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

Method and system for rehospitalization prediction based on time-series portraits

A time series and portrait technology, which is applied in the field of rehospitalization prediction based on patient medical time series portraits, can solve the problems of losing the benefits of graph representation, detachment, etc., achieve good medical services, solve sparsity problems, and rationally arrange medical resources.

Active Publication Date: 2019-02-26
SHANDONG UNIV
View PDF4 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Someone proposed a timing diagram representation method. The timing diagram can capture the timing relationship between different clinical events and can provide rich information for predictive analysis tasks. The structure itself is detached from the relationship, losing some of the benefits brought by the graph representation

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
  • Method and system for rehospitalization prediction based on time-series portraits
  • Method and system for rehospitalization prediction based on time-series portraits
  • Method and system for rehospitalization prediction based on time-series portraits

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0088] This embodiment provides a method for predicting rehospitalization based on patient medical time series portraits, such as figure 1 shown, including the following steps:

[0089] Step 1: Analyze and extract medical events from medical data, and serialize the extracted medical events in chronological order;

[0090] Step 2: Based on the serialized medical events, construct a medical sequence portrait for each patient;

[0091] Step 3: Use the improved AGM algorithm to mine the frequent subgraphs of all medical time series portraits;

[0092] Step 4: According to the frequent subgraphs, the Monte Carlo simulation method is used to calculate the corresponding reconstruction coefficients for all frequent subgraphs of each patient, and based on the reconstruction coefficients, the random forest algorithm is used to predict whether the patient will be rehospitalized.

[0093] The step 1, as in figure 2 shown, including:

[0094] Step 101: Analyzing patient data sets, spe...

Embodiment 2

[0148] Based on the second object of the present invention, according to the method for predicting rehospitalization, this embodiment provides a computer device for predicting rehospitalization of patients, including a memory, a processor, and a memory stored on the memory and can be used on the processor. A running computer program, wherein the processor implements the following steps when executing the program, including:

[0149] Step 1: Analyze and extract medical events from medical data, and serialize the extracted medical events in chronological order;

[0150] Step 2: Based on the serialized medical events, construct a medical sequence portrait for each patient;

[0151] Step 3: Use the improved AGM algorithm to mine the frequent subgraphs of all medical time series portraits;

[0152] Step 4: According to the frequent subgraphs, the Monte Carlo simulation method is used to calculate the corresponding reconstruction coefficients for all frequent subgraphs of each pati...

Embodiment 3

[0154] Based on the third object of the present invention, according to the method for rehospitalization prediction, this embodiment provides a computer-readable storage medium on which is stored a computer program for patient rehospitalization prediction, which is characterized in that the program is The following steps are implemented when the processor executes:

[0155] Step 1: Analyze and extract medical events from medical data, and serialize the extracted medical events in chronological order;

[0156] Step 2: Based on the serialized medical events, construct a medical sequence portrait for each patient;

[0157] Step 3: Use the improved AGM algorithm to mine the frequent subgraphs of all medical time series portraits;

[0158] Step 4: According to the frequent subgraphs, the Monte Carlo simulation method is used to calculate the corresponding reconstruction coefficients for all frequent subgraphs of each patient, and based on the reconstruction coefficients, the rando...

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 re-hospitalization prediction method based on sequential portraits of patients, which includes the following steps: analyzing and extracting medical events from medical data;serializing the extracted medical events in chronological order; constructing a medical sequential portrait graph for each patient based on the serialized medical events; mining the frequent sub-graphs of all the medical sequential portrait graphs through an improved AGM algorithm; and calculating the corresponding reconstruction coefficient for the frequent sub-graphs of each patient through a Monte Carlo simulation method according to the frequent sub-graphs, and predicting whether each patient will be re-hospitalized through a random forest algorithm based on the reconstruction coefficient. According to the technical scheme of the invention, patients can be helped to know the health condition thereof in advance, and medical institutions can be helped to provide better medical services.

Description

technical field [0001] The invention belongs to the field of health care, in particular to a method and system for rehospitalization prediction based on patient medical time-series portraits. Background technique [0002] The Central Committee of the Communist Party of China and the State Council have issued the "Outline of the "Healthy China 2030" Plan". The "Outline" clearly states that health is an inevitable requirement for promoting the overall development of people and a basic condition for economic and social development. With the rapid development of computer software and hardware and the comprehensive coverage of medical information systems, the field of health care has gradually accumulated a large amount of data. Data mining technology can mine valuable medical information from a large amount of medical data to achieve accurate and personalized disease prevention and early warning. [0003] In the existing graph-based rehospitalization prediction and disease risk...

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 Patents(China)
IPC IPC(8): G16H50/50G16H50/70G06Q10/04
Inventor 李晖徐祥朕郭伟崔立真
Owner SHANDONG 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