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DTSTART:20241103T020000
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DESCRIPTION:A Prompt Library for Efficient Clinical Entity Recognition Usi
 ng Large Language Models by Yang Ren\, PhD Abstract: Large Language Model
 s (LLMs) hold strong potential for clinical information extraction (IE)\,
  but their evaluation is often limited by manually crafted prompts and th
 e need for annotated data. We developed an automated framework that extra
 cts entity-level schema information from published clinical IE studies to
  construct structured prompts. Using literature covering 44 diseases and 
 over 100 entities\, we generated prompts to evaluate multiple LLMs under 
 few-shot and fine-tuned settings. Compared to baselines using generic pro
 mpts\, models prompted with schema-derived information consistently outpe
 rformed across tasks. Our results demonstrate the value of structured pro
 mpting for robust and reproducible LLM evaluation in diverse clinical IE 
 applications.\n\nAdmission:\nFree\n\nDetails URL:\nhttps://medicine.yale.
 edu/event/nlpllm-interest-group-14/\n
DTEND;TZID=America/New_York:20251215T170000
DTSTAMP:20260430T063054Z
DTSTART;TZID=America/New_York:20251215T160000
LOCATION:Zoom: Passcode Required  - Please Email Sooyoun Tan \, URL: https
 ://yale.zoom.us/j/93599941969
SEQUENCE:0
STATUS:Confirmed
SUMMARY:NLP/LLM Interest Group
UID:7c91eeb4-714b-41e2-ae60-9ff0fb6281c7
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