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

A technology of neural network model and prediction method, which is applied in the field of pneumonia prediction method and prediction system based on incremental neural network model, which can solve the problems of low computing efficiency, failure of servers to complete training tasks in time, inability to judge data and data logical association, and variables etc.

Inactive Publication Date: 2017-01-25
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, an

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

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Embodiment

[0054] Such as figure 1 As shown, a kind of pneumonia prediction method based on incremental neural network model provided by the present invention comprises the following steps:

[0055] Step (1), obtaining hospital pneumonia etiology and pathology data sources and patient daily monitoring data, thereby establishing a pneumonia daily data database;

[0056] Among them, the daily monitoring data is 21 items of data, and the 21 items of data are age, gender, heart rate, body temperature, food intake, cough frequency, body temperature change curve, water consumption, sore throat, complexion, throat secretion color, sleep time, Sleep quality, time to fall asleep, smoking amount (daily), drinking amount (daily), contact with high-risk personnel, 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 ;

[0057] Step (2), according to the pneumonia daily data database set up ...

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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 ...

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Application Information

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IPC IPC(8): G06F19/00G06N3/04G06N3/08
CPCG06N3/08G16H50/20G06N3/045
Inventor 杨滨
Owner 湖南老码信息科技有限责任公司
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