[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ml-model-uses-circadian-scores-to-screen-for-depression":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},3975,"ml-model-uses-circadian-scores-to-screen-for-depression","ML Model Uses Circadian Scores to Screen for Depression","Researchers built a depression screening tool that compresses sleep, activity, and social data into a single score, hitting 0.825 AUC on 15,233 participants.","A new machine learning framework screens for depression by collapsing multi-domain behavioral data into a single \"Circadian Rhythm Score\" rather than treating sleep, activity, and social patterns as separate signals.\n\nThe research, tested on the China Health and Retirement Longitudinal Study with 15,233 participants, constructs the composite score to maximize how well it separates depressed from non-depressed individuals while keeping the underlying behavioral meaning intact. The model then feeds that score into gradient-boosted trees paired with SHAP analysis, which surfaces why the model made a given prediction rather than just whether risk is high or low. The system reached an ROC-AUC of 0.825, meaning it correctly ranked a depressed individual above a non-depressed one about 82.5% of the time.\n\nWhat sets this apart from earlier work is the shift from \"who is at risk\" to \"what would actually help.\" The framework layers in counterfactual regression to estimate dose-dependent effects of behavioral changes, spitting out concrete thresholds: roughly 300 MET-minutes of exercise per week as a minimum effective dose, and around 65 minutes of restorative napping for sleep-deprived individuals. That kind of intervention-oriented output is harder to get from a model that just predicts a probability.\n\nDepression screening via wearables and passive data has attracted steady research interest, but most prior work stops at prediction. This approach borrows from causal inference to edge toward recommendation — a meaningful step, though the gap between a research dataset and a clinical deployment remains wide.","[\"machine learning\",\"mental health\",\"circadian rhythm\",\"healthcare\"]","2026-07-07T04:00:00.000Z","2026-07-07T13:55:17.604Z","2026-07-07T13:55:20.558Z","published",null,[],"ai",[26,27,28,29],"machine learning","mental health","circadian rhythm","healthcare",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.04648",0,{"sections":36},[37,41,46,51,56,61,66,71,76,80,85,89,94,99],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":45},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":18},"Dev Tools","dev-tools",59,{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":86,"slug":87,"count":83,"latest_published_at":88},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]