Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction - Jian Xue; Huaimin Cao; Lu Yang; Jiajia Liu; Ying Zhang; Chunlei Zhang; Ke Bai; Peng Jiang; Fanchang Hao; Lina Xu (PLoS ONE, 2025)
The management of blood glucose in hospitalized patients is confined to retrospective interventions, preventing healthcare professionals from predicting patients’ blood glucose levels and potential adverse events in advance. This study employs a deep learning model, specifically a Stacked Attention-Gated Recurrent Unit (SA-GRU) network, to forecast short-term blood glucose (BG) levels and predict adverse events in hospitalized patients, assisting clinicians in making clinical decisions. We collect continuous glucose monitoring(CGM) data from 196 hospitalized patients with type 2 diabetes, and by constructing and training this deep learning model, we predict blood glucose levels and adverse events.The model’s predictions are then compared with the actual CGM data, and different evaluation metrics are used to assess the predictions of blood glucose levels and adverse events. Additionally, experiments were conducted on another publicly available type 2 diabetes dataset. On our collected data, for the 30-minute prediction, the root mean square error (RMSE) and mean absolute relative difference (MARD) of blood glucose are 4.27 ± 0.31 mg/dL and 1.77% ± 0.08%, respectively, with an adverse event classification accuracy of 98.57% ± 0.11%. For the 60-minute prediction, the RMSE and MARD of blood glucose are 10.46 ± 0.55 mg/dL and 4.59% ± 0.22%, respectively, with an adverse event classification accuracy of 95.74% ± 0.33%. Similar positive results were obtained on another publicly available dataset. The proposed model demonstrates accurate predictions for blood glucose values and adverse events in the next 30 and 60 minutes.