Stochastic subspace integrated learning based aortic dissection screening model and establishment method, system and model thereof

A random subspace and aortic dissection technology, applied in medical simulation, informatics, medical informatics, etc., can solve the problems of low missed diagnosis rate, high misdiagnosis rate, and high cost

Active Publication Date: 2018-12-25
XIANGYA HOSPITAL CENT SOUTH UNIV +2
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a method based on stochastic subspace integration learning in view of the shortcomings of the existing traditional di...

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  • Stochastic subspace integrated learning based aortic dissection screening model and establishment method, system and model thereof
  • Stochastic subspace integrated learning based aortic dissection screening model and establishment method, system and model thereof
  • Stochastic subspace integrated learning based aortic dissection screening model and establishment method, system and model thereof

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Embodiment Construction

[0097] For further illustrating implementation process of the present invention, adopt following experiment to verify beneficial effect of the present invention now:

[0098] The data set used in this experiment comes from the First Xiangya Hospital. The data set includes 53,213 collected patients as samples. The total number of patients with aorta is 802, and the number of non-patients is 52,411. That is, there are 802 positive samples and negative samples. There are 52411 samples, the ratio of positive and negative samples is 1:65, and 85 indicators are extracted as sample features.

[0099] 1) Divide the data set into 7 mutually exclusive subsets of similar size, and each subset ensures the distribution consistency as much as possible, that is, it is obtained from stratified sampling in the data set, and each time the union of 6 subsets is used as the training set, and the remaining That subset is used as the test set for 7 training and testing;

[0100] 2) In the training...

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Abstract

The invention discloses a stochastic subspace integrated learning based aortic dissection screening model and an establishment method, system and model thereof. To overcome the defects that the existing conventional diagnostic methods are low in efficiency of aortic dissection diagnosis, are high in misdiagnosis rate, are high in missed diagnosis rate, are high in cost, and are complex in process,the invention provides a stochastic subspace integrated learning based aortic dissection screening method. The method uses the RS-Ensemble algorithm of machine learning to establish an RS model, anduses the RS model to screen and diagnose the aortic dissection, which greatly improves the diagnostic accuracy, greatly reduces the rate of misdiagnosis and missed diagnosis, and achieves efficient and low-cost diagnosis. .

Description

technical field [0001] The invention relates to the fields of medicine and artificial intelligence, in particular to an aortic dissection screening model based on stochastic subspace integration learning and its establishment method, system and model. Background technique [0002] Aortic dissection is a relatively rare clinical emergency. Its pathogenesis is that the blood in the aortic lumen enters the aortic wall from the aortic intima breach under aortic pressure, and then forms a dissection in the aortic wall. Hematoma, and extended along the longitudinal axis of the aorta to form a "double lumen aorta". This is a very dangerous cardiovascular disease. The mortality rate of the disease is 1% to 2% per hour in the first 24 hours of onset, and the mortality rate within a week is as high as 60% to 70%. Most patients without treatment Both die within a year. [0003] At present, the diagnostic methods of aortic dissection are mainly imaging methods and ultrasound methods. ...

Claims

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Application Information

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IPC IPC(8): G16H50/70G16H50/50
CPCG16H50/50G16H50/70
Inventor 张国刚刘丽珏柏勇平谭世洋罗靖旻穆阳张伟
Owner XIANGYA HOSPITAL CENT SOUTH UNIV
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