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

A technology of neural network model and prediction method, applied in the field of depression prediction method and prediction system based on incremental neural network model, can solve the problem of poor specificity, inability to judge the logical relationship between data and data, and inability of variables and servers to complete training in time tasks, etc.

Inactive Publication Date: 2017-02-08
湖南老码信息科技有限责任公司
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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 o...

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

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Embodiment

[0056] like figure 1 As shown, a kind of method for predicting depression based on incremental neural network model provided by the invention comprises the following steps:

[0057] Step (1), obtaining the hospital's depression etiology and pathology data source and patient daily monitoring data, thereby establishing a depression daily data database;

[0058] Among them, the daily monitoring data is 21 items of data, and the 21 items of data are age, sex, heart rate, mental condition, drinking water frequency, weight, diet, fatigue, time to fall asleep, sleep quality, smoking amount (daily), emotional condition, physical Response speed, occupation, temperature, humidity, air quality index and other 21 items of data, the present invention uses 21 items of data to establish a 21-dimensional vector;

[0059] Step (2), the daily data database of depression established according to step (1) trains the neural network model in an off-line mode, to obtain the trained depression patho...

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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...

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Application Information

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IPC IPC(8): G06F19/00
CPCG16H50/20G16H50/70
Inventor 杨滨
Owner 湖南老码信息科技有限责任公司
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