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Found 8 papers, 1 papers with code
Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People
In order to use the loss function in practice, we propose an algorithm that progressively improves the clinical acceptability of the model, so that we can achieve the best tradeoff possible between accuracy and given clinical criteria.
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People
Then, we show the importance of making stable predictions by smoothing the predictions made by the models, resulting in an overall improvement of the clinical acceptability of the models at the cost in a slight loss in prediction accuracy.
Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People
We show the usefulness of its interpretable nature by analyzing the contribution of each variable to the final prediction.
Interpreting Deep Glucose Predictive Models for Diabetic People Using RETAIN
As informative for the patients as for the practitioners, it can enhance the understanding of the predictions made by the model and improve the design of future glucose predictive models.
Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions
First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted.
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
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.
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People
To improve the quality of the transfer between multiple sources of data, we propose a multi-source adversarial transfer learning framework that enables the learning of a feature representation that is similar across the sources, and thus more general and more easily transferable.
GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes
In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.
