Electronic Medical Data Analysis Based on Word Vector and Deep Learning Model

With the continuous promotion of medical informatization, electronic medical data is rapidly increasing. And in the field of natural language processing, it has been increasingly used in conjunction with text processing technology to assist doctors in diagnosis. To solve the problem of large amount of redundancy, missing semantics, and ambiguity in the text part of the electronic medical data, this paper combines the word vector method and deep learning theory. The vectorization of medical text data words is expressed in a fixed-length matrix, Then, it combines LSTM model with Yoon model, Finally, the integrative learning method is used to obtain a comprehensive learning model, and this has achieved good results in the text classification of electronic medical data.