Publication: Qualitative analysis of manual annotations of clinical text with SNOMED CT
Authors
Miñarro Giménez, José Antonio ; Martínez Costa, Catalina ; Karlsson, Daniel ; Schulz, Stefan ; Gøeg, Kristine Rosenbeck
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Publisher
Public Library of Science
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DOI
https://doi.org/10.1371/journal.pone.0209547
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info:eu-repo/semantics/article
Description
© 2018 Miñarro-Gimébez et al. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Published Manuscript version of a Published Work that appeared in final form in PLoS ONE. To access the final edited and published work see https://doi.org/10.1371/journal.pone.0209547
Abstract
SNOMED CT provides about 300,000 codes with fine-grained concept definitions to support interoperability of health data. Coding clinical texts with medical terminologies it is not a trivial task and is prone to disagreements between coders. We conducted a qualitative analysis to identify sources of disagreements on an annotation experiment which used a subset of SNOMED CT with some restrictions. A corpus of 20 English clinical text fragments from diverse origins and languages was annotated independently by two domain medically trained annotators following a specific annotation guideline. By following this guideline, the annotators had to assign sets of SNOMED CT codes to noun phrases, together with concept and term coverage ratings. Then, the annotations were manually examined against a reference standard to determine sources of disagreements. Five categories were identified. In our results, the most frequent cause of inter-annotator disagreement was related to human issues. In several cases disagreements revealed gaps in the annotation guidelines and lack of training of annotators. The reminder issues can be influenced by some SNOMED CT features.
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Citation
PLoS One, 2018, Vol. 13(12) : e0209547
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Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/