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Traditional machine learning\u2010based models for personalized medication regimens typically rely solely on either clinical macro\u2010phenotypes or molecular\u2010level drug characteristics. It remains challenging to capture the patient\u2010medication relationship from a comprehensive perspective that integrates individual patient characteristics with macro\u2010 and micro\u2010level properties of the medication. Determining patient\u2010medication relationships constitutes a three\u2010stage sequential decision process from a clinical decision\u2010making perspective. Therefore, inspired by Chain\u2010of\u2010Thought prompting, which simulates the decision\u2010making process of human experts, a Multimodal Data\u2010Driven Chain\u2010of\u2010Decisions (MDD\u2010CoD) framework is proposed, where three\u2010stage deep learning tasks are sequentially organized to reflect upstream\u2013downstream logical dependencies, thereby forming a coherent clinical decision\u2010making process. 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