A method constructs patient-specific hard prompts, refined only by word deletion with a BERTScore reward, to make proprietary LLMs output more personalized medical responses.
It contains 194,345 entries, each with 302 examination indicators, and corresponding dis- charge diagnoses are used as labels and reference responses
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From General to Specific: Tailoring Large Language Models for Personalized Healthcare
A method constructs patient-specific hard prompts, refined only by word deletion with a BERTScore reward, to make proprietary LLMs output more personalized medical responses.