Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries.

ABSTRACT:

Objective

SNOMED CT provides a standardized terminology for clinical concepts, allowing cohort queries over heterogeneous clinical data including Electronic Health Records (EHRs). While it is intuitive that missing and inaccurate subtype (or is-a) relations in SNOMED CT reduce the recall and precision of cohort queries, the extent of these impacts has not been formally assessed. This study fills this gap by developing quantitative metrics to measure these impacts and performing statistical analysis on their significance.

Material and methods

We used the Optum de-identified COVID-19 Electronic Health Record dataset. We defined micro-averaged and macro-averaged recall and precision metrics to assess the impact of missing and inaccurate is-a relations on cohort queries. Both pr

SUBMITTER: Hao X 

PROVIDER: S-EPMC11648736 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature