PHEONA introduces a standardized evaluation framework for assessing how Large Language Models can revolutionize computational phenotyping in healthcare research.

• Creates a structured methodology to compare LLM approaches for extracting patient conditions from clinical data
• Focuses on practical application for Acute Respiratory Failure (ARF) therapies
• Addresses the labor-intensive challenge of manual data review in traditional phenotyping
• Establishes benchmarks for future research on AI-assisted phenotyping

This research matters because it helps medical researchers systematically evaluate how AI can accelerate patient classification for clinical studies, potentially reducing the time and resources needed for biomedical research while maintaining accuracy.

PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping