Kidney stone prediction method and prediction system based on incremental neural network model

Abstract

The invention discloses a method for predicting kidney stones based on an incremental neural network model, comprising the following steps: establishing a daily data database for kidney stones; training the neural network model; collecting daily life data and sending it to a server, and saving it to the user's daily data Record table; the data of the day is extracted from the user's daily data record table to form an n-dimensional vector. Greater than 0.5; the user judges that he has kidney stones, and the user goes to the hospital for examination by himself, and sends the examination results back to the server through the smart home kidney stone care equipment, and the server judges whether the examination results are correct; The network model is dynamically corrected. The prediction of the present invention is accurate, and the neural network model is tailored for each user.

Description

technical field [0001] The invention belongs to the field of medical technology, in particular to a method and system for predicting kidney stones based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up prediction and evaluation of kidney stones, and the prediction method used is data matching. The principle is to input personal life data into the system, and the system matches the fixed data to obtain the probability of disease. However, due to the complexity and unpredictability of the human body and diseases, the detection and signal expression of biological signals and information in the form of expression and change rules (self-change and changes after medical intervention), the obtained data and information Analysis, decision-making and many other aspects have very complex nonlinear connections. Therefore, the use of traditional data matching can only be blind data screening, unable to...

AI Technical Summary

Problems solved by technology

However, due to the complexity and unpredictability of the human body and diseases, the detection and signal expression of biological signals and information in the form of expression and change law (self-change and change after medical intervention), the acquired data and information There are very complex nonlinear relationships in analysis, decision-making and many other aspects

Therefore, the use of traditional data matching can only be blind data screening, unable to judge the logical relationship between data and variables, and the obtained value range deviation is large, resulting in very poor specificity of system prediction, so the current domestic health management The system cannot effectively predict an individual's kidney stones accurately

[0003] Previously, most of the predictions for kidney stones used the BP neural network model, but when new detection data is generated, the neural network model must be trained again, and the calculation efficiency is extremely low

And when the scale of system users increases, the server will not be able to complete the training tasks in time

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