From annotation to adaptation: extracting temporal relations in French clinical narratives

Extracting temporal information from unstructured clinical narratives is a foundational step toward automated patient timeline generation, a capability that has been proposed as having potential for rare disease diagnosis and care coordination, though prospective clinical validation remains future work. We present a comprehensive framework for temporal relation extraction from French clinical text, addressing a critical gap in non-English clinical NLP resources. We developed specialized annotation guidelines tailored to French medical language and created an annotated corpus of 490 clinical reports from Necker Hospital with 12,464 entity-relation pairs, achieving strong inter-annotator agreement (F1 ≥ 0.94 for core entities). Our comparative evaluation of modern AI approaches-including transformer-based models, large language models, and parameter-efficient fine-tuning (PEFT)-demonstrates that PEFT with CamemBERT-bio-base achieves the strongest temporal relation extraction performance (F1=0.82-0.87 for major relation types), significantly outperforming traditional approaches and matching few-shot large language models with greater computational efficiency. Entity consolidation substantially improves named entity recognition across all methods (DATE F1=0.96). This work provides validated temporal relation extraction methods as a technical foundation for future patient timeline generation systems. We discuss the pathway toward clinical integration, including deployment requirements, governance considerations, and the prospective validation studies needed to confirm clinical utility-particularly for rare genetic disease populations where automated temporal pattern recognition could support earlier diagnosis.