Tip 1 Diyabette Çok Katmanlı GRU Tabanlı Glikoz Tahmini

Abstract

Diyabet teknolojisindeki son gelişmeler, özellikle de sürekli glikoz izleme (CGM) sistemleri, güvenilir glikoz veri kaynakları sağlamaktadır. Bu veriler, yapay zeka ve veri odaklı tekniklerdeki teknolojik ilerlemelerle diyabet hastaları için gelişmiş glikoz tahmin modellerini hızlandırmıştır. Ancak, bu gelişmelere rağmen, modeller karmaşık sıralı verilerdeki bağlamsal örüntüleri öğrenmekte zorlandığından, glikoz seviyelerini doğru bir şekilde tahmin etmek hala bir zorluktur. Bu makalede, kodlayıcı-kod çözücü çerçevesi altında glikoz değerleri dizilerinden özellik çıkarımı için bir konvolüsyonel katman içeren yeni bir çok katmanlı GRU tabanlı model öneriyoruz. Önerilen çok katmanlı GRU tabanlı modeli eğitmek ve test etmek için açık erişimli D1NAMO veri seti kullanılmıştır. Önerilen model, 30 dakikalık glikoz tahmini için 9,88 mg/dL Ortalama Karekök Hatası, 6,46 mg/dL Ortalama Mutlak Hata, 0,92 Belirleme Katsayısı ve %4,83 Ortalama Mutlak Yüzde Hatası elde etmiştir. Ayrıca, tahmin modelinin sağlamlığını değerlendirmek için klinik bir ölçüt olarak Parkes Hata Izgarası kullanılmıştır. Önerilen model, son teknoloji glikoz tahmin modellerine kıyasla üstün performans göstermektedir.

Keywords

• Yapay Zeka
• Derin Öğrenme
• Kan Şekeri Tahmini
• Kapılı Tekrarlayan Hücre

Stacked GRU-Based Glucose Prediction in Type 1 Diabetes

Abstract

Recent advances in diabetes technology, especially continuous glucose monitoring (CGM) systems, provide reliable sources of glucose data. These data have accelerated advanced glucose prediction models for diabetics with technological advances in artificial intelligence and data-driven techniques. However, despite these advancements, accurately predicting glucose levels still is a challenge as the models struggle to learn contextual patterns in complex sequential data. In this paper, we propose a novel multilayer GRU-based model, including a convolutional layer for feature extraction from sequences of glucose values under the encoder-decoder framework. The open-access D1NAMO dataset was used to train and test the proposed multi-layer GRU-based model. The proposed model achieved a Root Mean Square Error of 9.88 mg/dL, Mean Absolute Error of 6.46 mg/dL, Coefficient of Determination of 0.92, and Mean Absolute Percentage Error of %4.83 for 30-min glucose prediction. Furthermore, the Parkes Error Grid was used as a clinical benchmark to assess the robustness of the prediction model. The proposed model demonstrates superior performance compared to state-of-the-art glucose prediction models.

Keywords

• Artificial Intelligence
• Deep Learning
• Glucose Prediction
• Gated Recurrent Unit

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