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Intelligent evaluation and diagnosis method and system for heart disease types and severity degrees

A technology of severity and diagnosis method, applied in the field of application and analysis of medical data, can solve problems such as deviation and difficulty in diagnosis by doctors, and achieve the effect of improving the accuracy rate, improving the accuracy of prediction, and strengthening the practical application value

Inactive Publication Date: 2018-12-18
杨成伟
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide an intelligent assessment and diagnosis method and system for the type and severity of heart disease, which is used to solve the existing problems caused by the limitations of medical conditions, doctors' energy, time, experience, diagnosis report description and other reasons. Difficult or biased issues to diagnose

Method used

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  • Intelligent evaluation and diagnosis method and system for heart disease types and severity degrees
  • Intelligent evaluation and diagnosis method and system for heart disease types and severity degrees
  • Intelligent evaluation and diagnosis method and system for heart disease types and severity degrees

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] An intelligent assessment and diagnosis method for the type and severity of heart disease, including:

[0031] obtain disease characteristic data and demographic characteristic data;

[0032] Using the learning model to analyze the acquired echocardiographic report data and patient demographic data, and obtain the model evaluation index, heart disease type and heart disease severity.

[0033] Another aspect of this embodiment discloses a system for applying the intelligent assessment and diagnosis method for heart disease type and severity according to any one of claims 1-9.

Embodiment 2

[0035] The disease characteristic data include LVEF, thickening of mitral valve leaflets, enhanced mitral valve echo, thickened aortic valve leaflets, unclear display of aortic valve leaflets, enhanced aortic valve echo, restricted opening of aortic valve, aortic valve There is a gap when the valve is closed, estimated aortic valve area, left atrium, left ventricle, right atrium, right ventricle, slightly diffuse wall motion decrease, systolic right atrium exploration and distribution limit tricuspid regurgitation Maximum jet regurgitation pressure gradient, systolic aortic valve anterior blood flow acceleration maximum regurgitation pressure gradient, aortic valve disease, aortic valve stenosis, aortic regurgitation, mitral valve sclerosis, mitral valve regurgitation , tricuspid regurgitation, slightly reduced left ventricular systolic function, pulmonary hypertension, left ventricular hypertrophy, left heart enlargement, and at least one of cardiomyopathy cannot be ruled out....

Embodiment 3

[0037] The learning model is one of a random forest model, a naive Bayesian classifier, a decision tree model, and a BP neural network model.

[0038] The learning model is a random forest model.

[0039] The model evaluation index includes at least one of Correctly, TP Rate, FP Rate, Precision, Recall, F value, area of ​​Roc curve, Accuracy and threshold.

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Abstract

The invention discloses an intelligent evaluation and diagnosis method and a system for heart disease types and severity degrees. The method comprises the steps of acquiring disease characteristic data and demographic characteristic data, and analyzing the acquired ultrasonic echocardiogram report data and the patient demographic characteristic data by utilizing a learning model to obtain a modelevaluation index, a heart disease type and a heart disease severity. According to the invention, a data mining method is adopted, so that data preprocessing, data screening and other operations are carried out on data through the data mining correlation method. The method is adopted for selecting a noise ratio during the characteristics selection process. A random forest model is adopted for carrying out the classification prediction of the heart disease severity. Meanwhile, an effective research method is obtained through comparing and analyzing the algorithm performances and the learning effects of the random forest model, a naive Bayes classifier, a decision tree model and a BP neural network model. Moreover, a standard for the severity classification of heart disease patients and a prediction method for predicting the treatment risk of the heart disease operation are provided.

Description

technical field [0001] The invention relates to the technical field of application and analysis of medical data, in particular to an intelligent evaluation and diagnosis method and system for the type and severity of heart disease. Background technique [0002] In the existing heart disease diagnosis method, the clinician needs to complete the accurate judgment of each patient's heart disease type and the assessment of the severity of heart disease according to the heart disease diagnosis report. This requires doctors to have solid theoretical knowledge and years of clinical experience to complete. However, in actual diagnosis, it is often difficult or biased by doctors due to limitations of medical conditions, doctors' energy, time, experience, and descriptions in diagnosis reports. Contents of the invention [0003] The purpose of the present invention is to provide an intelligent assessment and diagnosis method and system for the type and severity of heart disease, whi...

Claims

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Application Information

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IPC IPC(8): A61B8/08
CPCA61B8/0883A61B8/5223A61B8/5284
Inventor 杨成伟
Owner 杨成伟
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