Enhancing rare disease detection with deep phenotyping from EHR narratives: evaluation on Jeune syndrome
AI-based screening combined with high-quality deep phenotyping can help reduce diagnostic delay in rare diseases. The completeness and accuracy of phenotyping from EHRs have a strong impact on screening performances.
Authors
- Valérie Cormier‐Daire (ORCID: https://orcid.org/0000-0002-2839-9856)
- Carole Faviez (ORCID: https://orcid.org/0000-0002-1500-0236)
- Nicolas Garcelon (ORCID: https://orcid.org/0000-0002-3326-2811)
- Sophie Saunier (ORCID: https://orcid.org/0000-0002-1069-0047)
- Marc Vincent (ORCID: https://orcid.org/0000-0002-0985-4550)
- Anita Burgun (ORCID: https://orcid.org/0000-0001-6855-4366)
- Xiaomeng Wang (ORCID: https://orcid.org/0000-0001-5436-5003)
- Xiaoyi Chen (ORCID: https://orcid.org/0000-0002-7378-5158)
Institutions
- Délégation Paris 5 (FR)
- Hôpital Necker-Enfants Malades (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Institut Necker Enfants Malades (FR)
- Sorbonne Paris Cité (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Institut des Maladies Génétiques Imagine (FR)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-06-21
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.106021
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Agence Nationale de la Recherche
- HORIZON EUROPE Framework Programme
- HORIZON EUROPE Health