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.