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

Chronic-disease recurrence forecasting method and device based on xgboost model and computer equipment

A prediction method and technology for chronic diseases, applied in computer-aided medical procedures, health index calculation, medical informatics, etc., can solve the problems of low accuracy of calculation results, achieve the effect of solving unbalanced distribution and improving accuracy

Active Publication Date: 2019-03-19
THE FIRST AFFILIATED HOSPITAL OF GUANGZHOU MEDICAL UNIV (GUANGZHOU RESPIRATORY CENT) +1
View PDF7 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Based on this, it is necessary to address the technical problem of low accuracy of calculation results in the above-mentioned chronic disease recurrence prediction method, and provide a chronic disease recurrence prediction method, device, computer equipment and storage medium based on the xgboost model that can reasonably solve the above technical problems

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

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Chronic-disease recurrence forecasting method and device based on xgboost model and computer equipment
  • Chronic-disease recurrence forecasting method and device based on xgboost model and computer equipment
  • Chronic-disease recurrence forecasting method and device based on xgboost model and computer equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0046] It should be noted that the prediction model chooses xgboost (Extreme Gradient Boosting). The main algorithm idea is to train a base learner with initial samples, and adjust the sample distribution according to the learning performance, so that samples with poor performance get more attention, and then Continuously iteratively train the next base learner with samples after adjusting the distribution until the number of base learners reaches the specified number.

[0047] In one embodiment, such as figure 1 As shown, a chronic disease recurrence prediction method ba...

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 relates to a chronic-disease recurrence forecasting method and device based on a xgboost model and computer equipment. The method includes the steps that to-be-detected diagnostic data is obtained; the to-be-detected diagnostic data comprises hospitalizing-and-hospital discharge time; according to the hospitalizing-and-hospital discharge time, the to-be-detected diagnostic data serves as a model training sample; the model training sample is used for training the xgboost model, and the trained model is obtained; the trained model is used for carrying out chronic-disease recurrenceforecasting on the to-be-detected diagnostic data. According to the technical scheme, the problem that training samples are unbalanced in distribution can be solved, and thus the accuracy of a chronic-disease recurrence forecasting result is improved.

Description

technical field [0001] The present application relates to the field of disease prevention and control, in particular to a chronic disease recurrence prediction method, device, computer equipment and storage medium based on the xgboost model. Background technique [0002] Chronic obstructive pulmonary disease (COPD), referred to as COPD, has always been a chronic obstructive disease that is difficult to cure. According to statistics, in 2015, an estimated 3.17 million people died of COPD worldwide, accounting for 5% of the world's mortality rate in the same year; there were 251 million cases of COPD in the world in 2016, and the impact of COPD on human life The threat can no longer be ignored. [0003] In order to improve this situation, more and more scholars and medical institutions have begun to pay attention to the prediction of the recurrence of the chronic disease within one year, so as to prevent the aggravation of the disease. However, the existing methods for predi...

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
IPC IPC(8): G16H50/30
CPCG16H50/30
Inventor 李菁罗俊宇
Owner THE FIRST AFFILIATED HOSPITAL OF GUANGZHOU MEDICAL UNIV (GUANGZHOU RESPIRATORY CENT)
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