Summary: Researchers developed an artificial intelligence model capable of extracting hidden physiological signals from routine polysomnography data to predict long-term health risks.
Analyzing data from Cleveland Clinic’s STARLIT registry alongside a nationwide cohort, the AI model identified five distinct patient risk subtypes with vastly different health trajectories. Patients categorized into the highest-risk group faced double the five-year mortality risk compared to the lowest-risk group, a prognostic distinction invisible to conventional sleep apnea diagnostic measures.
Key Facts
- Two-Fold Mortality Risk Separation: The AI model stratified patients into five distinct risk categories, showing that individuals in the highest-risk tier had a 100 percent increase in five-year mortality risk compared to those in the lowest-risk tier.
- Superiority Over Conventional AHI: The prognostic risk stratification succeeded where the traditional Apnea-Hypopnea Index (AHI) failed, capturing latent physiological features across brain, lung, muscle, and cardiac signals that standard metrics miss.
- Sex-Balanced Predictive Accuracy: While the traditional AHI metric historically performs better in male populations, the new AI foundation model predicted cardiovascular, neurological, and mortality outcomes with equal high accuracy across both men and women.
- Unlocking Underutilized Clinical Data: Demonstrates that routine polysomnograms, 1 to 4 million of which are conducted annually in the United States, contain vast amounts of unused prognostic data capable of driving early preventative healthcare.
Source: Cleveland Clinic
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,” 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.
Key Questions Answered:
A: AHI compresses an entire night of complex physiological recordings into a single summary number measuring breathing pauses per hour. This oversimplification discards detailed continuous data regarding heart rate variability, brainwave architecture, muscle tone, and subtle oxygen desaturation patterns that directly reflect cardiovascular, neurological, and metabolic strain.
A: Clinical sleep studies have historically exhibited sex bias because the AHI metric correlates better with male presentation of sleep apnea. By analyzing full-spectrum physiological signals rather than relying solely on upper-airway obstruction counts, the AI model achieves equal predictive power for cardiovascular disease, cognitive decline, and mortality across both female and male patients.
A: The research team plans to validate the model across diverse international populations, expand technical and industrial collaborations, and integrate the algorithm into existing sleep lab software to provide automated risk stratification reports alongside standard clinical metrics.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this AI and sleep research news
Author: Alicia Reale
Source: Cleveland Clinic
Contact: Alicia Reale – Cleveland Clinic
Image: The image is credited to Neuroscience News
Original Research: Open access.
“A foundation model for sleep-based risk stratification and clinical outcomes” by Erhan Bilal, Matheus Lima Diniz Araujo, Kristen L. Beck, Catherine M. Heinzinger, Samer Ghosn, Carl Y. Saab, Nancy Foldvary-Schaefer, Jeffrey L. Rogers & Reena Mehra. Nature Communications
DOI:10.1038/s41467-026-75326-9
Abstract
A foundation model for sleep-based risk stratification and clinical outcomes
Clinical sleep studies capture multiple physiologic signals, yet interpretation is often reduced to single summary measures of limited prognostic value, such as the apnea–hypopnea index. We present a foundation model that learns rich representations of sleep physiology from more than 10,000 clinical sleep recordings linked to electronic medical records.
Here we show that sleep physiology contains latent risk structure invisible to conventional metrics, identifying five patient risk groups with markedly different trajectories for mortality, cardiovascular, and neurological disease.
The highest-risk group shows more than double the mortality risk of the lowest, whereas apnea–hypopnea index severity categories show limited predictive value.
The framework generalizes to the independent Sleep Heart Health Study, distinguishing high- and low-risk patients despite lower-resolution data.
We demonstrate that foundation models recover clinically meaningful risk information embedded in routine sleep recordings that conventional metrics systematically miss, providing a scalable path to precision sleep medicine.
