Hemodialysis risk prediction system based on combination of dynamic and static data and depth auto-encoder

A self-encoder and data combination technology, applied in the field of hemodialysis risk prediction system, can solve problems such as data imbalance and insufficient classification model training, and achieve the effect of improving prediction performance and improving prediction performance

Pending Publication Date: 2021-08-17
ZHEJIANG UNIV
View PDF4 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Aiming at the existing problems, the present invention utilizes the time series data in EHRs to build a hemodialysis risk pre...

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
  • Hemodialysis risk prediction system based on combination of dynamic and static data and depth auto-encoder
  • Hemodialysis risk prediction system based on combination of dynamic and static data and depth auto-encoder
  • Hemodialysis risk prediction system based on combination of dynamic and static data and depth auto-encoder

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] In order to make the above objects, features and advantages of the present invention more comprehensible, specific implementations of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0041] In the following description, a lot of specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, and those skilled in the art can do it without departing from the meaning of the present invention. By analogy, the present invention is therefore not limited to the specific examples disclosed below.

[0042] Such as figure 1 , 2 As shown, this application proposes a hemodialysis risk prediction system based on dynamic and static data combination and deep autoencoder, including:

[0043] 1) Data acquisition module

[0044] The data to be obtained mainly includes patient visit data, physical sign data and lab...

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 hemodialysis risk prediction system based on combination of dynamic and static data and a depth auto-encoder. The system is based on a depth auto-encoder.Relevance between high-dimension EHR time sequence data is modeled by utilizing blood pressure, weight and other sign data and hemodialysis treatment related data such as dialysis mode and membrane area collected in the hemodialysis treatment process of a patient, and hemodialysis risk of the patient is predicted; a multi-input model is constructed by combining static and dynamic data, the dynamic data is partially introduced into an LSTM auto-encoder network, a time interval between time sequence data is considered, the static data is integrated as additional input, and a multi-layer neural network is introduced; and the model is trained only by using survival samples, so that the model prediction performance is improved and the influence on model training caused by artificial data amplification is reduced on the premise that the number of dead samples is small or dead samples are not amplified.

Description

technical field [0001] The invention belongs to the technical field of medical treatment and machine learning, and in particular relates to a hemodialysis risk prediction system based on the combination of dynamic and static data and a deep self-encoder. Background technique [0002] End-stage renal disease (End-Stage Renal Disease, ESRD), commonly known as uremia, is the end stage of chronic kidney disease, requiring renal replacement therapy for long-term survival, and most patients choose dialysis for lack of suitable kidney sources. L. The early mortality rate of hemodialysis patients is relatively high, and with periodic hemodialysis treatment, the mortality rate is gradually decreasing, but the three-year mortality rate of hemodialysis patients is still as high as 43%. Although the current research has found some high-risk factors related to death, such as age, serum albumin, blood phosphorus, residual renal function, cardiovascular complications, etc., how to evaluat...

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): G16H50/30G16H50/20G16H50/70G16H10/60A61B5/00G06N3/04G06N3/08G06N20/00
CPCG16H50/30G16H50/20G16H50/70G16H10/60G06N3/08G06N20/00A61B5/7275G06N3/044
Inventor 李劲松田雨周天舒娄国锋
Owner ZHEJIANG UNIV
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