An artificial intelligence-based patient classification method used for enhancing the accuracy of patient classification. Said method comprises: reading the medical history data of multiple patients, and extracting from the medical history data the sequential measurement data of multiple pre-set physiological indicators of each patient and the treatment plan data for each patient; according to the sequential measurement data, correspondingly generating time-change relation images of the multiple pre-set physiological indicators for each patient; converting the treatment plan data to the corresponding treatment plan codes; inputting the change relation images into a convolutional neural network, and training a target convolutional neural network using the treatment plan codes as the target output; obtaining the image codes outputted by the last layer of the fully connected layer of the target convolutional neural network, and performing clustering on the multiple patients according to the image codes so as to divide the multiple patients into multiple patient clusters. The present method also relates to the blockchain technology, as said change relation images are stored in a blockchain.
ARTIFICIAL INTELLIGENCE-BASED PATIENT CLASSIFICATION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUM