Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome.

Journal: Studies in health technology and informatics
Published Date: May 25, 2022

Abstract

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.

Authors

  • Carole Faviez
    Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université de Paris, Paris, France.
  • Marc Vincent
    Université de Paris, Imagine Institute, Data Science Platform, INSERM UMR 1163, Paris, France.
  • Nicolas Garcelon
    Plateforme data science - institut des maladies génétiques Imagine, Inserm, centre de recherche des Cordeliers, UMR 1138 équipe 22, institut Imagine, Paris-Descartes, université Sorbonne- Paris Cité, Paris, France.
  • Caroline Michot
    Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, INSERM UMR 1163, Imagine Institute, Université de Paris, Paris, France.
  • Genevieve Baujat
    Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, INSERM UMR 1163, Imagine Institute, Université de Paris, Paris, France.
  • Valerie Cormier-Daire
    Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, INSERM UMR 1163, Imagine Institute, Université de Paris, Paris, France.
  • Sophie Saunier
    Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Imagine Institute, Université de Paris, Paris, France.
  • Xiaoyi Chen
    Department of Ultrasound, Shenzhen Children's Hospital of China Medical University, Shenzhen, China.
  • Anita Burgun
    Hôpital Necker-Enfants malades, AP-HP, Paris, France.

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