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.
arXiv preprint arXiv:1906.00346 , year=
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UNVERDICTED 4representative citing papers
ScaffoldAgent improves long-form report generation by modeling outline evolution as expansion, contraction, and revision guided by a utility function estimating downstream value.
MedCo builds and text-enriches a medical knowledge graph using LLMs and EHR data, then fuses text and graph signals via joint LoRA-tuned LLaMA and heterogeneous GNN training to improve EHR clinical predictions on MIMIC datasets.
GraD-IBD converts ICD diagnosis trajectories into visit-bucketized directed graphs and applies context-aware time-decay message passing to improve early IBD detection over sequential baselines.
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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ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
ScaffoldAgent improves long-form report generation by modeling outline evolution as expansion, contraction, and revision guided by a utility function estimating downstream value.
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Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation
MedCo builds and text-enriches a medical knowledge graph using LLMs and EHR data, then fuses text and graph signals via joint LoRA-tuned LLaMA and heterogeneous GNN training to improve EHR clinical predictions on MIMIC datasets.
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GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease
GraD-IBD converts ICD diagnosis trajectories into visit-bucketized directed graphs and applies context-aware time-decay message passing to improve early IBD detection over sequential baselines.