Characterizing database granularity using SNOMED-CT hierarchy.
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:
17
6
2021
Statut:
epublish
Résumé
Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.
Types de publication
Journal Article
Observational Study
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
983-992Subventions
Organisme : NLM NIH HHS
ID : R01 LM006910
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007079
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM009886
Pays : United States
Informations de copyright
©2020 AMIA - All rights reserved.
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