AI Model Uncovers Hidden Sleep Patterns That Predict Long-Term Health Risks

AI model can use routine sleep study data to identify patients' long-term health risks

A novel AI model can use information collected during routine sleep studies to identify patients' long-term health risks, according to a new study published in Nature Communications. Developed by a multidisciplinary research team, the model uncovered hidden sleep patterns linked to risks including heart disease, cognitive decline and death. The findings also suggest that routine medical tests may contain substantially more physiologic information than current clinical practice extracts from them. In this case, AI identified meaningful signals in standard overnight sleep study data that are not captured by conventional summary measures alone. The research revealed clinically meaningful patient subtypes with sharply different long-term health risks. Patients in the highest-risk group had twice the mortality risk over the next five years compared to those in the lowest-risk group, a distinction that was not captured by the standard clinical measure used to assess sleep apnea severity, the apnea-hypopnea index. Each year, an estimated 1 to 4 million polysomnograms, or in-lab sleep studies, are performed in the United States, typically to evaluate sleep apnea. While these studies collect rich data on each patient's brains, lungs, muscles and heart, clinicians historically have focused on a small subset of that information to grade sleep apnea severity. For decades we have distilled an overnight sleep study into a handful of summary measures. AI gives us the opportunity to move beyond those summaries and learn from the full richness of sleep physiology." Reena Mehra, M.D., professor of medicine, University of Washington and study's senior clinical author The model was developed by a collaborative team of sleep physicians, AI researchers, data scientists and neuroscientists brought together through the Discovery Accelerator, a 10-year joint research partnership between Cleveland Clinic and IBM aimed at advancing the pace of discovery in life sciences through AI and quantum computing. Using data from the Cleveland Clinic Sleep Signals, Testing, and Reports Linked to Patient Traits (STARLIT) registry, the researchers grouped patients into five risk categories. The model also predicted outcomes well for men and women, while the apnea hypopnea index has historically performed better in men. The findings were independently confirmed in a nationwide patient cohort. "Modern AI lets us recover much more of the information contained in a night's worth of sleep physiology, revealing clinically meaningful patient groups with very different long-term health risks," said Jeffrey Rogers, Ph.D., the corresponding author and professor adjunct, neurosurgery, Yale School of Medicine. "These findings demonstrate that routine medical tests can contain substantially more physiologic information than current clinical practice extracts from them." The model could also help researchers better understand how sleep impacts health outcomes. By looking beyond traditional measures, the approach uses AI to detect latent physiologic features invisible to the human eye and extract prognostic biomarkers that help stratify risk for cardiovascular and neurologic disease, and survival, opening the door to earlier and more personalized care. "Sleep is foundational to health and wellness," said Matheus Lima Diniz Araujo, Ph.D., a sleep researcher at Cleveland Clinic. "Nearly 70 million Americans live with chronic disorders of sleep and wakefulness, affecting daily functioning and overall health. This discovery offers a more personalized approach to sleep medicine, by potentially expanding the value of routine sleep testing and reinforcing the key role sleep plays in chronic disease." Carl Saab, Ph.D., a professor of biomedical engineering and Chief Scientist of Cleveland Clinic's Discovery Accelerator, said, "The next step is to validate these findings in diverse populations and expand collaborations among medical and technical experts, industry partners and professional society stakeholders." "Sleep is increasingly recognized as a critical component of health, yet the physiological information captured during sleep remains largely underused," said Erhan Bilal, Ph.D., lead author of the study. "Because everyone sleeps, sleep studies offer a remarkable window into human health that extends far beyond the diagnosis of sleep disorders. Our work shows how foundation models can begin to unlock the richness of these complex signals. And this is only the beginning." Source: Cleveland Clinic Journal reference: Bilal, E., et al. (2026). A foundation model for sleep-based risk stratification and clinical outcomes. Nature Communications. DOI: 10.1038/s41467-026-75326-9. https://www.nature.com/articles/s41467-026-75326-9

AI Identifies Previously Unrecognized Health Insights in Routine Sleep Studies | Newswise

