Qualitative analysis of manual annotations of clinical text with SNOMED CT.
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0209547 |
| _version_ | 1818678213169119232 |
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| author | Jose Antonio Miñarro-Giménez Catalina Martínez-Costa Daniel Karlsson Stefan Schulz Kirstine Rosenbeck Gøeg |
| author_facet | Jose Antonio Miñarro-Giménez Catalina Martínez-Costa Daniel Karlsson Stefan Schulz Kirstine Rosenbeck Gøeg |
| author_sort | Jose Antonio Miñarro-Giménez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T09:11:41Z |
| format | Article |
| id | doaj.art-79c4c2e91f3d4652a37b98a292a0dc48 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T09:11:41Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-79c4c2e91f3d4652a37b98a292a0dc482022-12-21T21:55:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011312e020954710.1371/journal.pone.0209547Qualitative analysis of manual annotations of clinical text with SNOMED CT.Jose Antonio Miñarro-GiménezCatalina Martínez-CostaDaniel KarlssonStefan SchulzKirstine Rosenbeck GøegSNOMED 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.https://doi.org/10.1371/journal.pone.0209547 |
| spellingShingle | Jose Antonio Miñarro-Giménez Catalina Martínez-Costa Daniel Karlsson Stefan Schulz Kirstine Rosenbeck Gøeg Qualitative analysis of manual annotations of clinical text with SNOMED CT. PLoS ONE |
| title | Qualitative analysis of manual annotations of clinical text with SNOMED CT. |
| title_full | Qualitative analysis of manual annotations of clinical text with SNOMED CT. |
| title_fullStr | Qualitative analysis of manual annotations of clinical text with SNOMED CT. |
| title_full_unstemmed | Qualitative analysis of manual annotations of clinical text with SNOMED CT. |
| title_short | Qualitative analysis of manual annotations of clinical text with SNOMED CT. |
| title_sort | qualitative analysis of manual annotations of clinical text with snomed ct |
| url | https://doi.org/10.1371/journal.pone.0209547 |
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