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

Carole Faviez; Marc Vincent; Nicolas Garcelon; Caroline Michot; Genevieve Baujat; Valerie Cormier-Daire; Sophie Saunier; Xiaoyi Chen; Anita Burgun

doi:10.3233/SHTI220604

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

Carole Faviez
, Marc Vincent
, Nicolas Garcelon
, Caroline Michot
, Genevieve Baujat
, Valerie Cormier-Daire
, Sophie Saunier
, Xiaoyi Chen
, Anita Burgun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

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.

Original language	English (US)
Title of host publication	Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
Editors	Brigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
Publisher	IOS Press BV
Pages	844-848
Number of pages	5
ISBN (Electronic)	9781643682846
DOIs	https://doi.org/10.3233/SHTI220604
State	Published - May 25 2022
Externally published	Yes
Event	32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France Duration: May 27 2022 → May 30 2022

Publication series

Name	Studies in Health Technology and Informatics
Volume	294
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	32nd Medical Informatics Europe Conference, MIE 2022
Country/Territory	France
City	Nice
Period	5/27/22 → 5/30/22

Bibliographical note

Publisher Copyright:
© 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

Keywords

deep phenotyping
electronic health records
Named entity recognition
rare disease

Publisher link

10.3233/SHTI220604

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Faviez, C., Vincent, M., Garcelon, N., Michot, C., Baujat, G., Cormier-Daire, V., Saunier, S., Chen, X., & Burgun, A. (2022). Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome. In B. Seroussi, P. Weber, F. Dhombres, C. Grouin, J.-D. Liebe, J.-D. Liebe, J.-D. Liebe, S. Pelayo, A. Pinna, B. Rance, B. Rance, L. Sacchi, A. Ugon, A. Ugon, A. Benis, & P. Gallos (Eds.), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022 (pp. 844-848). (Studies in Health Technology and Informatics; Vol. 294). IOS Press BV. https://doi.org/10.3233/SHTI220604

Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome. / Faviez, Carole; Vincent, Marc; Garcelon, Nicolas et al.
Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. ed. / Brigitte Seroussi; Patrick Weber; Ferdinand Dhombres; Cyril Grouin; Jan-David Liebe; Jan-David Liebe; Jan-David Liebe; Sylvia Pelayo; Andrea Pinna; Bastien Rance; Bastien Rance; Lucia Sacchi; Adrien Ugon; Adrien Ugon; Arriel Benis; Parisis Gallos. IOS Press BV, 2022. p. 844-848 (Studies in Health Technology and Informatics; Vol. 294).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Faviez, C, Vincent, M, Garcelon, N, Michot, C, Baujat, G, Cormier-Daire, V, Saunier, S, Chen, X & Burgun, A 2022, Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome. in B Seroussi, P Weber, F Dhombres, C Grouin, J-D Liebe, J-D Liebe, J-D Liebe, S Pelayo, A Pinna, B Rance, B Rance, L Sacchi, A Ugon, A Ugon, A Benis & P Gallos (eds), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. Studies in Health Technology and Informatics, vol. 294, IOS Press BV, pp. 844-848, 32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 5/27/22. https://doi.org/10.3233/SHTI220604

Faviez C, Vincent M, Garcelon N, Michot C, Baujat G, Cormier-Daire V et al. Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome. In Seroussi B, Weber P, Dhombres F, Grouin C, Liebe JD, Liebe JD, Liebe JD, Pelayo S, Pinna A, Rance B, Rance B, Sacchi L, Ugon A, Ugon A, Benis A, Gallos P, editors, Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. IOS Press BV. 2022. p. 844-848. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220604

Faviez, Carole ; Vincent, Marc ; Garcelon, Nicolas et al. / Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning : Evaluation on Jeune Syndrome. Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. editor / Brigitte Seroussi ; Patrick Weber ; Ferdinand Dhombres ; Cyril Grouin ; Jan-David Liebe ; Jan-David Liebe ; Jan-David Liebe ; Sylvia Pelayo ; Andrea Pinna ; Bastien Rance ; Bastien Rance ; Lucia Sacchi ; Adrien Ugon ; Adrien Ugon ; Arriel Benis ; Parisis Gallos. IOS Press BV, 2022. pp. 844-848 (Studies in Health Technology and Informatics).

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

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