Reátegui, Ruth et Ratté, Sylvie.
2018.
« Comparison of MetaMap and cTAKES for entity extraction in clinical notes ».
BMC Medical Informatics and Decision Making, vol. 18, nº supp. 3.
Compte des citations dans Scopus : 61.
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Résumé
Background Clinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narrative format. In this context, we aimed to compare the automatic extraction capacity of medical entities using two tools: MetaMap and cTAKES. Methods We worked with i2b2 (Informatics for Integrating Biology to the Bedside) Obesity Challenge data. Two experiments were constructed. In the first one, only one UMLS concept related with the diseases annotated was extracted. In the second, some UMLS concepts were aggregated. Results Results were evaluated with manually annotated medical entities. With the aggregation process the result shows a better improvement. MetaMap had an average of 0.88 in recall, 0.89 in precision, and 0.88 in F-score. With cTAKES, the average of recall, precision and F-score were 0.91, 0.89, and 0.89, respectively. Conclusions The aggregation of concepts (with similar and different semantic types) was shown to be a good strategy for improving the extraction of medical entities, and automatic aggregation could be considered in future works.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Informations complémentaires: | Thématique : Selected articles from the 7th Translational Bioinformatics Conference (TBC 2017): medical informatics and decision making ; Identifiant de l'article: 74 |
| Professeur: | Professeur Ratté, Sylvie |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 16 oct. 2018 19:41 |
| Dernière modification: | 12 juill. 2019 20:03 |
| URI: | https://espace2.etsmtl.ca/id/eprint/17422 |
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