Application of Deep Learning Methods in the Diagnosis of Coronary Heart Disease Based on Electronic Health Record

With the development of Internet technology, the number of electronic health record data has surged. Also, artificial intelligence simulates the ability of human beings to solve problems and make decisions by conducting complex and fast calculation on a large amount of data. Based on the electronic health records of hypertension patients in Peking University Shenzhen Hospital, we proposed the use of deep learning methods to achieve intelligent coronary heart disease diagnosis for hypertensive patients. We firstly conducted statistical data analysis and effective feature selection experiments. Then, we established an intelligent diagnosis model for coronary heart disease based on the Transformer and contrastive learning. The model integrates multiple types of health record data such as patient's personal information, symptoms, concurrent diseases and test data, and the results proved that our model achieved the best classification performance and accuracy compared with CNN, RNN and LSTM, with AUC value reached 0.9349. In the future, this model can be extended to the diagnosis of general chronic diseases.