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

A method for predicting mortality in ICU based on deep learning

A deep learning and prediction method technology, applied in the field of mortality prediction, can solve the problem of inaccurate prediction results of mortality of ICU patients, and achieve the effect of not easy to lose and fast convergence speed

Active Publication Date: 2021-12-07
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned deficiencies in the prior art, a method for predicting in-hospital mortality in ICU based on deep learning provided by the present invention solves the problem of inaccurate prediction of in-hospital mortality in ICU patients

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
  • A method for predicting mortality in ICU based on deep learning
  • A method for predicting mortality in ICU based on deep learning
  • A method for predicting mortality in ICU based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0047] Such as figure 1 As shown, a deep learning-based ICU mortality prediction method includes the following steps:

[0048] S1. Collect the signs and indicators of patients within 48 hours after admission to the ICU;

[0049] Physical indicators include discrete variables including capillary refill rate, Glascow Coma Scale eye opening, Glascow Coma Scale motor response, Glascow Coma Scale verbal response, and Glascow Coma...

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 mortality in an ICU based on deep learning. The present invention predicts the death in the ICU by using deep learning, not only using the patient's dynamic sign change data, but also adding important demographic characteristics such as age and disease type that affect the mortality rate; Based on the long-short-term memory model) network, three time gates are added to control the time increment change to solve the common irregular sampling and data missing problems of clinical data; the attention mechanism is introduced to fuse the hidden state at each moment , the model converges faster and is less likely to lose the information contained in the initial stage.

Description

technical field [0001] The invention relates to the technical field of mortality prediction, in particular to a method for predicting mortality in ICU hospitals based on deep learning. Background technique [0002] Intensive Care Unit (ICU) admits and treats various critically ill patients, and usually requires daily monitoring of patients, such as heart rate, ECG, blood pressure, respiration, body temperature, etc., as well as special monitoring for different patients, so A large amount of monitoring data is generated every day, including physiological data collected by various medical and biosensors, as well as subjective evaluation results of medical staff. These time-series data can be used to classify patients, estimate hospital stays, and predict mortality. The most concerned part of the link is the prediction of in-hospital mortality, that is, to predict whether the patient will die during the ICU stay. ICU mortality prediction can help clinicians make auxiliary deci...

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): G16H50/20G16H50/30G06N3/04G06N3/08
CPCG16H50/20G16H50/30G06N3/08G06N3/048G06N3/044G06N3/045
Inventor 刘勇国刘朗李巧勤杨尚明曹晨任志扬傅翀
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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