In Computers in biology and medicine
As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of medical concepts and time interval characteristics of patients' multiple visit sequences, which reflect the frequency of patient visits and the severity of the disease. Therefore, this paper proposes a new end-to-end deep neural network structure, MST-CNN, for medical cost prediction. The MST-CNN model improves the representation quality of medical concepts by constructing a multi-granularity embedding model of medical concepts and incorporates a time interval vector to accurately measure the frequency of patient visits and form an accurate representation of medical events. Moreover, the MST-CNN model integrates a channel attention mechanism to adaptively adjust the channel weights to focus on significant medical features. The MST-CNN model systematically addresses the problem of deep learning models for temporal data representation. A case study and three comparative experiments are conducted using data collected from Pingjiang County. Through experiments, the methods used in the proposed model are analyzed, and the super contribution of the model performance is demonstrated.
Luo Min, Wang Yi-Ting, Wang Xiao-Kang, Hou Wen-Hui, Huang Rui-Lu, Liu Ye, Wang Jian-Qiang
2022-Oct-30
Attention mechanism, Convolutional neural network, Deep Learning, Multi-granularity embedding, t-SNE
