[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-ai-score-predicts-patient-risk-better-than-old-formulas":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},5516,"new-ai-score-predicts-patient-risk-better-than-old-formulas","New AI Score Predicts Patient Risk Better Than Old Formulas","A new machine-learning model turns diagnosis codes into a single risk score that beats old comorbidity indexes across multiple outcomes.","A new algorithm scores how sick a hospital patient really is - and early results say it beats the decades-old formulas doctors still lean on.\n\nFor years, clinicians have used comorbidity indexes like Charlson and Elixhauser to size up patient risk: tally a patient's chronic conditions, weight each one, and add up a score. Researchers say these tools have two problems. They were built mainly to predict death, so they line up poorly with other outcomes like readmission or complications. And because they're just weighted addition, they can't capture how conditions interact in nonlinear ways. The new model, called MLCI, instead learns to convert a patient's diagnosis codes into a single number by maximizing a statistical measure of dependence - nHSIC, a way of capturing correlation even when the relationship isn't a straight line - between that score and several clinical outcomes at once. Tested on multiple benchmark electronic health record datasets, it beat established baselines across several evaluation metrics.\n\nComorbidity scores aren't academic trivia - they drive hospital risk adjustment, quality comparisons, and research cohort matching, all quietly, in the background of how care gets measured. A tool that generalizes past mortality could reshape which hospitals look good or bad on paper, and how research studies decide who counts as a \"sicker\" patient. That's a bigger deal than the modest framing of the paper suggests.\n\nCharlson has survived four decades in medicine partly because any clinician can compute it with a pen. Whether hospitals will trust - or even be allowed to trust - a black-box score trained on benchmark datasets is a separate question from whether it scores well on a leaderboard.","[\"comorbidity-index\",\"machine-learning\",\"electronic-health-records\",\"healthcare-ai\"]","2026-08-18T04:00:00.000Z","2026-08-18T23:29:58.547Z","2026-08-18T23:30:09.734Z","published",null,[],"ai",[26,27,28,29],"comorbidity-index","machine-learning","electronic-health-records","healthcare-ai",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.17450",0,{"sections":36},[37,41,45,50,55,60,65,70,75,79,84,89,94,99],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",3293,"2026-08-20T04:00:00.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":40},"Security","security",435,{"name":46,"slug":47,"count":48,"latest_published_at":49},"Policy","policy",210,"2026-08-19T09:32:27.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",140,"2026-08-19T18:25:42.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",95,"2026-08-18T16:05:00.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Science","science",90,"2026-08-19T18:41:02.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Software","software",73,"2026-08-18T07:51:50.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":18},"Dev Tools","dev-tools",69,{"name":80,"slug":81,"count":82,"latest_published_at":83},"Startups","startups",47,"2026-08-19T19:13:46.000Z",{"name":85,"slug":86,"count":87,"latest_published_at":88},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",33,"2026-08-18T22:18:13.000Z",{"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"]