Sağlık Yönetimi Araştırmalarında Makine Öğrenimi Uygulaması: Tahmine Dayalı Analitikten Öğrenen Sağlık Sistemlerine
Özet
Bu bölüm, sağlık yönetimi araştırmalarında geleneksel istatistiksel yöntemlerden makine öğrenimi destekli analitik süreçlere geçişin teorik ve pratik yönlerini kapsamlı şekilde incelemektedir. Dijitalleşmenin sağlık hizmetlerinde artan veri hacmi ve çeşitliliği, klasik hipotez testi ve doğrusal regresyon modellerinin tahmin gücünü sınırlandırmaktadır. Burada, makine öğreniminin sağlık yönetimindeki rolü, denetimli, denetimsiz ve pekiştirmeli öğrenme yaklaşımlarıyla ele alınmaktadır. Özellikle Random Forest, Gradient Boosting ve Doğal Dil İşleme tekniklerinin acil servis yoğunluk tahmini, yeniden yatış riski analizi ve yapılandırılmamış klinik verilerin anlamlandırılması gibi uygulamalardaki üstünlükleri vurgulanmaktadır. Ayrıca, model başarısının ölçümünde geleneksel p-değeri yöntemiyle modern performans metrikleri arasındaki epistemolojik farklar tartışılmaktadır. Analizler, makine öğreniminin sadece tahmin yapmakla kalmayıp, verinin sürekli geri bildirim döngüsüyle klinik uygulamaları geliştiren öğrenen sağlık sistemlerinin temelini attığını ortaya koymaktadır. Bölüm, algoritmik hakkaniyet, açıklanabilirlik ve veri gizliliği gibi etik konulara dikkat çekmekte ve sağlık yönetimi araştırmalarında nedensel makine öğrenimine geçişin önemini savunmaktadır.
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Referanslar
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