Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction

Stanford Medicine researchers have developed SleepFM Clinical, a multimodal sleep foundation model capable of analyzing polysomnography data to predict the long-term risk of over 130 diseases from a single night's sleep. This innovative model leverages dense physiological time series dataincluding brain activity, heart signals, and breathing metricstraining on approximately 585,000 hours of sleep recordings from 65,000 individuals, with data sourced from the Stanford Sleep Medicine Center and linked electronic health records for comprehensive survival analysis. The key advancement lies in SleepFM's ability to learn a shared representation across multiple sleep-related modalities, enabling it