A continuous glucose monitoring measurements forecasting approach via sporadic blood glucose monitoring
2022 IEEE International Conference on Bioinformatics and …, 2022•ieeexplore.ieee.org
Continuous glucose monitoring prediction is a crucial yet challenging task in precision
medicine. This paper presents a novel neural ODE based approach for predicting
continuous glucose monitoring (CGM) levels purely based on sporadic self-monitoring
signals. We integrate the expert knowledge from physiological model into our model to
improve the accuracy. Experiments on the real-world data demonstrate that our method
outperforms other state-of-the-art methods on NRMSE metrics.
medicine. This paper presents a novel neural ODE based approach for predicting
continuous glucose monitoring (CGM) levels purely based on sporadic self-monitoring
signals. We integrate the expert knowledge from physiological model into our model to
improve the accuracy. Experiments on the real-world data demonstrate that our method
outperforms other state-of-the-art methods on NRMSE metrics.
Continuous glucose monitoring prediction is a crucial yet challenging task in precision medicine. This paper presents a novel neural ODE based approach for predicting continuous glucose monitoring (CGM) levels purely based on sporadic self-monitoring signals. We integrate the expert knowledge from physiological model into our model to improve the accuracy. Experiments on the real-world data demonstrate that our method outperforms other state-of-the-art methods on NRMSE metrics.
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