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Chronic non-infectious disease risk prediction system based on knowledge graph

A knowledge map and disease risk technology, applied in the field of chronic non-communicable disease risk prediction system, can solve the problems of wide range of diseases, complex causes of diseases, and high treatment costs, to eliminate ambiguity, avoid duplication, and reduce medical system. effect of stress

Inactive Publication Date: 2019-09-24
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Chronic diseases have the characteristics of long onset time, complex causes, high treatment costs, wide range of diseases, and high disability and mortality rates, and the disease management process of chronic diseases is relatively complicated
[0003] However, the traditional method of extracting content from literature can no longer meet the current people's requirements for the accuracy, speed and effectiveness of knowledge acquisition, and the knowledge map can make scientific knowledge intuitively expressed in a visualized form, helping people more conveniently and efficiently. Learn more about what you need

Method used

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  • Chronic non-infectious disease risk prediction system based on knowledge graph
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  • Chronic non-infectious disease risk prediction system based on knowledge graph

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Embodiment Construction

[0032] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] The present invention discloses a chronic non-communicable disease risk prediction system based on a knowledge map, which is connected with the established knowledge map of chronic non-communicable diseases and the hospital information system (HIS), and is used to predict according to the query information input by the user Disease risk rate and disease type, and can provide suggestions for improving the condition of confirmed patients.

[0034] Such as figure 1 As shown, the chronic non-communicable disease risk prediction system based on knowledge graph includes: user interface module 1, data update module 2, data collection module 3, data storage module 4, data analysis module 5, input module 6 and output Module 7. Wherein, user interf...

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Abstract

The invention provides a chronic non-infectious disease risk prediction system based on a knowledge graph. Based on the characteristics that chronic non-infectious diseases are not easy to find in the early stage and have long onset time, and patients are rejuvenated, the advantage that the knowledge graph can quickly and effectively obtain related knowledge and the logical relationship between the knowledge is utilized, the knowledge graph is constructed by utilizing the core technologies such as a decision tree algorithm and a BP neural network algorithm, and a disease risk prediction result is more conveniently and accurately provided for a user, so that the pressure of a medical system is reduced.

Description

technical field [0001] The invention relates to a chronic non-communicable disease risk prediction system based on a knowledge map, belonging to the field of knowledge maps. Background technique [0002] The incidence of chronic non-communicable diseases has not only continued to rise in recent years, but also shows a younger trend. Chronic diseases have the characteristics of long onset time, complex causes, high treatment costs, wide range of diseases, and high disability and mortality rates, and the disease management process of chronic diseases is relatively complicated. [0003] However, the traditional method of extracting content from literature can no longer meet the current people's requirements for the accuracy, speed and effectiveness of knowledge acquisition, and the knowledge map can make scientific knowledge intuitively expressed in a visualized form, helping people more conveniently and efficiently. Drill down to what you need. [0004] In view of this, it i...

Claims

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Application Information

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IPC IPC(8): G16H50/30G06F16/36
CPCG06F16/367G16H50/30
Inventor 王堃高子云朱娟杨璐孙雁飞亓晋岳东
Owner NANJING UNIV OF POSTS & TELECOMM
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