A workforce of Stanford Medication researchers have launched SleepFM Scientific, a multimodal sleep basis mannequin that learns from scientific polysomnography and predicts long run illness threat from a single night time of sleep. The analysis work is printed in Nature Medication and the workforce has launched the scientific code because the open supply sleepfm-clinical repository on GitHub below the MIT license.
From in a single day polysomnography to a common illustration
Polysomnography data mind exercise, eye actions, coronary heart indicators, muscle tone, respiration effort and oxygen saturation throughout a full night time in a sleep lab. It’s the gold normal take a look at in sleep medication, however most scientific workflows use it just for sleep staging and sleep apnea analysis. The analysis workforce 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 skilled on about 585,000 hours of sleep recordings from about 65,000 individuals, drawn from a number of cohorts. The biggest cohort comes from the Stanford Sleep Medication Heart, the place about 35,000 adults and kids had in a single day research between 1999 and 2024. That scientific cohort is linked to digital well being data, which later permits survival evaluation for lots of of illness classes.
Mannequin structure and pretraining goal
On the modeling stage, 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 night time. 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 go away one out contrastive studying. For every brief time section, 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 strong to lacking channels and heterogeneous recording montages, that are widespread in actual world sleep labs.
After pretraining on unlabeled polysomnography, the spine is frozen and small activity particular heads are skilled. For traditional sleep duties, a light-weight recurrent or linear head maps embeddings to sleep phases or apnea labels. For scientific threat prediction, the mannequin aggregates the complete night time right into a single affected person stage embedding, concatenates fundamental 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 shifting to illness prediction, the analysis workforce verified that SleepFM competes with specialist fashions on normal 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 good points in macro AUROC and AUPRC on a number of public datasets.
Within the scientific examine, the identical pretrained spine is reused for sleep staging and apnea severity classification throughout multi middle cohorts. Outcomes reported within the analysis paper present that SleepFM matches or exceeds current instruments corresponding to conventional convolutional fashions and different automated sleep staging methods, 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 night time of sleep
The core contribution of this Stanford’s analysis paper is illness prediction. The analysis workforce maps analysis codes within the Stanford digital well being data 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 night time of polysomnography with robust discrimination. These embrace all trigger mortality, dementia, myocardial infarction, coronary heart failure, power kidney illness, stroke, atrial fibrillation, a number of cancers and a number of psychiatric and metabolic problems. 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 fundamental demographics.
The reporting additionally notes that for some cancers, being pregnant problems, circulatory circumstances and psychological well being problems, predictions based mostly on SleepFM attain accuracy ranges round 80 p.c for multi yr threat home windows. This implies 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 workforce 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 larger concordance and better lengthy horizon AUROC than each baselines.
This analysis clearly exhibits that the acquire comes much less from a posh prediction head and extra from the muse mannequin that has discovered a common illustration of sleep physiology. In observe, because of this scientific facilities can reuse a single pretrained spine, study small web site particular heads with comparatively modest labeled cohorts and nonetheless method cutting-edge efficiency.
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