In this talk, we discuss the paper “A multimodal sleep foundation model for disease prediction” by Rahul Thapa et al., Nature Medicine, 2026.<\/p>\n
Abstract<\/strong><\/p>\n Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)\u2014the gold standard for sleep analysis\u2014captures rich physiological signals but is underutilized due to challenges in standardization, generalizability and multimodal integration. To address these challenges, we developed SleepFM, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations. Trained on a curated dataset of over 585,000\u2009hours of PSG recordings from approximately 65,000 participants across several cohorts, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. From one night of sleep, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected\u00a0P<\/i>\u2009<\u20090.01), 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). Moreover, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study\u2014a dataset that was excluded from pretraining\u2014and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks, achieving mean\u00a0F<\/i>1<\/sub>\u00a0scores of 0.70\u20130.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence. This work shows that foundation models can learn the language of sleep from multimodal sleep recordings, enabling scalable, label-efficient analysis and disease prediction.<\/p>\n","protected":false},"excerpt":{"rendered":" In this talk, we discuss the paper “A multimodal sleep foundation model for disease prediction” by Rahul Thapa et al., Nature Medicine, 2026. Abstract Sleep is a fundamental biological process … <\/p>\n