208 citations
- Institut Polytechnique de ParisFR35 papers
- Centre National de la Recherche ScientifiqueFR21 papers
- Services répartis, Architectures, MOdélisation, Validation, Administration des RéseauxFR13 papers
- Institut Mines-TélécomFR7 papers
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11 papers · 1 filter
Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Standard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinic…
Interpreting Deep Glucose Predictive Models for Diabetic People Using RETAIN
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the…
Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the histo…
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researcher…
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particu…
GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes
Maxime De Bois, Mehdi Ammi, Mounîm A. El Yacoubi
Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address t…