Trained on a curated dataset of over 585,000 hours of PSG recordings from approximately 65,000 participants across multiple cohorts, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. SleepFM achieved a C-Index of at least 0.75 (Bonferroni-corrected p < 0.01) for 130 conditions, including all-cause mortality (C-Index: 0.84), dementia (0.85), myocardial infarction (0.81), heart failure (0.80), chronic kidney disease (0.79), stroke (0.78), and atrial fibrillation (0.78).
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