Are Time Series Foundation Models Ready for Vital Sign Forecasting in Healthcare?

The rise of foundation models, particularly large language models like ChatGPT, has revolutionized natural language processing and demonstrated remarkable generalization across numerous healthcare applications. Building on this success, foundation models for time series forecasting have emerged, offering new opportunities by leveraging pretraining on large-scale datasets. However, existing time series foundation models are pretrained with minimal clinical data, and their potentials for continuously recorded clinical time series, such as vital signs, remain largely under-explored. This motivates our endeavor to integrate time series foundation models with vital sign data to address critical clinical challenges, particularly in predicting patient deterioration. Through an extensive evaluation of various settings and configurations of these models, alongside comparisons with conventional forecasting models, we highlight the significant opportunities for improvement in developing clinically useful time series forecasting models. In a word, the “ChatGPT” moment for time series foundation models, in the typical clinical domain, is yet to come.