Type 2 diabetes is the most common chronic disease for the elderly people. This disease is difficult to be cured and causes continued medical expenses. The early and personalized risk assessment of type 2 diabetes is necessary. So far, various type 2 diabetes risk prediction methods have been proposed. However, these methods have three major issues: 1) not fully considering the importance of personal information and rating information of healthcare system, 2) not adopting the long-term temporal information, and 3) not comprehensively capturing the correlation between the diabetes risk factor categories. To address these issues, the personalized risk assessment framework for elderly people with type 2 diabetes is needed. However, it is very challenging due to two reasons, namely imbalanced label distribution and high-dimensional features. In this paper, we propose diabetes mellitus network framework (DMNet) for type 2 diabetes risk assessment of elderly people. Specifically, we propose tandem long short-term memory to extract the long-term temporal information of different diabetes risk categories. In addition, the tandem mechanism is used to capture the correlation between the diabetes risk factor categories. To balance the label distribution, we adopt the method of synthetic minority over-sampling technique with Tomek links. To form the better feature representations, we utilize entity embedding to solve the problem of high-dimensional features. To evaluate the performance of our proposed method, we conduct the experiments on a real-world dataset called Research on Early Life and Aging Trends and Effects. The experiment results show that DMNet outperforms the baseline methods in terms of six evaluation metrics (i.e., accuracy of 0.94, balanced accuracy of 0.94, precision of 0.95, F1-score of 0.95, recall of 0.95 and AUC of 0.94).

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the multi-perspective patient representation Extractor (MPRE) for disease prediction. Specifically, we propose frequency transformation module (FTM) to extract the trend and variation information of dynamic features in the time–frequency domain, which can enhance the feature representation. In the 2D multi-extraction network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the first-order difference attention mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.