{"id":4613,"date":"2026-01-10T12:47:05","date_gmt":"2026-01-10T12:47:05","guid":{"rendered":"https:\/\/academicsolidarity.com\/?p=4613"},"modified":"2026-01-10T12:47:07","modified_gmt":"2026-01-10T12:47:07","slug":"yapay-zekanin-geldigi-nokta-bir-gece-uyku-kaydi-ile-130dan-fazla-hastalik-tahmin-edilebiliyor","status":"publish","type":"post","link":"https:\/\/academicsolidarity.com\/?p=4613&lang=tr","title":{"rendered":"Yapay Zekan\u0131n Geldi\u011fi Nokta: Bir Gece Uyku Kayd\u0131 ile 130\u2019dan Fazla Hastal\u0131k Tahmin Edilebiliyor"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Nature Medicine dergisinde Ocak 2026\u2019da yay\u0131mlanan bir ara\u015ft\u0131rma, uykunun yaln\u0131zca dinlenme an\u0131 olmad\u0131\u011f\u0131n\u0131 ortaya koydu. Uyku ayn\u0131 zamanda gelecekteki hastal\u0131k risklerini \u00f6ng\u00f6rebilen g\u00fc\u00e7l\u00fc bir biyolojik sinyal. Ara\u015ft\u0131rmac\u0131lar, SleepFM adl\u0131 \u00e7ok-modlu bir yapay zek\u00e2 modelini geli\u015ftirerek, tek bir geceye ait uyku kay\u0131tlar\u0131ndan 130\u2019dan fazla hastal\u0131\u011f\u0131n riskini y\u00fcksek do\u011frulukla tahmin edebildiklerini g\u00f6sterdi (<a href=\"https:\/\/www.nature.com\/articles\/s41591-025-04133-4\">https:\/\/www.nature.com\/articles\/s41591-025-04133-4<\/a>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00c7al\u0131\u015fma, yakla\u015f\u0131k 65.000 ki\u015finin 585.000 saatten fazla polisomnografi (PSG) kayd\u0131n\u0131 i\u00e7eren devasa bir veri setine dayan\u0131yor. PSG; beyin dalgalar\u0131 (EEG), kalp ritmi (EKG), solunum, kas aktivitesi gibi bir\u00e7ok fizyolojik sinyali ayn\u0131 anda kaydeden alt\u0131n standart bir uyku inceleme y\u00f6ntemidir. SleepFM, bu farkl\u0131 sinyalleri birlikte analiz ederek uykunun \u201cdilini\u201d \u00f6\u011frenen bir temel model (foundation model) olarak tasarlanm\u0131\u015f.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00c7al\u0131\u015fman\u0131n en \u00e7arp\u0131c\u0131 y\u00f6nlerinden biri, kardiyovask\u00fcler hastal\u0131klar\u0131n \u00f6ng\u00f6r\u00fclmesindeki y\u00fcksek performans. Model, kalp yetmezli\u011fi, inme, miyokard enfarkt\u00fcs\u00fc ve kardiyovask\u00fcler nedenlere ba\u011fl\u0131 \u00f6l\u00fcm gibi sonu\u00e7lar\u0131 anlaml\u0131 do\u011frulukla tahmin edebilmekte.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00d6zellikle ba\u011f\u0131ms\u0131z bir veri setinde yap\u0131lan d\u0131\u015f do\u011frulamada, kardiyovask\u00fcler nedenli \u00f6l\u00fcm i\u00e7in AUROC de\u011feri 0,88 olarak rapor edilmi\u015f. Bu de\u011fer, klinik \u00f6ng\u00f6r\u00fc modellerinde olduk\u00e7a g\u00fc\u00e7l\u00fc bir ay\u0131rt edicili\u011fe i\u015faret eder. Benzer \u015fekilde inme ve kalp yetmezli\u011fi i\u00e7in de y\u00fcksek do\u011fruluk de\u011ferleri elde edilmi\u015f. Modelin bu ba\u015far\u0131s\u0131nda, EKG sinyalleri ile solunum parametrelerinin birlikte de\u011ferlendirilmesinin \u00f6nemli rol oynad\u0131\u011f\u0131 belirtiliyor.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bu bulgular, uyku s\u0131ras\u0131nda kaydedilen fizyolojik sinyallerin, hen\u00fcz klinik olarak ortaya \u00e7\u0131kmam\u0131\u015f kardiyovask\u00fcler riskleri erken d\u00f6nemde yakalayabilece\u011fini d\u00fc\u015f\u00fcnd\u00fcrmektedir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bug\u00fcn klinikte uyku testleri \u00e7o\u011funlukla uyku apnesi, insomnia veya g\u00fcnd\u00fcz a\u015f\u0131r\u0131 uyku hali gibi sorunlar\u0131n tan\u0131s\u0131 i\u00e7in kullan\u0131l\u0131yor. Ancak bu \u00e7al\u0131\u015fma, uyku verilerinin \u00e7ok daha geni\u015f bir potansiyele sahip oldu\u011funu ortaya koyuyor:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bu bulgular g\u00fcnl\u00fck pratikte \u015fu anlama geliyor:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bir hastan\u0131n tek gecelik uyku kayd\u0131, gelecekteki kalp-damar hastal\u0131\u011f\u0131 riski hakk\u0131nda \u00f6ng\u00f6r\u00fc sa\u011flayabilir. Bu, \u00f6zellikle semptomu olmayan bireylerde erken \u00f6nlem al\u0131nmas\u0131n\u0131 m\u00fcmk\u00fcn k\u0131labilir.<\/li>\n\n\n\n<li>Y\u00fcksek riskli hastalar bireysel olarak daha yo\u011fun ya\u015fam tarz\u0131 m\u00fcdahaleleri, yak\u0131n takip veya ileri tetkiklere y\u00f6nlendirilebilir.