Interdisciplinary research team develops foundation model that identifies patient groups with markedly different long-term health risks Newswise -- August 3, 2026, 5:00 AM EDT: A novel AI model can use information collected during routine sleep studies to identify patients' long-term health risks, according to a new study published in Nature Communications. Developed by a multidisciplinary research team that included Cleveland Clinic, the model uncovered hidden sleep patterns linked to risks including heart disease, cognitive decline and death. The findings also suggest that routine medical tests may contain substantially more physiologic information than current clinical practice extracts from them. In this case, AI identified meaningful signals in standard overnight sleep study data that are not captured by conventional summary measures alone. The research revealed clinically meaningful patient subtypes with sharply different long-term health risks. Patients in the highest-risk group had twice the mortality risk over the next five years compared to those in the lowest-risk group, a distinction that was not captured by the standard clinical measure used to assess sleep apnea severity, the apnea-hypopnea index. Each year, an estimated 1 to 4 million polysomnograms, or in-lab sleep studies, are performed in the United States, typically to evaluate sleep apnea. While these studies collect rich data on each patient's brains, lungs, muscles and heart, clinicians historically have focused on a small subset of that information to grade sleep apnea severity. "For decades we have distilled an overnight sleep study into a handful of summary measures," said Reena Mehra, M.D., professor of medicine at the University of Washington and the study's senior clinical author. "AI gives us the opportunity to move beyond those summaries and learn from the full richness of sleep physiology." The model was developed by a collaborative team of sleep physicians, AI researchers, data scientists and neuroscientists brought together through the Discovery Accelerator, a 10-year joint research partnership between Cleveland Clinic and IBM aimed at advancing the pace of discovery in life sciences through AI and quantum computing. Using data from the Cleveland Clinic Sleep Signals, Testing, and Reports Linked to Patient Traits (STARLIT) registry, the researchers grouped patients into five risk categories. The model also predicted outcomes well for men and women, while the apnea hypopnea index has historically performed better in men. The findings were independently confirmed in a nationwide patient cohort. "Modern AI lets us recover much more of the information contained in a night's worth of sleep physiology, revealing clinically meaningful patient groups with very different long-term health risks," said Jeffrey Rogers, Ph.D., the corresponding author and professor adjunct, neurosurgery, Yale School of Medicine. "These findings demonstrate that routine medical tests can contain substantially more physiologic information than current clinical practice extracts from them." The model could also help researchers better understand how sleep impacts health outcomes. By looking beyond traditional measures, the approach uses AI to detect latent physiologic features invisible to the human eye and extract prognostic biomarkers that help stratify risk for cardiovascular and neurologic disease, and survival, opening the door to earlier and more personalized care. "Sleep is foundational to health and wellness," said Matheus Lima Diniz Araujo, Ph.D., a sleep researcher at Cleveland Clinic. "Nearly 70 million Americans live with chronic disorders of sleep and wakefulness, affecting daily functioning and overall health. This discovery offers a more personalized approach to sleep medicine, by potentially expanding the value of routine sleep testing and reinforcing the key role sleep plays in chronic disease." Carl Saab, Ph.D., a professor of biomedical engineering and Chief Scientist of Cleveland Clinic's Discovery Accelerator, said, "The next step is to validate these findings in diverse populations and expand collaborations among medical and technical experts, industry partners and professional society stakeholders." "Sleep is increasingly recognized as a critical component of health, yet the physiological information captured during sleep remains largely underused," said Erhan Bilal, Ph.D., lead author of the study. "Because everyone sleeps, sleep studies offer a remarkable window into human health that extends far beyond the diagnosis of sleep disorders. Our work shows how foundation models can begin to unlock the richness of these complex signals. And this is only the beginning." The research team included Erhan Bilal, Ph.D.; Matheus Lima Diniz Araujo, Ph.D.; Kristen Beck, Ph.D.; Catherine Heinzinger, D.O.; Samer Ghosn, B.S.; Nancy Foldvary-Schaefer, D.O.; Carl Saab, Ph.D.; Jeffrey Rogers, Ph.D.; and Reena Mehra, M.D.