Arşiv logosu
  • Türkçe
  • English
  • Giriş
    Yeni kullanıcı mısınız? Kayıt için tıklayın. Şifrenizi mi unuttunuz?
Arşiv logosu
  • Koleksiyonlar
  • Sistem İçeriği
  • Analiz
  • Talep/Soru
  • Türkçe
  • English
  • Giriş
    Yeni kullanıcı mısınız? Kayıt için tıklayın. Şifrenizi mi unuttunuz?
  1. Ana Sayfa
  2. Yazara Göre Listele

Yazar "Codal, Keziban Seckin" seçeneğine göre listele

Listeleniyor 1 - 1 / 1
Sayfa Başına Sonuç
Sıralama seçenekleri
  • [ X ]
    Öğe
    Health Big Data Modelling and Analytics
    (CRC Press, 2024) Hiziroglu, Abdulkadir; Pisirgen, Ali; Codal, Keziban Seckin
    This chapter delves into the multifaceted applications of health big data modelling and analytics across distinct categories, offering valuable insights into the transformative landscape of healthcare systems. The discussion encompasses health big data, health decision-making development, business process management for more efficient hospital operations, and analytics focused on medical diagnostics and disease/treatment monitoring. Health data modelling involves creating structured frameworks for organizing and managing healthcare data. These frameworks are crucial for efficient data storage, retrieval, and analysis within healthcare systems. Moreover, the concept of health big data extends beyond personal health records and includes vast amounts of information produced by the health sector. It explores opportunities and challenges associated with health big data analytics, highlighting its potential benefits in improving health services, early disease detection, and personalized medicine. Further exploration into artificial intelligence for health data modelling and analytics emphasizes the transformative impact of machine learning in healthcare, specifically in disease diagnosis, outcome prediction, and personalized treatment strategies. The selection of an appropriate AI model is referred crucial, considering factors such as accuracy, interpretability, scalability, and ethical considerations. Transparent and interpretable models, exemplified by decision trees, are recommended to foster trust among healthcare professionals. The chapter concludes by underscoring the paramount importance of addressing ethical and legal considerations, along with domain-specific requirements, to ensure the responsible and effective application of machine learning in healthcare, ultimately contributing to optimal patient care and the evolution of healthcare systems. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.

| İzmir Bakırçay Üniversitesi | Kütüphane | Açık Bilim Politikası | Rehber | OAI-PMH |

Bu site Creative Commons Alıntı-Gayri Ticari-Türetilemez 4.0 Uluslararası Lisansı ile korunmaktadır.


Gazi Mustafa Kemal Mahallesi, Kaynaklar Caddesi Seyrek,Menemen, İzmir, TÜRKİYE
İçerikte herhangi bir hata görürseniz lütfen bize bildirin

DSpace 7.6.1, Powered by İdeal DSpace

DSpace yazılımı telif hakkı © 2002-2025 LYRASIS

  • Çerez Ayarları
  • Gizlilik Politikası
  • Son Kullanıcı Sözleşmesi
  • Geri Bildirim