Stanford Medicine researchers have developed SleepFM Clinical, an AI model that predicts long-term disease risk from a single night of sleep using clinical polysomnography. This innovative model, trained on 585,000 hours of sleep data, utilizes a convolutional backbone and attention-based aggregation to learn shared representations across various physiological signals. SleepFM's predictive power spans over 130 disease outcomes, including heart disease, dementia, and certain cancers, with accuracy levels comparable to established risk scores. By leveraging a general representation of sleep physiology, this model allows clinical centers to achieve state-of-the-art performance with minimal labeled data. This matters because it offers a groundbreaking approach to early disease detection, potentially transforming preventative healthcare.
