Known as SleepFM, the model was trained on nearly 600,000 hours of sleep data collected from 65,000 participants. The sleep data comes from polysomnography, a comprehensive sleep assessment that uses various sensors to record brain activity, heart activity, respiratory signals, leg movements, eye movements and more.

Stanford Medicine scientists and their colleagues created the first artificial intelligence model that can predict more than 100 health conditions from one night’s sleep.

“SleepFM is essentially learning the language of sleep,” Zou said.

The model was able to incorporate multiple streams of data — electroencephalography, electrocardiography, electromyography, pulse reading and breathing airflow, for example — and glean how they relate to each other.

To achieve this, the researchers developed a new training technique,... See more