{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:37:43Z","timestamp":1791999463522,"version":"4.4.0"},"posted":{"date-parts":[[2026,3,24]]},"group-title":"Health Informatics","reference-count":99,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,4,3]]},"abstract":"<jats:title>\n                  A\n                  <jats:sc>bstract<\/jats:sc>\n                <\/jats:title>\n                <jats:p>\n                  Sleep disturbances lead to cardiovascular (CV), metabolic, and neurological diseases. While in-lab polysomnography (PSG) is the gold standard for measuring sleep disturbances, at-home PSG (hPSG) are more cost-effective, less resource intensive, and have been extensively used in large-scale studies. Further, hPSG devices that record EEG, EOG, and EMG are developing rapidly and collect similar data compared to in-lab PSG . However, the link between hPSG measurements and future disease risk is not well understood. We present SleepJEPA, a foundational sleep study representation model trained via a joint embedding predictive architecture that learns full night, multichannel sleep representations using hPSGs in the latent space, uncovering high-dimensional information that more precisely informs future health outcomes than standard clinical scoring. SleepJEPA was trained, validated, and tested with 422,035 hours of sleep signa.0l data from 55,518 sleep studies. It accurately estimates 1-to 15-year cumulative risk using a discrete hazard loss function for 10 conditions, including angina (integrated area under the receiver operating characteristic curve at 15 years [\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>15<\/jats:sub>\n                  ] = 0.73), CV disease death (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>15<\/jats:sub>\n                  = 0.83), congestive heart failure (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>15<\/jats:sub>\n                  = 0.85), coronary heart disease death (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>15<\/jats:sub>\n                  = 0.85), incident cognitive decline (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>10<\/jats:sub>\n                  = 0.65), diabetes (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>10<\/jats:sub>\n                  = 0.82), hypertension (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>10<\/jats:sub>\n                  = 0.79), obstructive sleep apnea (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>5<\/jats:sub>\n                  = 0.86), myocardial infarction (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>15<\/jats:sub>\n                  = 0.80), and stroke (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>15<\/jats:sub>\n                  = 0.78). We also show that these outcomes can be accurately predicted in independent cohorts, including CV disease death (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>10<\/jats:sub>\n                  = 0.79), coronary heart disease death (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>10<\/jats:sub>\n                  = 0.74), obstructive sleep apnea (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>5<\/jats:sub>\n                  = 0.77), and stroke (\n                  <jats:italic>iAUC<\/jats:italic>\n                  <jats:sub>10<\/jats:sub>\n                  = 0.60). We report increased performance across all outcomes compared to other sleep foundation models, such as SleepFM. Through correlational analyses and explainability approaches, we illustrate features most informative for risk at different horizons. We further demonstrate SleepJEPA can effectively estimate sleep stages with high accuracy (F1 = 0.77 [95% CI: 0.76 - 0.77]), objective daytime sleepiness with modest performance (AUC = 0.64 [0.57 - 0.71]), and type 1 narcolepsy (AUC = 0.88 [0.68 - 0.97]), allowing for comprehensive labeling and disease risk assessment from hPSG signals.\n                <\/jats:p>","DOI":"10.64898\/2026.03.20.26348834","type":"posted-content","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T22:55:12Z","timestamp":1774392912000},"source":"Crossref","is-referenced-by-count":2,"title":["SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5751-4884","authenticated-orcid":false,"given":"Benjamin","family":"Fox","sequence":"first","affiliation":[{"name":"The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joy","family":"Jiang","sequence":"additional","affiliation":[{"name":"The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5951-1866","authenticated-orcid":false,"given":"Dung T.","family":"Hoang","sequence":"additional","affiliation":[{"name":"The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Brush","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Boulgakov","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1045-8616","authenticated-orcid":false,"given":"Sajila","family":"Wickramaratne","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankit","family":"Sakhuja","sequence":"additional","affiliation":[{"name":"The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oren","family":"Cohen","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8712-3553","authenticated-orcid":false,"given":"Mayte","family":"Suarez-Farinas","sequence":"additional","affiliation":[{"name":"Division of Digital and Data Driven Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1828-1216","authenticated-orcid":false,"given":"Neomi A","family":"Shah","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Division of Digital and Data Driven 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