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.
Prompting large language models for zero-shot clinical prediction with structured longitudinal electronic health record data
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CURA improves calibration of clinical LM risk predictions by combining individual error alignment with neighborhood-based soft labels without harming discrimination on MIMIC-IV tasks.
A scoping review of 12 studies finds LLM applications for rare disease patient education remain early-stage, dominated by general models like ChatGPT focused on curated question-answering with limited real-world or patient-centered evaluation.
Traj-CoA is a multi-agent LLM framework that sequentially processes noisy five-year EHR data via worker agents into EHRMem for manager-agent lung cancer risk prediction and outperforms four categories of baselines in zero-shot evaluation.
citing papers explorer
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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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CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction
CURA improves calibration of clinical LM risk predictions by combining individual error alignment with neighborhood-based soft labels without harming discrimination on MIMIC-IV tasks.
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An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
A scoping review of 12 studies finds LLM applications for rare disease patient education remain early-stage, dominated by general models like ChatGPT focused on curated question-answering with limited real-world or patient-centered evaluation.
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Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction
Traj-CoA is a multi-agent LLM framework that sequentially processes noisy five-year EHR data via worker agents into EHRMem for manager-agent lung cancer risk prediction and outperforms four categories of baselines in zero-shot evaluation.