Individualized disease risk level analyzing method based on conventional factor

A disease risk and analysis method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to real-time adjustment, poor personalization and real-time performance, inaccurate disease risk assessment, etc. high degree of personalization, accurate assessment of disease risk

Inactive Publication Date: 2017-10-27
北斗云谷(北京)科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] For disease prevention, disease risk assessment is an important means of disease prevention. However, the existing disease risk assessment methods usually give some specific groups of people a higher risk of certain diseases than other gr

Method used

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  • Individualized disease risk level analyzing method based on conventional factor
  • Individualized disease risk level analyzing method based on conventional factor
  • Individualized disease risk level analyzing method based on conventional factor

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0043] This embodiment provides a method for analyzing individualized disease risk levels based on conventional factors, such as figure 1 As shown, the method includes:

[0044] Step 101: Create a regular factor logic table

[0045] According to medical information and big data information, establish a routine factor logic table, the routine factor logic table, including: disease name, routine factor and the threshold value of the described routine factor, a described disease name and at least one routine factor association, the direct association is divided into direct first-level association, direct second-level association and direct third-level association, the indirect association is divided into indirect first-level association, indirect second-level association and indirect third-level association, and the first-level association An association level higher than that of the second-level association is higher than that of the third-level association; wherein, the regula...

Embodiment 2

[0078] This embodiment provides a method for analyzing individualized disease risk levels based on conventional factors, such as image 3 As shown, the method includes:

[0079] Step 201: Create a regular factor logic table

[0080] According to medical information and big data information, establish a routine factor logic table, the routine factor logic table, including: disease name, routine factor and the threshold value of the described routine factor, a described disease name and at least one routine factor Correlation, the direct correlation is divided into direct level 1 correlation, direct level 2 correlation and direct level 3 correlation, and the indirect correlation is divided into indirect level 1 correlation, indirect level 2 correlation and indirect level 3 correlation; wherein, the conventional factor The logic table is adjusted in real time according to the update of the medical information and / or the big data information.

[0081] Step 202: Obtain personal i...

Embodiment 3

[0124] This embodiment provides a method for analyzing individualized disease risk levels based on conventional factors, such as Figure 5 As shown, the method includes:

[0125] Step 301: Create a regular factor logic table

[0126] According to medical information and big data information, establish a routine factor logic table, the routine factor logic table, including: disease name, routine factor and the threshold value of the described routine factor, a described disease name and at least one routine factor Correlation, the direct correlation is divided into direct level 1 correlation, direct level 2 correlation and direct level 3 correlation, and the indirect correlation is divided into indirect level 1 correlation, indirect level 2 correlation and indirect level 3 correlation; wherein, the conventional factor The logic table is adjusted in real time according to the update of the medical information and / or the big data information.

[0127] Step 302: Obtain personal ...

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Abstract

The invention discloses an individualized disease risk level analyzing method based on a conventional factor. The method comprises the following steps: establishing a conventional factor logic table according to medical information and big data information and adjusting in real time according to the update of the medical information and/or the big data information; obtaining personal information; screening out individualization factors and setting weights thereof; sorting the individualization factors according to the weights to obtain a personal label, wherein the personal label comprises an initial disease group, and the initial disease group comprises at least one disease name and a risk level corresponding to the disease name; obtaining personal updated information and obtaining a personal updated label according to the personal updated information, wherein the personal updated label comprises an updated disease group, and the updated disease group comprises at least one disease name and a risk level corresponding to the disease name; when the updated disease group and the initial disease group comprise the same disease name, the updated disease group comprises the change trend of the same disease name. The method disclosed by the invention has relatively high individuality and real-time property and higher accuracy.

Description

technical field [0001] The present invention relates to the technical field of disease risk analysis, and more particularly, relates to a method for analyzing individualized disease risk levels based on conventional factors. Background technique [0002] With the development of social economy, people's dietary structure and living habits have undergone tremendous changes, and people are paying more and more attention to diseases and health problems. [0003] According to the research report of the World Health Organization, 1 / 3 of human diseases can be avoided through preventive health care, 1 / 3 of diseases can be effectively controlled by early detection, and 1 / 3 of diseases can be improved through effective communication. For diseases, treatment is not the only way. Effective prevention and control of diseases and improvement of the efficiency of disease treatment through health management are the foundation of human health. [0004] For disease prevention, disease risk a...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 姜涵予
Owner 北斗云谷(北京)科技有限公司
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