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Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/95198, first published .
Doctor in white coat using laptop in modern clinic office

Leveraging Generative Large Language Models for Temporal Relation Extraction From French Clinical Narratives: Prompt-Chaining Approach

Leveraging Generative Large Language Models for Temporal Relation Extraction From French Clinical Narratives: Prompt-Chaining Approach

Nesrine Bannour   1, 2 , PhD ;   Guillaume Assié   3, 4 , MD, PhD ;   Anne Sophie Jannot   5, 6 , MD, PhD ;   Olivia Boyer   7 , MD, PhD ;   Nicolas Garcelon   1, 2 , PhD ;   Xavier Tannier   8 , PhD ;   Marc Vincent   1, 2 , PhD

1 Clinical Bioinformatics Laboratory, INSERM UMR 1163, Imagine Institute, Université Paris Cité, Paris, France

2 Université Paris-Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, Paris, France

3 Université Paris Cité, CNRS, Inserm, Institut Cochin, Paris, France

4 Department of Endocrinology and National Reference Center for Rare Adrenal Disorders, AP-HP, Hôpital Cochin, Paris, France

5 Banque Nationale de Données de Maladies Rares, Assistance Publique – Hôpitaux de Paris, Paris, France

6 UMRS 1346 – HEKA, Université Paris Cité, Inserm, Inria, Paris, France

7 Néphrologie Pédiatrique, Centre de Référence MARHEA, Hôpital Universitaire Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (APHP), Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, France

8 Sorbonne Université, Université Sorbonne Paris-Nord, Inserm, Limics, Paris, France

Corresponding Author:

  • Nesrine Bannour, PhD
  • Université Paris-Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163
  • 24 Bd du Montparnasse
  • Paris 75015
  • France
  • Email: nesrine.bannour@institutimagine.org