AI RESEARCH
RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
arXiv CS.CL
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ArXi:2604.17725v1 Announce Type: new Large Language Models (LLMs) have shown strong promise for mining Electronic Health Records (EHRs) by reasoning over longitudinal clinical information to capture context-rich patient trajectories. However, leveraging LLMs for structured EHRs (e.g., standardized diagnosis and medication codes) presents two key challenges. First, translating time-stamped EHR sequences into plain text can obscure both temporal structure and code identities, weakening the ability to capture code co-occurrence and longitudinal regularities.