AI analysis of sleep data can predict disease risk
Artificial intelligence can analyze large amounts of sleep data and identify patterns that make it possible to predict the risk of over 130 diseases.
AI is already being used today to analyze the large datasets generated in sleep studies. One example is USleep, a model for classifying sleep stages that has shown promising results in the diagnosis of narcolepsy. Photo: Bax Lindhardt.
Thursday 23 April 2026
Peter Aagaard Brixen
Artificial intelligence can identify patterns in sleep and detect correlations with diseases with a high degree of accuracy. Researchers from Stanford University, DTU, and international partners have developed a so-called self-learning AI model (see fact box) to analyze hundreds of thousands of hours of sleep measurements and identify patterns in signals from the brain, heart, muscles, and breathing.
The researchers use their model, SleepFM, to predict the risk of up to 130 diseases. The model has been trained on more than 585,000 hours of sleep measurements, known as polysomnography (PSG) (see fact box), from approximately 65,000 participants, making it one of the most comprehensive sleep models to date. The study “A multimodal sleep foundation model for disease prediction” has been published in the renowned journal Nature Medicine.
One of the researchers behind the study, Magnus Ruud Kjær, a PhD student at DTU Health Tech, hopes that the team behind the AI model will have the opportunity to test it in a hospital.
“When a sleep study is already being conducted - for example, to diagnose sleep apnea - AI could potentially be used to provide a broad assessment of disease risk without additional tests. It could identify patients who should be referred further within the healthcare system,” he says.
The researchers show that their model, with an accuracy of up to 85 percent, can predict diseases such as dementia, chronic kidney disease, atrial fibrillation, and heart attack. The researchers can validate their predictions by comparing SleepFM’s results with anonymized data on disease outcomes recorded among the participants. This is possible because the researchers have gained access to sleep records from the U.S. Sleep Heart Health Study and the Stanford Sleep Cohort, which contain data from several large hospital sleep clinics.
Sleep as a data stream
According to the study, sleep is a rich physiological data stream that can reveal a great deal about our overall health status, but which has not yet been fully utilized.
SleepFM represents a breakthrough in both sleep research and in systems for predicting future disease risk. Whereas previous AI models primarily focus on determining sleep stages, SleepFM expands the field by linking sleep patterns to broad disease risk. This new approach has the potential to change the way healthcare professionals currently screen for and monitor a wide range of chronic diseases.
Assistant Professor at DTU Health Tech and co-author Andreas Brink-Kjær is continuing to work on the project to better understand the body’s measurable signals - so-called biomarkers - that SleepFM identifies to predict diseases.
Facts
Self-learning AI
The researchers behind the AI model SleepFM have built a large mathematical system capable of learning correlations in data from sleep measurements.
The researchers did not manually specify which specific patterns the model should look for. Instead, they have developed a self-learning algorithm that automatically identifies structures and signals in sleep data through so-called self-supervised contrastive learning.
The model is therefore a mathematical network that adjusts its own internal parameters as it examines more and more examples. It is this process that makes it possible to discover new correlations with diseases that humans have not previously identified.
Sleep Studies
Polysomnography (PSG) is a comprehensive sleep study in which multiple physiological signals are measured during sleep.
Typically, the brain’s electrical activity, eye movements, muscle activity, heart rate, breathing patterns, oxygen saturation, and leg movements are recorded.
The purpose is to obtain a complete picture of what happens in the body while sleeping. PSG is the most comprehensive and reliable method for diagnosing sleep disorders such as sleep apnea and narcolepsy.
