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Deep learning-based diabetic nephropathy early prediction method and system

A technology of diabetic nephropathy and deep learning, applied in the field of early prediction methods and systems of diabetic nephropathy, can solve the problems of complex omics data structure and insufficient statistical analysis methods to meet the needs of result accuracy and efficiency, so as to improve efficiency and accuracy Effect

Pending Publication Date: 2021-11-23
TIANJIN UNIV OF TRADITIONAL CHINESE MEDICINE
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, with the advent of the era of big data, omics data presents the characteristics of massive, high-dimensional, complex structure, and structured. Traditional statistical analysis methods are not enough to meet the accuracy and efficiency of the results.

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  • Deep learning-based diabetic nephropathy early prediction method and system
  • Deep learning-based diabetic nephropathy early prediction method and system
  • Deep learning-based diabetic nephropathy early prediction method and system

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Embodiment Construction

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0038] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0039] The present invention provides a method for early prediction of diabetic nephropathy based on deep learning, such as figure 1 shown, including the following steps:

[0040] S1. Select a subject, collect a ...

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Abstract

The invention discloses a deep learning-based diabetic nephropathy early prediction method and system. The method comprises the following steps of: S1, collecting a subject sample, and extracting proteomics and lipidomics; S2, analyzing the proteomics and the lipidomics to obtain primitive characters; S3, screening the primitive characters, and extracting fusion features; S4, analyzing correlation among the fusion features to obtain a biomarker for early prediction of diabetic nephropathy; and S5, based on the biomarker for early prediction of diabetic nephropathy, conducting early prediction of diabetic nephropathy. According to the deep learning-based diabetic nephropathy early prediction method, massive and high-dimensional data volume calculation is carried out by using deep learning, sensitive correlation characteristics between diabetes and nephropathy are found, and the early prediction efficiency is improved.

Description

technical field [0001] The present invention relates to the field of computer applications, in particular to a method and system for early prediction of diabetic nephropathy based on deep learning. Background technique [0002] Diabetes (Diabetemellitus, DM) is a metabolic disease characterized by hyperglycemia. Among many complications, diabetic nephropathy (DKD) It is one of the most serious complications and a major cause of chronic kidney disease and kidney failure. At present, urinary microalbumin is the most commonly used indicator for the diagnosis and evaluation of the progression of DKD. However, before its level is abnormal, renal pathology has already appeared in some patients, and the limited sensitivity and specificity cannot meet the early prediction of DKD. [0003] Driven by the rapid development of high-throughput technologies, lipidomics and proteomics have enabled unprecedented insight into dynamic circulating biomarkers of DKD. As participants in metabo...

Claims

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Application Information

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IPC IPC(8): G01N33/68G01N33/66G06N3/08
CPCG01N33/68G01N33/66G06N3/08Y02A90/10
Inventor 李遇伯王玉明孙桂江赵换
Owner TIANJIN UNIV OF TRADITIONAL CHINESE MEDICINE
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