VitalFM: A Foundation Model of High-Resolution Vital Signs in the ICU

ICU monitors record physiology every few seconds, yet many prediction models use hourly representations that may obscure short-term changes. Clinical prediction using handcrafted summaries requires domain expertise, whereas sequence models learning directly from measurements may underperform when cohorts and positive labels are limited. We therefore introduce VitalFM, a foundation model that learns representations from dense, partially observed ICU monitoring through self-supervised pre-training, without input imputation. Its bi-axial encoder combines bidirectional state-space modeling over time with masked attention across channels, and pre-training reconstructs an entirely withheld vital sign while preserving its correlations with remaining signals. The source corpus comprises approximately 13.5 million monitoring hours from 79,000 patients, and we evaluate four outcomes in Site A and MIMIC-III. We compare VitalFM against LightGBM, tabular foundation models, and supervised deep sequence models, evaluating both frozen representations and end-to-end fine-tuning. With TabPFN-3 as a fixed classifier, frozen VitalFM representations improve over raw grids by 3.1 AUROC and 2.5 AUPRC points, outperform handcrafted features on Site A, and achieve comparable performance on MIMIC-III. Fine-tuned VitalFM achieves the highest mean AUROC and AUPRC of all methods: slightly above handcrafted features with tabular foundation models, and 3.2 and at least 7.9 AUROC points above handcrafted LightGBM and supervised deep sequence models, respectively. Pre-training accounts for much of this performance: VitalFM w/o pre-training, which uses the identical architecture trained only on the downstream task, is lower by 14.8 AUROC and 8.4 AUPRC points on average. Fine-tuned models also transfer between health systems without destination-site refitting. These results support self-supervised pre-training on dense monitoring for clinical prediction across outcomes and healthcare systems.