<\/li>\n\n\n\n<li>Aile hekimli\u011fi ve kardiyoloji prati\u011finde, elektronik hasta kay\u0131tlar\u0131na entegre edilen yapay zek\u00e2 modelleri, hekimlere objektif risk skorlar\u0131 sunabilir.<\/li>\n\n\n\n<li>Kime ileri tetkik yap\u0131laca\u011f\u0131 veya hangi hastan\u0131n daha s\u0131k izlenmesi gerekti\u011fi daha rasyonel \u015fekilde belirlenebilir.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Di\u011fer taraftan bu ara\u015ft\u0131rmadaki \u00e7al\u0131\u015fma pop\u00fclasyonu a\u011f\u0131rl\u0131kl\u0131 olarak uyku klini\u011fine ba\u015fvuran hastalardan olu\u015fmaktad\u0131r; bu nedenle genel toplum i\u00e7in do\u011frudan genellenebilirlik s\u0131n\u0131rl\u0131 olabilir. Ayr\u0131ca yapay zek\u00e2 modelinin karar mekanizmas\u0131n\u0131n tam olarak a\u00e7\u0131klanabilir olmamas\u0131, klinik kabul a\u00e7\u0131s\u0131ndan halen bir tart\u0131\u015fma konusudur. Bu nedenle sonu\u00e7lar, hekim de\u011ferlendirmesinin yerine de\u011fil, onu destekleyici ara\u00e7lar olarak g\u00f6r\u00fclmelidir<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00c7al\u0131\u015fma, uyku laboratuvar\u0131 verilerinin elektronik hasta kay\u0131tlar\u0131yla geriye d\u00f6n\u00fck olarak e\u015fle\u015ftirilmesine dayanmaktad\u0131r. Ancak PSG yap\u0131lan t\u00fcm hastalar\u0131n uzun d\u00f6nem boyunca ayn\u0131 sa\u011fl\u0131k sisteminde izlenip izlenmedi\u011fi net de\u011fildir. ABD\u2019de merkezi bir ulusal EHR altyap\u0131s\u0131n\u0131n bulunmamas\u0131, baz\u0131 tan\u0131lar\u0131n farkl\u0131 kurumlarda kayda girmi\u015f ve veri setine yans\u0131mam\u0131\u015f olabilece\u011fi anlam\u0131na gelmektedir. Bu durum, \u00f6zellikle uzun d\u00f6nem hastal\u0131k tahminlerinde eksik olay kayd\u0131 (loss to follow-up) riskini do\u011furabilir ve model performans\u0131n\u0131 etkileyebilir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Sonu\u00e7ta bu ara\u015ft\u0131rman\u0131n k\u0131s\u0131tl\u0131l\u0131klar\u0131na ra\u011fmen ara\u015ft\u0131rmac\u0131lar, SleepFM gibi modellerin gelecekte giyilebilir cihazlardan gelen uyku verileriyle entegre edilebilece\u011fini vurguluyor. Ak\u0131ll\u0131 saatler ve ev tipi uyku sens\u00f6rleri yayg\u0131nla\u015ft\u0131k\u00e7a, yak\u0131n gelecekte invazif olmayan ve s\u00fcrekli sa\u011fl\u0131k izleme m\u00fcmk\u00fcn hale gelebilir.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Nature Medicine dergisinde Ocak 2026\u2019da yay\u0131mlanan bir ara\u015ft\u0131rma, uykunun yaln\u0131zca dinlenme an\u0131 olmad\u0131\u011f\u0131n\u0131 ortaya koydu. Uyku ayn\u0131 zamanda gelecekteki hastal\u0131k risklerini \u00f6ng\u00f6rebilen g\u00fc\u00e7l\u00fc bir biyolojik sinyal. Ara\u015ft\u0131rmac\u0131lar, SleepFM adl\u0131 \u00e7ok-modlu bir yapay zek\u00e2 modelini geli\u015ftirerek, tek bir geceye ait uyku kay\u0131tlar\u0131ndan 130\u2019dan fazla hastal\u0131\u011f\u0131n riskini y\u00fcksek do\u011frulukla tahmin edebildiklerini g\u00f6sterdi (https:\/\/www.nature.com\/articles\/s41591-025-04133-4). \u00c7al\u0131\u015fma, yakla\u015f\u0131k 65.000 ki\u015finin [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":4611,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[114],"tags":[],"class_list":["post-4613","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-guncel"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/academicsolidarity.com\/wp-content\/uploads\/2026\/01\/20260110YZ.png?fit=1536%2C1024&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/posts\/4613","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4613"}],"version-history":[{"count":1,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/posts\/4613\/revisions"}],"predecessor-version":[{"id":4614,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/posts\/4613\/revisions\/4614"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=\/wp\/v2\/media\/4611"}],"wp:attachment":[{"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4613"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4613"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/academicsolidarity.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4613"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}