Combining knowledge extension with convolution neural network for diabetes prediction

Prediction and diagnosis of diabetes are critical issues in the field of smart healthcare. However, the dependence of large-scale annotated diabetes data and the lack of diabetes knowledge represent significant challenges for diabetes prediction. To address these challenges, we propose a new diabetes prediction model named KE-CNN, which combines knowledge extension and convolution neural network. The KE-CNN model first extracts abnormal indicator features from physical examination index data of diabetic patients and uses Word2vec to embed the feature words. We then employ entity recognition technique named BERT-BiLSTM-CRF to identify medical entities in the condition description text and utilize a knowledge graph to extend the knowledge of each medical entity, followed by using pre-trained Chinese word vectors to embed the extended description text. Finally, we construct a semantic enhanced convolutional neural network model with word embedding vectors and text embedding vectors as dual-channel input, aiming to enhance the feature expression of the KE-CNN model. Our model not only learns and captures more fine-grained features of diabetes information, but also significantly reduces the amount of data required for model training and improves the prediction performance of convolutional neural network models. Our experiments show that the KE-CNN model effectively improves the accuracy of diabetes prediction compared with the benchmark model.