A machine learning-based method for predicting risk of lower extremity venous thrombosis

Abstract

The application discloses a lower limb venous thrombosis risk prediction method based on machine learning, which comprises the following steps: step one, constructing a three-axis thrombus evolution dynamic container space; step two, generating a pre-thrombus microstate orbit chain; step three, determining a thrombus formation critical mutation window; step four, introducing a causal intervention operator at the thrombus formation critical mutation window to construct a natural evolution path and a controlled path; step five, obtaining an orbit offset difference value; step six, calculating a dynamic orbit stability index; step seven, inputting the pre-thrombus microstate orbit chain and the dynamic orbit stability index into an improved PatchTST model, introducing a flow continuity adjustment operator into a reconfiguration module, and obtaining a corrected orbit stability index; and step eight, outputting a thrombus formation risk prediction result. The application realizes thrombus formation risk prediction by constructing a three-axis thrombus evolution dynamic container space and combining a neuro-architecture differential equation model and an improved PatchTST model.

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