A Vector-Quantized Foundation Model for Patient Behavior Monitoring
This work addresses the underexplored problem of using foundation models for patient behavior monitoring in healthcare, offering a novel approach for clinicians and researchers, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the challenge of applying foundation models to patient behavior monitoring using heterogeneous data from smartphones and wearables, introducing a vector quantized variational autoencoder that achieved effective performance in suicide risk assessment and emotional state prediction without fine-tuning on held-out clinical cohorts.
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.