Towards Foundation Models for Time Series in Healthcare

Time series data underpin nearly every aspect of modern healthcare, from continuous monitoring in the ICU to longitudinal signals from wearable sensors. Yet most machine learning models in this domain either assume shared dynamics across highly heterogeneous populations or are designed at the individual level, requiring sufficient personal data before meaningful predictions can be made. Our analyses across multiple ICU and wearable cohorts reveal substantial heterogeneity within and across subpopulations. This talk will argue that one way to address this heterogeneity while still pursuing the vision of foundation models for temporal health data, lies in learning dynamic subgroup-level embeddings: representations that can adapt across individuals, time scales, and data modalities. I will present our recent progress toward this goal, including a nonparametric Bayesian approach. These efforts aim to create a new wave of adaptive time series models capable of supporting individualized healthcare on time and at scale.