A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at Scale

Intensive care departments generate vast multivariate time series data capturing the dynamic physiological states of critically ill patients. Despite advances in AI-driven clinical decision support, existing models remain limited. They are tailored to specific conditions or single institutions and require extensive adaptation for new settings. To make such generalization feasible, we introduce ICareFM, a novel foundation model for intensive care, trained on a harmonized dataset of unprecedented scale. The dataset contains 650,000 patient stays, accumulating more than 4,000 patient years of data, and over one billion measurements from hospitals in the US, several European countries, and China. ICareFM employs a novel self-supervised time-to-event objective that extracts robust patient representations from noisy, irregular, multivariate time series. As a result, ICareFM can generalize to new tasks and beyond its training distribution, a property we demonstrate through evaluations in a range of out-of-distribution scenarios, including transfer to unseen hospitals and zero-shot inference on previously unobserved tasks. ICareFM consistently outperforms conventional machine learning models and recent foundation model baselines, demonstrating strong generalization, improved data efficiency, and the ability to generate interpretable forecasts. These results establish ICareFM as a scalable and adaptable foundation model for critical care time series, enabling zero-shot clinical prediction and working towards the development of digital patient twins for precision medicine.