{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T03:44:43Z","timestamp":1707536683950},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/shti220604","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:50Z","timestamp":1653481070000},"source":"Crossref","is-referenced-by-count":1,"title":["Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome"],"prefix":"10.3233","author":[{"given":"Carole","family":"Faviez","sequence":"first","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, Paris, France"},{"name":"Inria Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Vincent","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Paris, Imagine Institute, Data Science Platform, INSERM UMR 1163, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, Paris, France"},{"name":"Inria Paris, France"},{"name":"Universit\u00e9 de Paris, Imagine Institute, Data Science Platform, INSERM UMR 1163, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline","family":"Michot","sequence":"additional","affiliation":[{"name":"Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, INSERM UMR 1163, Imagine Institute, Universit\u00e9 de Paris, Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, Service de g\u00e9n\u00e9tique, AP-HP, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genevieve","family":"Baujat","sequence":"additional","affiliation":[{"name":"Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, INSERM UMR 1163, Imagine Institute, Universit\u00e9 de Paris, Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, Service de g\u00e9n\u00e9tique, AP-HP, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerie","family":"Cormier-Daire","sequence":"additional","affiliation":[{"name":"Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, INSERM UMR 1163, Imagine Institute, Universit\u00e9 de Paris, Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, Service de g\u00e9n\u00e9tique, AP-HP, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"Saunier","sequence":"additional","affiliation":[{"name":"Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Imagine Institute, Universit\u00e9 de Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, Paris, France"},{"name":"Inria Paris, France"},{"name":"Universit\u00e9 de Paris, Imagine Institute, Data Science Platform, INSERM UMR 1163, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anita","family":"Burgun","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, Paris, France"},{"name":"Inria Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, D\u00e9partement d\u2019informatique m\u00e9dicale, AP-HP, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220604","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:51Z","timestamp":1653481071000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220604"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220604","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}