Warfarin dose prediction modeling method based on integrated evolution learning

Abstract

The invention discloses a warfarin dose prediction modeling method based on integrated evolution learning. The method comprises the following steps: (1) based on genetic coding, evoluting primary functions; (2) calculating a coefficient based on an evolutionary strategy; (3) obtaining M candidate models based on the selection and optimization of a genetic algorithm framework; (4) selected m modelsto perform weight-sum integration based on the M candidate models, and obtaining a final prediction model; integrally learning an obtained regression prediction model to perform actual prediction oftest set data. According to the method, the model is constructed in an integrated evolution learning mode, and the integrated evolution learning is a key point for constructing a model for accuratelypredicting the warfarin dose; compared with modeling methods such as a Bayesian decision system, an artificial neural network and a support vector machine, an integrated evolutionary regression modelhas better generalization capability. Compared with an individual evolution regression model, the integrated evolutionary regression model has better stability.

Description

technical field [0001] The invention relates to a warfarin dose prediction modeling method based on integrated evolutionary learning. Background technique [0002] Warfarin is the most widely used oral anticoagulant in the world for the prevention and treatment of thromboembolic disorders. Recent work by the International Warfarin Pharmacogenetics Consortium (IWPC) has shown that clinical factors account for 26% of warfarin dose, with CYP2C9 and VKORC1 genotypes increasing to 43%. However, there is currently no evidence that some clinical variables such as alcohol consumption, sex, and renal function clearly affect warfarin dosing. Because of such large interindividual variability, anticoagulant therapy with warfarin dosing is often complex and unpredictable. In fact, this makes accurate prediction of warfarin dose more difficult. During anticoagulant therapy, the INR (International normalized ratio) of prothrombin should be closely monitored to ensure the selection of an...

AI Technical Summary

Problems solved by technology

Although many advances in support vector machines have been achieved, specific kernel functions are only suitable for certain kinds of problems, and choosing an appropriate kernel function when modeling warfarin dose prediction is a rather difficult task

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