Pneumonia prediction method and prediction system based on incremental neural network model

Abstract

The invention discloses a pneumonia prediction method based on an incremental neural network model. The pneumonia prediction method comprises the following steps that a pneumonia daily data database is established; the neural network model is trained; daily life data is acquired and sent to a server and is saved in a daily data record sheet of a user; current-day data is extracted from the daily data record sheet of the user to form n-dimensional vectors, normalization processing is performed, and then the vectors are input into the neural network model of pneumonia pathology to perform pneumonia possibility prediction; an intelligent household pneumonia nursing device judges whether a pneumonia possibility value is greater than 0.5 or not; when the user determines pneumonia, the user goes to a hospital by himself / herself for examination and transmits an examination result to the server through the intelligent household pneumonia nursing device, and the server judges whether the examination result is correct or not; when the examination result is wrong, an incremental algorithm is executed, and dynamic correction is conducted on the neural network model. The pneumonia prediction method is accurate in prediction, and the neural network model is customized 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 pneumonia based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up pneumonia prediction and evaluation, 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 judge the ...

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 pneumonia

[0003] Previously, most pneumonia predictions 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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