Incremental neural network model-based allergic rhinitis prediction method and prediction system

A neural network model, a technology for allergic rhinitis, applied in the medical field, can solve the problems of inability to predict allergic rhinitis, large range deviation, and low computing efficiency.

Inactive Publication Date: 2017-02-08
湖南老码信息科技有限责任公司
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  • Claims
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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 allergic rhinitis accurately
[0003] Previously, most of the predictions for allergic rhinitis 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

Method used

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  • Incremental neural network model-based allergic rhinitis prediction method and prediction system
  • Incremental neural network model-based allergic rhinitis prediction method and prediction system
  • Incremental neural network model-based allergic rhinitis prediction method and prediction system

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Embodiment

[0055] Such as figure 1 As shown, a kind of allergic rhinitis prediction method based on incremental neural network model provided by the present invention comprises the following steps:

[0056] Step (1), obtain the source of etiology and pathology data of allergic rhinitis in the hospital and the daily monitoring data of patients, so as to establish the daily data database of allergic rhinitis;

[0057] Among them, the daily monitoring data is 12 items of data, and the 12 items of data are body temperature, heartbeat, heart rate, body fat, drinking water volume and frequency, sneezing times, nasal secretion color, nasal congestion, intranasal conditions, sleep time and quality. Daily walking distance etc., the present invention sets up 12 dimension vectors with 12 items of data;

[0058] Step (2), according to the allergic rhinitis daily data database established in step (1), the neural network model is trained in an offline manner to obtain the trained allergic rhinitis pa...

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Abstract

The invention discloses an incremental neural network model-based allergic rhinitis prediction method. The method comprises the following steps of establishing an allergic rhinitis 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 an allergic rhinitis pathologic neural network model to perform allergic rhinitis probability prediction; judging whether an allergic rhinitis probability value is greater than 0.5 or not by an intelligent household allergic rhinitis nursing device; if it is judged that a user suffers from allergic rhinitis, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household allergic rhinitis 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 an allergic rhinitis prediction method and prediction system based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up the prediction and evaluation of allergic rhinitis, 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 ...

Claims

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Application Information

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