Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome
| Autor: | Carole Faviez, Marc Vincent, Nicolas Garcelon, Caroline Michot, Genevieve Baujat, Valerie Cormier-Daire, Sophie Saunier, Xiaoyi Chen, Anita Burgun |
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| Přispěvatelé: | Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laboratoire des Maladies Rénales Héréditaires = Laboratory of Hereditary Kidney Diseases (Equipe Inserm U1163 ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), This work was supported by the French National Research Agency (ANR) under the C’IL-LICO project (ANR-17-RHUS-0002), approved by the French ethics and scientific committee for health research (CESREES) (#2201437), and as part of the 'Investissements d’avenir' (ANR-19-P3IA-0001) (PRAIRIE 3IA Institute)., Garcelon, Nicolas, École pratique des hautes études (EPHE) |
| Rok vydání: | 2022 |
| Předmět: |
MESH: Deep Learning
MESH: Rare Diseases Ellis-Van Creveld Syndrome [SDV]Life Sciences [q-bio] MESH: Algorithms Deep phenotyping Unified Medical Language System [SDV] Life Sciences [q-bio] Named entity recognition [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing Deep Learning Rare Diseases Electronic Health Records Humans MESH: Unified Medical Language System Rare disease MESH: Electronic Health Records Algorithms |
| Zdroj: | Challenges of Trustable AI and Added-Value on Health Challenges of Trustable AI and Added-Value on Health, 294, IOS Press; IOS Press, pp.844-848, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220604⟩ Challenges of Trustable AI and Added-Value on Health, IOS Press, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220604⟩ |
| ISSN: | 1879-8365 |
| DOI: | 10.3233/SHTI220604⟩ |
| Popis: | International audience; The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The phenotype extraction from narrative reports can be performed by using either dictionary-based or data-driven methods. We developed a hybrid pipeline using deep learning to enrich the UMLS Metathesaurus for automatic detection of phenotypes from EHRs. The pipeline was evaluated on a French database of patients with a rare disease characterized by skeletal abnormalities, Jeune syndrome. The results showed a 2.5-fold improvement regarding the number of detected skeletal abnormalities compared to the baseline extraction using the standard release of UMLS. Our method can help enrich the coverage of the UMLS and improve phenotyping, especially for languages other than English. |
| Databáze: | OpenAIRE |
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