Papers by Drew Ross
RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown promising results for mining EHRs . translating time-stamped sequences into plain text can obscure both temporal structure and code identities, weakening the ability to capture code co-occurrence and longitudinal regularities. |
| Approach: | They propose a time-aware LLM framework that integrates structured EHR encoders through prompt tuning without modifying underlying architectures. |
| Outcome: | Experiments on MIMIC-III and MIMIC IV show that RePrompT outperforms both EHR-based and LLM-based baselines across multiple clinical prediction tasks. |