Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model

Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model

Abstract Personalized blood glucose (BG) prediction in Type 1 Diabetes (T1D) is challenged by significant inter-patient heterogeneity. To address this, we propose BiT-MAML, a hybrid model combining a Bidirectional LSTM-Transformer with Model-Agnostic Meta-Learning. We evaluated our model using a rig...

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Bibliographic Details
Main Authors: Kihoon Moon, Jaehong Kim, Seohyun Yoo, Jaehyuk Cho
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Type 1 diabetes
Blood glucose prediction
Deep learning
Bidirectional long short term memory
Transformer
Model-agnostic meta-learning
Online Access:https://doi.org/10.1038/s41598-025-13491-5
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https://doi.org/10.1038/s41598-025-13491-5

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