[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ai-memory-trick-shrinks-patient-records-by-97-percent":10,"sections":40},{"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":30,"tags":31,"sources":35,"feedback":39,"feedback_at":22,"cost_usd":39,"total_tokens":39},8641,"ai-memory-trick-shrinks-patient-records-by-97-percent","AI Memory Trick Shrinks Patient Records by 97 Percent","Researchers built a recurrent memory system that compresses patient visit histories by 97 percent while getting close to full-history prediction accuracy.","Researchers have built an AI memory system that compresses a patient's entire visit history into a compact, constantly updated summary instead of rereading every record from scratch.\n\nThe system, called ReLMem, pairs a frozen large language model with lightweight adapters that update a fixed-size memory each time a new clinical visit comes in. Rather than reprocessing a patient's full history for every prediction, the model folds new visit data into the existing memory using a training method that aligns compressed and full-history attention outputs on the same queries. On a medication-prediction task built from electronic health records, ReLMem approached the F1 scores of a baseline that kept the complete history, while cutting stored data by 97.1 percent. Against the strongest existing compressed-memory approach under the same storage budget, it improved macro-F1 by 4.66 points and micro-F1 by 4.75 points.\n\nThe pitch here is cost, not magic: feeding a model someone's full, years-long medical record on every query gets expensive fast, and that expense compounds as patient histories grow. A memory that updates incrementally, without rereading everything each time, is the kind of infrastructure change that makes longitudinal EHR modeling practical to deploy rather than just publish.\n\nThe gap between 'approaches' and 'matches' full-history accuracy is the real number to watch. Compression research has a habit of quietly narrowing that gap in follow-up papers, and this reads like version one for patient memory built this way.","[\"ai\",\"healthcare\",\"ehr\",\"machine-learning\"]","2026-09-30T04:00:00.000Z","2026-09-30T16:38:27.297Z","2026-09-30T16:38:33.301Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"Fix the headline\u002Fdek's 'Without Losing Accuracy'\u002F'matched accuracy' claim, which overstates the source's own wording that ReLMem only 'approaches' the full-history F1 score (not matches it), and replace the caveat-only final paragraph with a proper closing that doesn't just trail off on unresolved limitations.","resolved","ai",[30,32,33,34],"healthcare","ehr","machine-learning",[36],{"name":37,"url":38},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.37587",0,{"sections":41},[42,45,49,53,58,63,67,72,77,81,86,91,96,101],{"name":43,"slug":30,"count":44,"latest_published_at":18},"AI",5180,{"name":46,"slug":47,"count":48,"latest_published_at":18},"Security","security",791,{"name":50,"slug":51,"count":52,"latest_published_at":18},"Policy","policy",417,{"name":54,"slug":55,"count":56,"latest_published_at":57},"Deals","deals",284,"2026-09-29T21:00:00.000Z",{"name":59,"slug":60,"count":61,"latest_published_at":62},"Hardware","hardware",194,"2026-09-29T13:16:04.000Z",{"name":64,"slug":65,"count":66,"latest_published_at":18},"Science","science",155,{"name":68,"slug":69,"count":70,"latest_published_at":71},"Consumer Tech","consumer-tech",142,"2026-09-29T18:38:03.000Z",{"name":73,"slug":74,"count":75,"latest_published_at":76},"Software","software",91,"2026-09-25T20:55:00.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":18},"Dev Tools","dev-tools",90,{"name":82,"slug":83,"count":84,"latest_published_at":85},"Startups","startups",83,"2026-09-29T21:51:36.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"General","general",49,"2026-09-28T16:44:57.000Z",{"name":92,"slug":93,"count":94,"latest_published_at":95},"Gaming","gaming",48,"2026-09-25T18:35:21.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":102,"slug":103,"count":104,"latest_published_at":105},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]