Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Construction method of decompensated liver cirrhosis combined infection risk prediction model

A technology for decompensation and prediction model, which is applied in the field of construction of a risk prediction model for decompensated liver cirrhosis complicated by infection, and achieves the effect of high reference value, high reliability, and simple and easy-to-use model.

Inactive Publication Date: 2020-11-27
CHONGQING MEDICAL UNIVERSITY
View PDF4 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

So far, there is no report on the risk prediction model of decompensated liver cirrhosis complicated with infection

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
  • Construction method of decompensated liver cirrhosis combined infection risk prediction model
  • Construction method of decompensated liver cirrhosis combined infection risk prediction model
  • Construction method of decompensated liver cirrhosis combined infection risk prediction model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] 1. Objects and methods

[0033] 1. Research object

[0034] Inpatients with decompensated liver cirrhosis who visited seven medical institutions in Chongqing from January 2013 to June 2020 were collected. Inclusion criteria: decompensated liver cirrhosis; age >18 years old. Exclusion criteria: other cancers except primary liver cancer; mental illness; pregnant and lactating women; incomplete medical records. A total of 6648 patients were finally included in the study, who were divided into infection group (n=3430) and non-infection group (n=3218) according to whether they were co-infected or not. 70% (4654 cases) of the patients included in the study were randomly selected as the training set to construct the prediction model, and the remaining 30% (1994 cases) were used as the test set for internal validation.

[0035] 2. Research indicators

[0036] Included indicators in this study are as follows: age, sex, smoking history, drinking history, liver disease history...

Embodiment 2

[0060] Embodiment 2 application example

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 construction method of a decompensated liver cirrhosis combined infection risk prediction model. The method comprises the following steps: S1, data acquisition: collecting decompensated liver cirrhosis patient information; S2, data preprocessing: cleaning and sorting the original data; S3, index screening by adopting LASSO regression: dividing the patients into an infection group and a non-infection group according to whether the patients have combined infection or not, and performing single-factor analysis on the grouped index data of the patients to obtain single-factor meaningful indexes; bringing the single-factor meaningful indexes into Lasso regression for index re-screening to obtain indexes for constructing a prediction model; and S4, prediction model construction: constructing the prediction model by using the indexes screened by Lasso regression through multi-factor Logistic regression. The method is based on application of a clinical big data methodand is high in reliability; the constructed model is simple and easy to use, and the used indexes can be obtained through conventional inspection and are easy to obtain.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a method for constructing a risk prediction model for decompensated liver cirrhosis combined with infection. Background technique [0002] Patients with decompensated liver cirrhosis are in critical condition and have many complications. Infection is the most common complication, which is an important predisposing factor for severe complications such as upper gastrointestinal bleeding, hepatic encephalopathy, and hepatorenal syndrome, and is the leading cause of death. One of the main reasons. Understanding the characteristics of decompensated liver cirrhosis with infection, finding out the risk factors for infection and constructing an infection prediction model are of great significance for early diagnosis, early prevention and effective treatment, and improving the survival rate of patients. So far, there is no report on the risk prediction model of decompensated liver cirrh...

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): G16H50/30G16H50/70
CPCG16H50/30G16H50/70
Inventor 何雨芯蒲川
Owner CHONGQING MEDICAL UNIVERSITY
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
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