Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information

With the continuous improvement of economic level and the continuous development of science and technology in China, information technology has begun to integrate into all walks of life. Medical units have begun to change from the traditional medical system to the intelligent system, and the processing of online case information has become an important component of medical informationization. To improve the efficiency of dealing with online case information, this study proposes to establish a fully connected neural network model to deal with online cases. Using jieba word segmentation tool and data preprocessing technology, the data of electronic medical records are sorted out, and the data are quantified using Word2Vec and other tools, and the data on electronic medical records are converted into one-hot binary variables. The quantified data are trained into a fully connected neural model, and the accuracy rate is about 88%. It is compared with naive Bayes and decision tree classification methods, and then a comparative experiment is carried out by solving e-health services in different ways. The results show that the fully connected neural network model has the best classification effect: the highest accuracy rate is about 93.7%, the highest precision rate is about 94.0%, the highest recall rate is about 95.3%, and the highest F1 score is about 94.6%. However, using artificial intelligence technology to solve electronic health services has great advantages, among which efficiency, assistance, and service satisfaction are all higher than 90%, which provides favorable technical support for electronic health services.