Acquired immune deficiency syndrome personnel behavior analysis method based on deep learning

A technology of behavior analysis and deep learning, applied in informatics, medical informatics, epidemic warning system, etc., can solve problems such as less research

Inactive Publication Date: 2019-10-11
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] User behavior analysis under the current big data background often focuses on network social behaviors,

Method used

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  • Acquired immune deficiency syndrome personnel behavior analysis method based on deep learning
  • Acquired immune deficiency syndrome personnel behavior analysis method based on deep learning
  • Acquired immune deficiency syndrome personnel behavior analysis method based on deep learning

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Embodiment Construction

[0039] The implementation of the present invention will be described in detail below with examples, so as to fully understand and implement the implementation process of how the present invention uses technical means to solve technical problems and achieve technical effects.

[0040] The invention collects multi-dimensional spatiotemporal generalized data such as network behavior, geographic behavior, and social behavior of users based on mobile terminals of AIDS volunteers, builds an AIDS prevention and control database, and stores user behavior data. Based on deep learning and other algorithms to model and analyze the behavior of AIDS patients, including mining the characteristics of network behavior based on the content of users' web browsing, and discovering specific network content related to the spread of AIDS, to achieve precise publicity and education intervention. Record and analyze geographical location and behavior information such as location clustering centers and ...

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Abstract

The invention discloses an AIDS (acquired immune deficiency syndrome) personnel behavior analysis method based on deep learning. The method comprises the following steps: acquiring user behavior data;and based on the user behavior data, analyzing the multi-dimensional space-time information of the user, and constructing a user behavior portrait. Behavior portraying is performed on the AIDS patient based on network, geographic position and social communication behavior analysis, and descriptive label attributes for the user are constructed in multiple dimensions such as network, geographic position and social communication. The label attributes are utilized to describe and outline the real personal characteristics of the AIDS patient in multiple aspects, and are used for describing relatedcharacteristics, behaviors and preferences. Potential social communication rules of AIDS crowds are discovered, high-risk AIDS crowds, potential AIDS propagators and AIDS propagation paths are discovered, and the AIDS intervention link work is assisted to be intervened in advance.

Description

technical field [0001] The present invention relates to a method for analyzing the behavior of AIDS patients, in particular to a method for analyzing the behavior of AIDS patients based on deep learning. Background technique [0002] As an infectious disease, AIDS spreads among the population mainly through a series of autonomous behaviors such as risky sexual behavior and sharing unclean syringes and taking drugs. These behaviors generally occur in groups that are in close contact or have specific relationships. Structured patterns of network transmission rather than random distribution among independent individuals. Mining the behavior of AIDS users is of great significance for assisting the development of AIDS epidemic prevention and guiding and improving AIDS patients to form healthy and good behavior habits. [0003] User behavior analysis under the current big data background often focuses on network social behaviors, abnormal network attack behaviors, etc. There are...

Claims

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Application Information

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IPC IPC(8): G06F16/335G06F16/35G06F16/2458G06F16/28G06F16/29H04L29/08G16H50/80
CPCG06F16/335G06F16/353G06F16/29G06F16/285G06F16/2465G16H50/80G06F2216/03H04L67/535
Inventor 李巧勤王志华朱俊林陆佳鑫刘勇国杨尚明
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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