Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.
Journal
AMIA ... Annual Symposium proceedings. AMIA Symposium
ISSN: 1942-597X
Titre abrégé: AMIA Annu Symp Proc
Pays: United States
ID NLM: 101209213
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
3
5
2021
pubmed:
4
5
2021
medline:
14
7
2021
Statut:
epublish
Résumé
The normalization of clinical documents is essential for health information management with the enormous amount of clinical documentation generated each year. The LOINC Document Ontology (DO) is a universal clinical document standard in a hierarchical structure. The objective of this study is to investigate the feasibility and generalizability of LOINC DO by mapping from clinical note titles across five institutions to five DO axes. We first developed an annotation framework based on the definition of LOINC DO axes and manually mapped 4,000 titles. Then we introduced a pre-trained deep learning model named Bidirectional Encoder Representations from Transformers (BERT) to enable automatic mapping from titles to LOINC DO axes. The results showed that the BERT-based automatic mapping achieved improved performance compared with the baseline model. By analyzing both manual annotations and predicted results, ambiguities in LOINC DO axes definition were discussed.
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1441-1450Subventions
Organisme : NCATS NIH HHS
ID : U01 TR002062
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA194215
Pays : United States
Informations de copyright
©2020 AMIA - All rights reserved.
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