RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
Change matters: Medication change prediction with recurrent residual networks
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FLAME applies fine-grained list-wise alignment and Group Relative Policy Optimization to large language models for generating safe medication recommendations by sequentially adding or removing drugs.
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RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
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Fine-grained List-wise Alignment for Generative Medication Recommendation
FLAME applies fine-grained list-wise alignment and Group Relative Policy Optimization to large language models for generating safe medication recommendations by sequentially adding or removing drugs.