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Ishita Srivastava

Scientists Forecast Over 100 Conditions Through One Night's Sleep Using New AI Model

Researchers at <span>Stanford University</span> have developed a new <span>AI</span> model which can predict more than 100 health conditions including <span><a href="https://www.healthandme.com/health/cancer">cancer</a></span>, mental health, cardiovascular issues and death using <span>sleep</span> study data.

SleepFM uses  polysomnography, a comprehensive sleep assessment that utilizes various sensors to record brain activity, heart activity, respiratory signals, leg movements, eye movements and more to provide an accurate prediction of future disease risk, according to the study published in <a href="https://www.nature.com/articles/s41591-025-04133-4" data-type="tilCustomLink" target="_blank" rel="noopener nofollow noreferrer" rel= "noopener nofollow noreferrer" target= "_blank">Nature </a> on January 6. 

Emmanuel Mignot, Craig Reynolds Professor in Sleep Medicine and co-senior author of the study, <a href="https://www.eurekalert.org/news-releases/1111430" data-type="tilCustomLink" target="_blank" rel="noopener nofollow noreferrer" rel= "noopener nofollow noreferrer" target= "_blank">said</a>, "We record an amazing number of signals when we study sleep. It’s a kind of general physiology that we study for eight hours in a subject who’s completely captive. It’s very data rich."

It remains unclear when SleepFM will be commercially available to the public and whether it will be incorporated into wearable technology such as watches and phones. 

The AI model has been trained on nearly 600,000 hours of sleep data collected from 65,000 participants which had been split into five-second increments to combine multiple body signals, such as brain activity, heart activity, muscle activity, pulse and breathing and understand how they relate to each other.

James Zou, PhD, associate professor of biomedical data science and co-senior author of the study commented, "One of the technical advances that we made in this work is to figure out how to harmonize all these different data modalities so they can come together to learn the same language."

After being trained to identify and link different body signals, SleepFM was taught how to understand standard sleep analysis tasks such as different stages of sleep and diagnosing the severity of sleep apnea, a serious sleep disorder where breathing repeatedly stops and starts often due to airway blockage or the brain failing to signal muscles.

Once the model was seen successfully identifying sleep-related issues, it was paired with the sleep recordings and health data of 35,000 patients collected over 25 years for it to learn how to identify chronic diseases through nighttime body signals.  

<h2>Which Diseases Can It Accurately Predict?</h2>The AI model analyzed over  1,000 disease categories in the health records and found 130 that could be predicted with reasonable accuracy using merely sleep data. The model’s predictions were particularly strong for cancers, pregnancy complications, circulatory conditions and mental disorders. 

Particularly, SleepFM excelled at predicting Parkinson’s disease, dementia, hypertensive heart disease, heart attack, prostate cancer, breast cancer and death.

“We were pleasantly surprised that for a pretty diverse set of conditions, the model is able to make informative predictions,” Zou said.

Even though heart signals were prominently used to predict heart disease and brain signals were predominant in mental health predictions, the researchers noted that it was the combination of all the data modalities that achieved the most accurate predictions.

“The most information we got for predicting disease was by contrasting the different channels. Body constituents that were out of sync. A brain that looks asleep but a heart that looks awake, for example, seemed to spell trouble,” Mignot said.

Zhou added, "From an AI perspective, sleep is relatively understudied. There’s a lot of other AI work that’s looking at pathology or cardiology, but relatively little looking at sleep, despite sleep being such an important part of life. 

"SleepFM is essentially learning the language of sleep."

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Researchers at Stanford University have developed a new AI model which can predict more than 100 health conditions including cancer, mental health, cardiovascular issues and death using sleep study data.SleepFM uses  polysomnography, a comprehensive sleep assessment that utilizes various sensors to record brain activity, heart activity, respiratory signals, leg movements, eye movements and more to provide an accurate prediction of future disease risk, according to the study published in Nature  on January 6. Emmanuel Mignot, Craig Reynolds Professor in Sleep Medicine and co-senior author of the study, said, "We record an amazing number of signals when we study sleep. It’s a kind of general physiology that we study for eight hours in a subject who’s completely captive. It’s very data rich."It remains unclear when SleepFM will be commercially available to the public and whether it will be incorporated into wearable technology such as watches and phones. How Does It Work?The AI model has been trained on nearly 600,000 hours of sleep data collected from 65,000 participants which had been split into five-second increments to combine multiple body signals, such as brain activity, heart activity, muscle activity, pulse and breathing and understand how they relate to each other.James Zou, PhD, associate professor of biomedical data science and co-senior author of the study commented, "One of the technical advances that we made in this work is to figure out how to harmonize all these different data modalities so they can come together to learn the same language."After being trained to identify and link different body signals, SleepFM was taught how to understand standard sleep analysis tasks such as different stages of sleep and diagnosing the severity of sleep apnea, a serious sleep disorder where breathing repeatedly stops and starts often due to airway blockage or the brain failing to signal muscles.Once the model was seen successfully identifying sleep-related issues, it was paired with the sleep recordings and health data of 35,000 patients collected over 25 years for it to learn how to identify chronic diseases through nighttime body signals.  Which Diseases Can It Accurately Predict?The AI model analyzed over  1,000 disease categories in the health records and found 130 that could be predicted with reasonable accuracy using merely sleep data. The model’s predictions were particularly strong for cancers, pregnancy complications, circulatory conditions and mental disorders. Particularly, SleepFM excelled at predicting Parkinson’s disease, dementia, hypertensive heart disease, heart attack, prostate cancer, breast cancer and death.“We were pleasantly surprised that for a pretty diverse set of conditions, the model is able to make informative predictions,” Zou said.Even though heart signals were prominently used to predict heart disease and brain signals were predominant in mental health predictions, the researchers noted that it was the combination of all the data modalities that achieved the most accurate predictions.“The most information we got for predicting disease was by contrasting the different channels. Body constituents that were out of sync. A brain that looks asleep but a heart that looks awake, for example, seemed to spell trouble,” Mignot said.Zhou added, "From an AI perspective, sleep is relatively understudied. There’s a lot of other AI work that’s looking at pathology or cardiology, but relatively little looking at sleep, despite sleep being such an important part of life. "SleepFM is essentially learning the language of sleep."

scientists forecast over 100 conditions through one night's sleep using new ai model

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