Big data disease prediction system based on 5G and block chain

Abstract

The embodiment of the invention provides a big data disease prediction system and disease prediction method based on 5G and blockchain, and aims to automatically realize disease prediction. The methodis applied to a server, a plurality of disease prediction models used for predicting different skin diseases are stored in the server, the multiple models are divided into multiple groups, and the multiple skin diseases predicted by each group of models have the same symptom. The disease prediction method comprises the following steps: receiving a focus image and text description of a disease symptom; generating a word vector according to the text description; comparing the word vector with respective sample word vectors of each group of disease prediction models so as to determine a sample word vector closest to the word vector; calling a group of disease prediction models corresponding to the sample word vector, and inputting the focus image into each disease prediction model in the group of disease prediction models to obtain a plurality of prediction results so as to determine a skin disease type; and feeding back the skin disease type to the user terminal.

Description

technical field

[0001] The present invention relates to the technical field of artificial intelligence, in particular to a big data disease prediction system and disease prediction method based on 5G and blockchain. Background technique

[0002] With the application and popularization of the mobile Internet, more and more users start to conduct business online by means of mobile terminals. Taking the diagnosis of skin diseases through the telemedicine system as an example, the patient user usually needs to use a mobile terminal to take images of his own lesions and send them to the doctor's mobile terminal, so that the doctor can make a corresponding diagnosis based on the user's lesions.

[0003] However, since the size of the patient group is usually much larger than that of the doctor group, after the patient sends the lesion image to the doctor, he usually needs to wait in line for the doctor's diagnosis, resulting in a long time-consuming disease diagnosis and low user ...

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