Incremental neural network model-based depression prediction method and prediction system

Abstract

The invention discloses an incremental neural network model-based depression prediction method. The method comprises the following steps of establishing a depression daily data database; training a neural network model; acquiring daily life data, sending the daily life data to a server, and storing the daily life data in a user daily data record table; extracting day data in the user daily data record table to form an n-dimensional vector, performing normalization processing, and inputting the data to a depression pathologic neural network model to perform depression probability prediction; judging whether a depression probability value is greater than 0.5 or not by an intelligent household depression nursing device; if it is judged that a user suffers from depression, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household depression nursing device, and judging whether the examination result is correct or not by the server; and when the examination result is wrong, executing an incremental algorithm and performing dynamic correction on the neural network model. The 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 depression 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 depression, 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...

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 depression accurately

[0003] Previously, most of the depression 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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