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

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. ...

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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 diagnostic methods for the diagnosis of aortic dissection, such as low efficiency, high misdiagnosis rate, low missed diagnosis rate, high cost, and complicated process. Aortic Dissection Screening Methods

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