A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports
This study introduces the clinical event relative timeline extraction task for PubMed case reports, aiming to convert unstructured text into temporally ordered event sequences annotated with relative temporal relations—enabling patient trajectory modeling, causal reasoning, and process prediction. Methodologically, we establish the first medical-domain relative timeline annotation guideline and design a multi-LLM consistency evaluation framework to create a new benchmark; zero-shot event identification and relative ordering are performed using large language models (e.g., O1-preview), followed by human verification and cross-model consistency analysis. Experiments achieve 0.80 event recall and 0.95 temporal ordering accuracy on real-world case reports, demonstrating high-fidelity temporal structuring. Our core contributions include: (1) formal definition of a novel NLP task in clinical text understanding; (2) construction of the first domain-specific annotation schema and evaluation framework for relative timelines; and (3) empirical validation of LLMs’ effectiveness in medical relative temporal relation extraction.