Researchfoundation modelpolysomnographytime to eventcontrastive learning
SleepFM Predicts Over 100 Diseases From Sleep
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9.3
Stanford Medicine researchers publish SleepFM on January 6 in Nature Medicine, an AI foundation model trained on 585,000 hours of polysomnography from 65,000 participants to predict risks for over 100 conditions. Linked to longitudinal records for 35,000 patients (1999–2024), SleepFM identified 130 diseases with concordance index above 0.75, including Parkinson’s, breast and prostate cancer, dementia, and cardiovascular events.
Scoring Rationale
High novelty and robust peer-reviewed validation, with limited immediate clinical deployment due to integration and explainability challenges.
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