Fatty liver disease risk prediction method and device

A technology of disease risk and prediction method, applied in the direction of neural learning method, biological neural network model, health index calculation, etc., can solve the problem of low utilization efficiency of health longitudinal physical examination data, and achieve easy calculation, good stability and fitting effect of effect

Pending Publication Date: 2022-03-15
CHENGDU SEFON SOFTWARE CO LTD
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method and device for predicting the risk of fatty liver disease to solve the problems in the prior art that the utilization efficiency of health longitudinal physical examination data is low, and there is no accurate method for predicting the risk of fatty liver disease

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
  • Fatty liver disease risk prediction method and device
  • Fatty liver disease risk prediction method and device
  • Fatty liver disease risk prediction method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0026] like figure 1 , a fatty liver disease risk prediction method, comprising the following steps:

[0027] S1: Collect longitudinal physical examination data, establish a health examination queue, and use different methods to complete data cleaning according to the data of different variables to improve data quality.

[0028] The data in the preparation of the analyzed data described in step S1 should be longitudinal cohort data, and the data variables and longitudinal process should be clear and valid. The specific implementation method is as follows, establish a vertical queue, assuming that the year is {C1, C2, C3, C4.....Cn}, a total of n fields, the structure of the entire data should be:

[0029] [{C11,C12,C13,C14.....C1n},

[0030] {C21,C22,C23,C24.....C2n},

[0031] {C31,C32,C33,C34.....C3n},

[0032] …

[0033] {Cm1, Cm2, Cm3, Cm4.....Cmn}]

[0034] The meaning of C12 is the second variable in the first year data. In the data cleaning described above, the pr...

Embodiment 2

[0058] A fatty liver disease risk prediction device includes a memory: for storing executable instructions; a processor: for executing the executable instructions stored in the memory, to realize a fatty liver disease risk prediction method.

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 fatty liver disease risk prediction method which mainly solves the problems that in the prior art, the utilization efficiency of health longitudinal physical examination data is low, and an accurate method for predicting the fatty liver disease risk does not exist. The method comprises the following steps of collecting longitudinal physical examination data, establishing a longitudinal queue, performing data cleaning, and improving data quality; a random forest algorithm is adopted to screen out fatty liver influence factors; selecting an LSTM machine learning algorithm suitable for time dynamic process prediction; and combining a recurrent neural network LSTM algorithm with a time-dependent Cox survival function to establish an LSTM-Joint joint model. Modeling is carried out according to the association strength between the longitudinal process and the survival result in the longitudinal queue data, and the estimation efficiency is improved, so that a better prediction result is obtained.

Description

technical field [0001] The invention relates to a method for predicting the risk of fatty liver disease, specifically, a method for predicting the risk of fatty liver disease on the basis of longitudinal physical examination data. Background technique [0002] Longitudinal physical examination data refer to multiple indicators of the same group of physical examination persons at different times, such as the results of multiple physical examinations within a year. Over the years, physical examinations have accumulated a large amount of health cohort measurement data. Since the data do not satisfy the independence assumption, conventional statistical analysis methods cannot be used for modeling analysis. [0003] In recent years, with the changes in people's consumption structure and living habits, the prevalence of diabetes, overweight and obesity has increased, and the prevalence of fatty liver in my country has increased year by year, reaching 27%. At present, there are ne...

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): G16H50/20G16H50/30G06K9/62G06N3/04G06N3/08G06N20/00
CPCG16H50/20G16H50/30G06N3/08G06N20/00G06N3/044G06F18/214
Inventor 雷丽赵红军
Owner CHENGDU SEFON SOFTWARE CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap