Sepsis early prediction method based on machine learning

A technology of machine learning and prediction methods, applied in the fields of instrumentation, informatics, medical informatics, etc., can solve problems such as low accuracy rate and difficulty in clinical diagnosis of sepsis in ICU patients, and achieve faster training speed and high prediction and classification accuracy , to improve the prognosis

Pending Publication Date: 2020-06-09
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF3 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of difficult and low accuracy in the clinical diagnosis of sepsis in ICU patients in the prior art, the present invention propos...

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
  • Sepsis early prediction method based on machine learning
  • Sepsis early prediction method based on machine learning
  • Sepsis early prediction method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] In order to better illustrate the technical solution, a more detailed description will be made below in conjunction with embodiments.

[0040] A method for early prediction of sepsis based on machine learning, comprising the following steps:

[0041]Step 1: Early prediction of sepsis, a predictive classification task, requires data acquisition first. Data is the cornerstone of research. For different disease problems, corresponding data need to be supported. ICU patients have done a lot of examinations, and the data records are also very complicated. Therefore, it is necessary to extract the required multiple predictive feature variables from the patient's electronic medical records or medical data sets. clinical data;

[0042] 1.1 Predictive feature variables commonly used in medical scoring systems

[0043] Clinically, the diagnosis of sepsis mainly uses medical scoring systems such as SOFA score to judge the patient's condition. The variables commonly used in thes...

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 sepsis early prediction method based on machine learning. The method comprises the following steps: firstly, extracting clinical data, including demographic statistics (suchas age and gender), vital sign variables (such as heart rate and systolic pressure) and laboratory measurement indexes (such as creatinine and platelet count), of a patient within 24 hours after a patient enters an ICU by utilizing an electronic medical record, preprocessing the data, inputting the preprocessed data into an improved deep forest algorithm model for training, and outputting the disease probability of the patient after training optimization. And meanwhile, the algorithm model can also sort characteristic variables and output an early warning factor which has great influence on early prediction of sepsis. Finally, corresponding variables of the patient needing to be predicted are input into the trained model, so that early prediction of sepsis can be carried out on the patient. According to the invention, early prediction is carried out on sepsis based on a machine learning method, doctors can be assisted in making clinical decisions, and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the field of medical data mining, and in particular relates to a method for early prediction of sepsis based on machine learning. Background technique [0002] Sepsis is a disease that poses a serious threat to life safety. Sepsis is a systemic inflammatory response syndrome caused by infection. It is one of the main causes of common high-risk complications and death in ICU patients. It is estimated that 30 million people in the world suffer from sepsis every year, and more than 6 million people die from sepsis. The cost of sepsis treatment is very high, and the risks are also very high. Due to the high morbidity, mortality and high cost of treatment, sepsis has become a public medical problem of great concern around the world. The clinical diagnosis definition of sepsis has developed from 1.0 to 3.0, and it is also constantly changing and updating. The latest clinical definition of sepsis-3 was proposed by the European Socie...

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): G16H50/20G06K9/62
CPCG16H50/20G06F18/214G06F18/24323G06F18/10
Inventor 付梦莎袁家斌
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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