Cerebral stroke recurrence risk perception and behavior decision model construction system and method

A risk perception and stroke technology, applied in the field of stroke diagnosis, can solve problems such as unseen technical solutions

Active Publication Date: 2021-08-17
ZHENGZHOU UNIV
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  • Application Information

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Problems solved by technology

[0005] However, for the perception and decision-making of the risk of...

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  • Cerebral stroke recurrence risk perception and behavior decision model construction system and method
  • Cerebral stroke recurrence risk perception and behavior decision model construction system and method
  • Cerebral stroke recurrence risk perception and behavior decision model construction system and method

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Embodiment Construction

[0053] In the following, the invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0054] refer to figure 1 , is a hierarchical structure diagram of a stroke recurrence risk perception system based on cloud computing according to an embodiment of the present invention.

[0055] exist figure 1 Among them, the risk perception system includes a data sensing layer, an edge analysis layer, a cloud early warning layer and a risk assessment layer;

[0056] More specifically, the data sensing layer includes a variety of smart sensors, which are used to perform multi-step sensing and detection on post-stroke patients to obtain at least one physiological measurement parameter.

[0057] The edge analysis layer includes at least one edge computing unit;

[0058] After the data sensing layer detects the physiological measurement parameters, it sends the physiological measurement parameters to the edge analysis layer;

[0059] the ...

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Abstract

The invention provides a cerebral apoplexy recurrence risk perception and behavior decision model construction system and method. The risk perception system comprises a data sensing layer, an edge analysis layer, a cloud early warning layer and a risk assessment layer; the data sensing layer sends the physiological measurement parameters to the edge analysis layer; the edge analysis layer executes edge calculation analysis; when the analysis result meets a first preset condition, a feedback signal is sent to the data sensing layer, and the edge calculation analysis results are stored in groups; the cloud early warning layer comprises a plurality of different types of cloud early warning databases; when the analysis result meets a second predetermined condition, the cloud early warning layer executes cloud query calculation in a cloud early warning database based on the edge calculation analysis result stored in groups; and the risk assessment layer receives a cloud query calculation result of the cloud early warning layer and gives a risk assessment value. The invention also discloses a recurrence risk decision system and method. According to the technical scheme, the recurrence risk of the cerebral apoplexy can be effectively perceived.

Description

technical field [0001] The invention belongs to the technical field of stroke diagnosis, and in particular relates to a stroke recurrence risk perception system based on cloud computing, a stroke recurrence risk decision-making system, a cloud computing-based stroke recurrence risk perception and decision-making method, and a computer program for realizing the method instruction. Background technique [0002] Stroke is a group of cerebrovascular diseases characterized by acute brain tissue damage caused by sudden rupture of cerebral blood vessels or blockage in blood vessels, including hemorrhagic stroke and ischemic stroke. Stroke often has the characteristics of high morbidity, high mortality and high disability rate. According to surveys, stroke in both urban and rural areas ranks first in the cause of death in my country and has become the leading cause of disability in adults in my country. [0003] After the onset of stroke, it is often accompanied by impairments i...

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Application Information

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IPC IPC(8): G16H50/30G16H50/20G16H50/70G06T7/00A61B3/12A61B3/14A61B5/00A61B5/02A61B5/22
CPCG16H50/30G16H50/20G16H50/70G06T7/0012A61B5/7282A61B5/02007A61B5/224A61B5/4803A61B3/12A61B3/14G06T2207/30041Y02A90/10
Inventor 张振香林蓓蕾刘雪婷郭娟娟禹瑞李冰华王玲玲郭二锋张娜
Owner ZHENGZHOU UNIV
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