A convolutional neural network-based method for automatically grouping DRGs, comprising: collecting case data and grouping same according to main diagnosis categories and groups related to core disease diagnosis; performing digitalized coding on the data; constructing a shallow convolutional neural network model, using a k-means clustering method to perform clustering on feature vectors extracted by a convolutional network, so as to obtain k category labels, and supervising iterative training of the network by combining the category labels and a classifier; and after the training of the model is completed, performing data grouping application. By means of the method, the shortcomings of manual feature selection and additional data annotation for newly-added grouping categories are avoided, and automatic learning grouping can be performed on data which is fuzzy and difficult in grouping.
CONVOLUTIONAL NEURAL NETWORK-BASED METHOD FOR AUTOMATICALLY GROUPING DRGS