ai Chronic kidney disease risk screening modeling method, chronic kidney disease risk screening method and system

A technology for chronic kidney disease and risk, applied in neural learning methods, biological neural network models, medical automated diagnosis, etc., can solve problems such as unfavorable and efficient census

Active Publication Date: 2020-12-29
SHENTAIWANG HEALTHCARE TECH NANJING CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, chronic kidney disease risk screening requires the examiner to conduct an examination in the hospital, which is judged by nephrologists in combination with clinical guidelines and practical experience, which is not conducive to efficient general screening

Method used

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  • ai Chronic kidney disease risk screening modeling method, chronic kidney disease risk screening method and system

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0085] Such as figure 1 As shown, an AI chronic kidney disease risk screening method includes the following steps:

[0086] Step S1, establishing an effective chronic kidney disease risk screening model;

[0087] Step S2, sorting out user data to be screened;

[0088] In step S3, the data of the user to be screened is substituted into the chronic kidney disease risk screening model for model calculation, and finally the kidney disease risk prediction result is obtained.

[0089] Establishing an effective CKD risk screening model includes the following steps:

[0090] Step S11: Prepare medical record data; collect electronic medical records of patients from the hospital electronic medical record platform, and collect electronic medical records of patients with chronic kidney disease and non-chronic kidney disease;

[0091] The method for collecting electronic medical records of patients with chronic kidney disease as a result of the diagnosis is as follows: comparing the dia...

Embodiment 2

[0144] The present invention also proposes a method for constructing an AI chronic kidney disease risk screening model, comprising the following steps: A1: the step of obtaining the chronic kidney disease risk screening model from training data

[0145] Using the sklearn package of the python development language, three models of BP neural network, XGBoost and random forest were used to establish an integrated learning classifier system; a suitable chronic kidney disease risk screening parameter set capable of discriminating chronic kidney disease was established. In the three models of XGBoost and random forest, the data are trained and iteratively trained, and the chronic kidney disease risk screening parameter set is tuned, and finally a suitable chronic kidney disease risk screening parameter set that can distinguish chronic kidney disease is obtained. The risk of chronic kidney disease The screening parameter set includes the neuron weight and bias of the adaptive BP neura...

Embodiment 3

[0164] Further, the present invention also proposes an AI chronic kidney disease risk screening system, including a chronic kidney disease risk effective risk screening model, which includes three models of BP neural network, XGBoost and random forest The established ensemble learning classifier system, and a suitable chronic kidney disease risk screening parameter set capable of discriminating chronic kidney disease.

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Abstract

The present invention provides a method and system for chronic kidney disease risk screening, and specifically relates to machine learning methods to construct a chronic kidney disease risk screening model, including establishing an effective chronic kidney disease risk screening model, sorting out user data to be screened, and User data is substituted into the chronic kidney disease risk screening model for model calculation, and finally the result of kidney disease risk is obtained. In this way, a high-efficiency, low-cost, and high-accuracy chronic kidney disease risk screening system can be realized. The invention uses machine learning BP neural network, XGBoost and random forest integrated algorithm to train the chronic kidney disease risk screening model, which can automatically screen according to the basic body measurement information, symptom information, medical examination information, family history, past history, living habits and other data. To identify high-risk groups of chronic kidney disease, the accuracy rate is as high as 0.96 or more.

Description

technical field [0001] The present invention relates to a chronic kidney disease risk screening method and system, in particular to a machine learning method to construct a chronic kidney disease risk screening model, a chronic kidney disease risk screening and evaluation method and system, using the model, evaluation method and system for medical examiners Medical characteristic indicators are used to screen, and the risk assessment value of chronic kidney disease is given, so as to achieve high-efficiency, low-cost, and high-accuracy chronic kidney disease risk screening. Background technique [0002] Chronic kidney disease has the characteristics of high prevalence, low awareness rate, poor prognosis and high medical expenses. It is another disease that seriously endangers human health after cardiovascular and cerebrovascular diseases, diabetes and malignant tumors. In recent years, with the aging of my country's population, the incidence of diseases such as diabetes and ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H50/30G16H50/20G06N3/08G06N20/20
CPCG06N3/084G16H50/20G16H50/30G06N20/20
Inventor 黎海源
Owner SHENTAIWANG HEALTHCARE TECH NANJING CO LTD
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