AI Model Stratifies Sleep-Based Risk and Clinical Outcomes

A multidisciplinary research team has developed a foundation model for sleep health that captures complex physiologic signals and extracts prognostic biomarkers that stratify risk for cardiovascular and neurologic disease and survival. The researchers, including Cleveland Clinic physicians and scientists, recently published study findings in Nature Communications validating the generalizability of the framework and providing what they describe as “a scalable path toward precision sleep medicine.”

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“More than 70 million Americans live with chronic disorders of sleep and wakefulness, and these conditions impact overall health,” says study co-author Matheus Lima Diniz Araujo, PhD, a computer scientist with Cleveland Clinic’s Sleep Disorders Center. “This discovery potentially expands the value of routine sleep testing and reinforces the key role sleep plays in chronic disease.”

Model development and performance

Clinical interpretation of polysomnography (PSG) — the gold-standard diagnostic test for sleep integrity — is typically limited to the apnea-hypopnea index (AHI). While the AHI is essential for diagnosing sleep apnea, it reflects only a narrow portion of sleep physiology. The goal of the research team was to apply the foundation model and tap into the full richness of PSG time-series data.