Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for predicting occurrence risk of acute cerebrovascular disease

A technology for acute cerebrovascular disease and cerebrovascular disease, applied in the fields of medical data mining, medical informatics, health index calculation, etc., can solve the problems of lack of statistical analysis of patients, lack of family genetic factors, and reduced model accuracy. Achieve the effect of improving prediction accuracy, increasing correlation, and ensuring accuracy

Pending Publication Date: 2021-10-08
THE FIRST AFFILIATED HOSPITAL OF XINXIANG MEDICAL UNIV
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, the above method is to compare the measured value of MP-8 and CRP concentration with the standard value to obtain the value, so as to construct the prediction model of cardiovascular and cerebrovascular, but it lacks the statistical analysis of the patients in the immediate relatives, so it lacks the consideration of family inheritance Factors that reduce the accuracy of the model, so a method for predicting the risk of acute cerebrovascular disease is needed

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] The first step is to collect data on age, gender, total cholesterol, high-density lipoprotein cholesterol, blood pressure, diabetes, smoking, and family medical history of a large number of patients;

[0041] The second step is to group the collected data so that the same data are in the same group, then extract the abnormal values ​​in the same group of data, and then calculate the p value of the value in this group, if p>0.05, It proves that the value does not have statistical significance to be eliminated, and the data is eliminated to avoid impact on subsequent modeling;

[0042] The third step is to classify the relationship between the data after data processing, and analyze the correlation between families at the same time, establish the onset model of acute cerebrovascular disease based on the information after data processing, and at the same time analyze the year and degree of onset Carry out segmental modeling, taking into account physical function, divide th...

Embodiment 2

[0051] The first step is to collect the age, gender, total cholesterol, high-density lipoprotein cholesterol, blood pressure, diabetes, and smoking data of a large number of patients;

[0052] The second step is to group the collected data so that the same data are in the same group, then extract the abnormal values ​​in the same group of data, and then calculate the p value of the value in this group, if p>0.05, It proves that the value does not have statistical significance to be eliminated, and the data is eliminated to avoid impact on subsequent modeling;

[0053] The third step is to classify the relationship between the data after data processing, establish the onset model of acute cerebrovascular disease based on the information after data processing, and conduct segmental modeling on the year and degree of onset, taking into account physical functions, predict The age of patients is divided into three segments: 25-45 years old, 46-65 years old and 66-85 years old. Afte...

Embodiment 3

[0062] The first step is to collect data on age, gender, total cholesterol, high-density lipoprotein cholesterol, blood pressure, diabetes, smoking, and family medical history of a large number of patients;

[0063] The second step is to group the collected data so that the same data is in the same group;

[0064] The third step is to classify the relationship between the data after data processing, and analyze the correlation between families at the same time, establish the onset model of acute cerebrovascular disease based on the information after data processing, and at the same time analyze the year and degree of onset Carry out segmental modeling, taking into account physical function, divide the predicted age into three segments: 25-45 years old, 46-65 years old, and 66-85 years old. The probability of onset during the period improves the prediction accuracy of acute cerebrovascular disease;

[0065] The fourth step is to compare the data of the patient who needs to be ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for predicting an occurrence risk of an acute cerebrovascular disease, and relates to the field of acute cerebrovascular diseases. The technical scheme comprises the following steps: step 1, data collection: collecting data of a large number of patients, and collecting body data of immediate family members of the patients suffering from the acute cerebrovascular disease; step 2, data processing: processing the collected data, paralleling each type of data, and removing abnormal data in the data at the same time; and step 3, carrying out grading processing on the relationship between the data after data processing, and analyzing the relevance between families at the same time. According to the method, the data of the patients is counted during data statistics, meanwhile, the data of the patients suffering from the acute cerebrovascular disease among the immediate family members of the patients is counted, and the data relevance between the patients and the immediate family members suffering from the acute cerebrovascular disease is analyzed, so that the relevance between the data can be increased, and the prediction accuracy is ensured.

Description

technical field [0001] The invention relates to the technical field of acute cerebrovascular disease prediction, in particular to a method for predicting the risk of acute cerebrovascular disease. Background technique [0002] The risk factors of acute cerebrovascular disease often appear in the same individual at the same time, and the combined effect of multiple risk factors determines the risk of acute cerebrovascular disease in an individual. [0003] Differences between regions and differences in physical fitness between races will affect the prediction of acute cerebrovascular disease. Total cholesterol, high-density lipoprotein cholesterol, blood pressure, diabetes and smoking are all important predictors of acute cerebrovascular disease. factor; [0004] After searching, the invention patent with Chinese patent number CN109791143A involves a new method for determining the risk of cardiovascular disease, including detecting MMP-8 and CRP in the sample, and comparing ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16H50/70
CPCG16H50/30G16H50/70
Inventor 张平贵永堃闫海清闫志新任瑞芳
Owner THE FIRST AFFILIATED HOSPITAL OF XINXIANG MEDICAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com