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

A neural network model and atopic dermatitis technology, applied in the medical field, can solve problems such as the inability to judge the logical relationship between data and data, the large deviation of variables and value ranges, and the inability of the server to complete training tasks in time

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 obtained val

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

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Embodiment

[0056] Such as figure 1 As shown, a kind of atopic dermatitis prediction method based on incremental neural network model provided by the present invention comprises the following steps:

[0057] Step (1), obtaining the hospital atopic dermatitis etiology and pathology data source and patient daily monitoring data, thereby establishing the daily data database of atopic dermatitis;

[0058] Among them, the daily monitoring data is 21 items of data, and the 21 items of data include age, gender, heart rate, eating frequency, trunk skin condition, allergens around the environment, weight, rash severity, pain and itching, user allergen detection report, Smoking amount (daily), skin condition of extremities, user's condition detection in different environments, 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), according to the atopic dermatitis daily d...

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Abstract

The invention discloses an incremental neural network model-based allergic dermatitis prediction method. The method comprises the following steps of establishing an allergic dermatitis 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 dermatitis pathologic neural network model to perform allergic dermatitis probability prediction; judging whether an allergic dermatitis probability value is greater than 0.5 or not by an intelligent household allergic dermatitis nursing device; if it is judged that a user suffers from allergic dermatitis, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household allergic dermatitis 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 atopic dermatitis 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 dermatitis, 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 dat...

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Application Information

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IPC IPC(8): G06F19/00G06N3/08
CPCG16H50/20G06N3/08G16H50/30G16H50/70
Inventor 杨滨
Owner 湖南老码信息科技有限责任公司
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