AI Model Predicts Long-Term Health Risks from Routine Sleep Study Data

Aug 3, 2026

A new AI foundation model developed by Cleveland Clinic and IBM analyzes routine sleep study data to predict long-term health risks. Trained on over 10,000 polysomnograms from the STARLIT registry, the model identifies five distinct patient risk groups. Patients in the highest-risk group face twice the five-year mortality risk of those in the lowest-risk group. This approach successfully predicts cardiovascular and cognitive decline risks across both men and women, outperforming the traditional apnea-hypopnea index.

Sleep health foundation model

▪The artificial intelligence model was developed by a collaborative team of researchers through the Discovery Accelerator, a 10-year research partnership between Cleveland Clinic and IBM.
▪Researchers developed a transformer-based artificial intelligence foundation model designed to learn general representations of sleep physiology from routine polysomnography data.
▪The artificial intelligence model was trained using data from the Cleveland Clinic Sleep Signals, Testing, and Reports Linked to Patient Traits registry, which contains over 10,000 clinical sleep recordings.

PSG embedding risk stratification

▪Patients classified in the highest-risk group faced twice the risk of dying within five years compared to those in the lowest-risk category.
▪The model's risk stratification performance was independently confirmed in a nationwide patient cohort, the Sleep Heart Health Study.
▪The artificial intelligence model grouped patients into five distinct risk categories based on latent physiological features extracted from overnight sleep studies.

Cardiovascular outcome prediction

▪The model's high-dimensional embeddings of sleep physiology predicted cardiovascular events with equal accuracy across both male and female patients.
▪The artificial intelligence model's sleep embeddings yielded risk groups that showed strong, monotonic associations with incident cardiovascular outcomes.

Neurologic outcome prediction

▪The model's risk stratification for neurological outcomes outperformed conventional diagnostic measures by identifying high-risk patients across different sleep apnea severity levels.
▪The artificial intelligence model identified hidden sleep patterns and latent physiological features linked to cognitive decline and neurological disease.

AHI diagnostic limitations

▪The standard clinical measure for sleep apnea, the apnea-hypopnea index, failed to capture the long-term mortality and disease risks identified by the artificial intelligence model.
▪The apnea-hypopnea index historically performs better in assessing risk for men than for women, whereas the new artificial intelligence model achieved balanced predictive accuracy across both sexes.

Precision sleep medicine implementation

▪Researchers emphasized that further validation in more diverse patient populations is required before the artificial intelligence model can be routinely deployed in clinical care.
▪An estimated 1 million to 4 million polysomnograms are conducted annually in the United States, representing a vast source of underutilized clinical data.