ArticleNature medicine2026
A multimodal sleep foundation model for disease prediction.
Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG.bioRxiv : the preprint server for biology · 2026Article
- Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold.European journal of clinical investigation · 2026Review
- A foundation model for sleep-based risk stratification and clinical outcomes.Nature communications · 2026Article
- Sleep as therapy.Nature medicine · 2026Article
- One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.ArXiv · 2026Article
- Predicting health and disease: a conceptual framework for AI in preventive and precision medicine.BMJ health & care informatics · 2026Review
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- Sleep EEG foundation models reveal within-stage microstructure that improves health screening beyond traditional stages.Research square · 2026Article
- Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group.Research square · 2026Article
- Wearable Sleep Measures May Improve Machine Learning Prediction of Home-Based Pulmonary Rehabilitation Engagement Among Patients With Chronic Obstructive Pulmonary Disease: A Proof-of-Concept Study.Mayo Clinic proceedings. Digital health · 2026Article
- CardioFM: A Multimodal Foundation Model for Joint ECG and PPG Representation Learning.Research square · 2026Article
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Review
- The Association of Periodontitis with Circadian Rhythm Disorders and Sleep-A Scoping Review.Medicina (Kaunas, Lithuania) · 2026Article
- Is accuracy enough? trust and barriers to AI-based clinical decision support in clinical neurophysiology.Clinical neurophysiology practice · 2026Article
- Construction and verification of a prediction model for sleep disorders in older patients with coronary heart disease based on machine learning algorithms.Frontiers in medicine · 2026Article
- Sleep stage classification using instantaneous heart and breathing rates with a TCN-BiGRU-SE model.Frontiers in bioengineering and biotechnology · 2026Article
- Monitoring and modulating interconnected physiological systems in space using portable closed-loop technologies.Frontiers in network physiology · 2026Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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)-the gold standard for sleep analysis-captures 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 hours 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 P < 0.01), 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-a dataset that was excluded from pretraining-and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks, achieving mean F
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.