Glucose Forecasting and Hypoglycemia Forewarning in Type 1 and Type 2 Diabetes using Deep Learning

Introduction This is the official implementation of: Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning Source code: DL_BGPred-main.zip Publicly Available Diabetes Dataset This repository also archives publicly available datasets for type 1 and type 2 diabetes for convenience. Please cite the original sources when using these data. Details of these datasets are shown in Table 1. Table 1. Dataset Information REAGENT OR RESOURCE SOURCE IDENTIFIER FILE NAME OhioT1DM Marling and Bunescu [1] https://pubmed.ncbi.nlm.nih.gov/33584164/ N/A Loop Study Lum et al. [2] https://doi.org/10.1089/dia.2020.0535 Loop study public dataset 2023-01-31.zip RT CGM JDRF CGM Study Group [3] https://doi.org/10.1089/dia.2007.0302 RT_CGM_Randomized_Clinical_Trial.zip Replace BG Aleppo et al. [4] https://pubmed.ncbi.nlm.nih.gov/28209654/ REPLACE-BG Dataset-79f6bdc8-3c51-4736-a39f-c4c0f71d45e5.zip ShanghaiT1DM Zhao et al. [5] https://doi.org/10.6084/m9.figshare.20444397 diabetes datasets.zip ShanghaiT2DM Zhao et al. [5] https://doi.org/10.6084/m9.figshare.20444397 diabetes datasets.zip Pone Colas et al. [6] https://doi.org/10.1371/journal.pone.0225817.s001 pone.0225817.s001.zip References [1] Marling, C., and Bunescu, R. (2020). The OhioT1DM dataset for blood glucose level prediction: Update 2020. CEUR Workshop Proc. 2675, 71–74. [2] Lum, J.W., Bailey, R.J., Barnes-Lomen, V., Naranjo, D., Hood, K.K., Lal, R.A., Arbiter, B., Brown, A.S., DeSalvo, D.J., Pettus, J., et al. (2021). A real-world prospective study of the safety and effectiveness of the loop open source automated insulin delivery system. Diabetes Technol. Ther. 23, 367–375. [3] JDRF CGM Study Group (2008). JDRF randomized clinical trial to assess the efficacy of real-time continuous glucose monitoring in the management of type 1 diabetes: Research design and methods. Diabetes Technol. Ther. 10, 310–321. [4] Aleppo, G., Ruedy, K.J., Riddlesworth, T.D., Kruger, D.F., Peters, A.L., Hirsch, I., Bergenstal, R.M., Toschi, E., Ahmann, A.J., Shah, V.N., et al. (2017). REPLACE-BG: A randomized trial comparing continuous glucose monitoring with and without routine blood glucose monitoring in adults with well-controlled type 1 diabetes. Diabetes Care 40, 538–545. [5] Zhao, Q., Zhu, J., Shen, X., Lin, C., Zhang, Y., Liang, Y., Cao, B., Li, J., Liu, X., Rao, W., et al. (2023). Chinese diabetes datasets for data-driven machine learning. Sci. Data 10, 35. [6] Colas, A., Vigil, L., Vargas, B., Cuesta-Frau, D., and Varela, M. (2019). Detrended fluctuation analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics. PLOS One 14, e0225817.