的真实世界数据而设计。利用该模型的离散潜在表示，在不同保留临床队列上无需微调即可有效完成自杀风险评估与情绪状态预测两项下游任务。我们还指出离散与连续潜在结构之间存在权衡，提示混合模型可能在各类监督与无监督任务间实现最佳准确率平衡。
原文摘要 · Abstract (English)
Foundation models 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 foundation models for patient behavior monitoring through personal digital devices remains underexplored. The data generated by these devices are inherently heterogeneous, multisource, and often exhibit high rates of missing data, posing unique challenges. This paper introduces a novel foundation model based on a modified vector quantized variational autoencoder, specifically designed to process real-world data from smartphones and wearable devices. We leveraged the discrete latent representation of this model to effectively perform two downstream tasks, suicide risk assessment and emotional state prediction, on different held-out clinical cohorts without the need of fine-tuning. We also highlight the existence of a trade-off between discrete and continuous latent structures, suggesting that hybrid models may be optimal for balancing accuracy across various supervised and unsupervised tasks.
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