4.5 Article

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying

期刊

MATHEMATICAL BIOSCIENCES AND ENGINEERING
卷 19, 期 10, 页码 10096-10107

出版社

AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2022472

关键词

diabetes; deep learning; home blood glucose monitoring; intelligence-aided health management

资金

  1. China Postdoctoral International Exchange Program Academic Exchange Project, Science and Technology Program of Tianjin [18ZXZNSY00280]
  2. Tianjin Medical University college student Innovation training program

向作者/读者索取更多资源

An intelligent prediction system for glucose trajectory based on deep learning was constructed and applied to a participant with type 2 diabetes. The results showed good predictive accuracy and improved glucose control by reducing glucose variability. However, the application of the system also brought about increased stress.
Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently and economically, an intelligent prediction of glucose is demanding. A glucose trajectory prediction system based on subcutaneous interstitial continuous glucose monitoring data and deep learning models for ensuing glucose trajectory was constructed, followed by the application of personalised prediction models on one participant with type 2 diabetes in a community. The predictive accuracy was then assessed by RMSE (root mean square error) using blood glucose data. Changes in glycaemic parameters of the participant before and after model intervention were also compared to examine the efficacy of this intelligence-aided health care. Individual Recurrent Neural Network model was developed on glucose data, with an average daily RMSE of 1.59 mmol/L in the application segment. In terms of the glucose variation, the mean glucose decreased by 0.66 mmol/L, and HBGI dropped from 12.99 x 10(2) to 9.17 x 10(2). However, the participant also had increased stress, especially in eating and social support. Our research presented a personalised care system for people with diabetes based on deep learning. The intelligence-aided health management system is promising to enhance the outcome of diabetic patients, but further research is also necessary to decrease stress in the intelligence-aided health management and investigate the stress impacts on diabetic patients.

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