A staff of Stanford Medication researchers have launched SleepFM Medical, a multimodal sleep basis mannequin that learns from medical polysomnography and predicts long run illness threat from a single evening of sleep. The analysis work is revealed in Nature Medication and the staff has launched the medical code because the open supply sleepfm-clinical repository on GitHub below the MIT license.
From in a single day polysomnography to a normal illustration
Polysomnography information mind exercise, eye actions, coronary heart indicators, muscle tone, respiration effort and oxygen saturation throughout a full evening in a sleep lab. It’s the gold customary check in sleep medication, however most medical workflows use it just for sleep staging and sleep apnea analysis. The analysis staff deal with these multichannel indicators as a dense physiological time collection and practice a basis mannequin to study a shared illustration throughout all modalities.
SleepFM is educated on about 585,000 hours of sleep recordings from about 65,000 folks, drawn from a number of cohorts. The biggest cohort comes from the Stanford Sleep Medication Heart, the place about 35,000 adults and youngsters had in a single day research between 1999 and 2024. That medical cohort is linked to digital well being information, which later allows survival evaluation for a whole lot of illness classes.
Mannequin structure and pretraining goal
On the modeling degree, SleepFM makes use of a convolutional spine to extract native options from every channel, adopted by consideration based mostly aggregation throughout channels and a temporal transformer that operates over brief segments of the evening. The identical core structure already appeared in earlier work on SleepFM for sleep staging and sleep disordered respiration detection, the place it confirmed that studying joint embeddings throughout mind exercise, electrocardiography and respiratory indicators improves downstream efficiency.
The pretraining goal is depart one out contrastive studying. For every brief time phase, the mannequin builds separate embeddings for every modality group, corresponding to mind indicators, coronary heart indicators and respiratory indicators, after which learns to align these modality embeddings in order that any subset predicts the joint illustration of the remaining modalities. This method makes the mannequin sturdy to lacking channels and heterogeneous recording montages, that are frequent in actual world sleep labs.
After pretraining on unlabeled polysomnography, the spine is frozen and small activity particular heads are educated. For normal sleep duties, a light-weight recurrent or linear head maps embeddings to sleep phases or apnea labels. For medical threat prediction, the mannequin aggregates the complete evening right into a single affected person degree embedding, concatenates primary demographics corresponding to age and intercourse, after which feeds this illustration right into a Cox proportional hazards layer for time to occasion modeling.
Benchmarks on sleep staging and apnea
Earlier than transferring to illness prediction, the analysis staff verified that SleepFM competes with specialist fashions on customary sleep evaluation duties. Prior work already confirmed {that a} easy classifier on prime of SleepFM embeddings outperforms finish to finish convolutional networks for sleep stage classification and for detection of sleep disordered respiration, with beneficial properties in macro AUROC and AUPRC on a number of public datasets.
Within the medical examine, the identical pretrained spine is reused for sleep staging and apnea severity classification throughout multi heart cohorts. Outcomes reported within the analysis paper present that SleepFM matches or exceeds present instruments corresponding to conventional convolutional fashions and different automated sleep staging techniques, which validates that the illustration captures core sleep physiology and never solely statistical artifacts from a single dataset.
Predicting 130 illnesses and mortality from one evening of sleep
The core contribution of this Stanford’s analysis paper is illness prediction. The analysis staff maps analysis codes within the Stanford digital well being information to phecodes and defines greater than 1,000 candidate illness groupings. For every phecode, they compute time to first analysis after the sleep examine and match a Cox mannequin on prime of SleepFM embeddings.
SleepFM identifies 130 illness outcomes whose dangers are predictable from a single evening of polysomnography with sturdy discrimination. These embrace all trigger mortality, dementia, myocardial infarction, coronary heart failure, continual kidney illness, stroke, atrial fibrillation, a number of cancers and a number of psychiatric and metabolic issues. For a lot of of those circumstances, efficiency metrics corresponding to concordance index and space below the receiver working curve are in ranges similar to established threat scores, despite the fact that the mannequin makes use of solely sleep recordings plus primary demographics.
The reporting additionally notes that for some cancers, being pregnant problems, circulatory circumstances and psychological well being issues, predictions based mostly on SleepFM attain accuracy ranges round 80 % for multi yr threat home windows. This means that delicate patterns within the coordination between mind, coronary heart and respiration indicators carry details about latent illness processes that aren’t but clinically seen.
Comparability with easier baselines
To evaluate added worth, the analysis staff in contrast SleepFM based mostly threat fashions with two baselines. The primary makes use of solely demographic options corresponding to age, intercourse and physique mass index. The second trains an finish to finish mannequin straight on polysomnography and outcomes, with out unsupervised pretraining. Throughout most illness classes, the pretrained SleepFM illustration mixed with a easy survival head yields greater concordance and better lengthy horizon AUROC than each baselines.
This analysis clearly reveals that the acquire comes much less from a fancy prediction head and extra from the muse mannequin that has realized a normal illustration of sleep physiology. In observe, because of this medical facilities can reuse a single pretrained spine, study small web site particular heads with comparatively modest labeled cohorts and nonetheless method state-of-the-art efficiency.
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