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In this work, we introduce a data\u2010driven AP adaptation method for improved glucose management in a home environment. A two\u2010phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate\u2010ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111\u2010adult cohort of the FDA\u2010accepted UVA\/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. 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