Butt Hatim, Khosa Ikramullah, Iftikhar Muhammad Aksam
Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Islamabad 54000, Pakistan.
Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Islamabad 54000, Pakistan.
Diagnostics (Basel). 2023 Jan 17;13(3):340. doi: 10.3390/diagnostics13030340.
Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation. In this research work, a transformation of event-based data into discriminative continuous features is proposed. Moreover, a multi-layered long short-term memory (LSTM)-based recurrent neural network is developed for the prediction of blood glucose levels in patients with type 1 diabetes. The proposed method is used to forecast the blood glucose level on a prediction horizon of 30 and 60 min. The results are evaluated for three patients using the Ohio T1DM dataset. The proposed scheme achieves the lowest RMSE score of 14.76 mg/dL and 25.48 mg/dL for prediction horizons of 30 min and 60 min, respectively. The suggested methodology can be utilized in closed-loop systems for precise insulin delivery to type 1 patients for better glycemic control.
糖尿病是一种代谢性疾病，会导致人体失去对血糖调节的控制。随着自我监测系统的最新进展，患者可以获取自己的个性化血糖状况，并可利用它来有效预测未来的血糖水平。一个有效的糖尿病管理系统需要准确估计血糖水平，这除了使用适当的预测算法外，还取决于有区分性的数据表示。在这项研究工作中，提出了将基于事件的数据转换为有区分性的连续特征的方法。此外，还开发了一种基于多层长短期记忆（LSTM）的递归神经网络，用于预测1型糖尿病患者的血糖水平。所提出的方法用于在30分钟和60分钟的预测范围内预测血糖水平。使用俄亥俄州1型糖尿病数据集对三名患者的结果进行了评估。所提出的方案在30分钟和60分钟的预测范围内分别实现了最低均方根误差分数，分别为14.76毫克/分升和25.48毫克/分升。所建议的方法可用于闭环系统，为1型患者精确输送胰岛素，以实现更好的血糖控制。
