Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism

Abstract

Type 2 diabetes mellitus (T2DM) has been identified as one of the most challenging chronic diseases to manage. In recent years, the incidence of T2DM has increased, which has seriously endangered people’s health and life quality. Glycosylated hemoglobin (HbA1c) is the gold standard clinical indicator of the progression of T2DM. An accurate prediction of HbA1c levels not only helps medical workers improve the accuracy of clinical decision-making but also helps patients to better understand the clinical progression of T2DM and conduct self-management to achieve the goal of controlling the progression of T2DM. Therefore, we introduced the long short-term memory (LSTM) neural network to predict patients’ HbA1c levels using time sequential data from electronic medical records (EMRs). We added the self-attention mechanism based on the traditional LSTM to capture the long-term interdependence of feature elements and which ensure that the memory was more profound and effective, and used the gradient search technology to minimize the mean square error of the predicted value of the network and the real value. LSTM with the self-attention mechanism performed better than the traditional deep learning sequence prediction method. Our research provides a good reference for the application of deep learning in the field of medical health management.

Keywords

• Network
• Machine learning
• T2DM
• Glycosylated hemoglobin
• Self-attention mechanism