Comparison of MetaMap and cTAKES for entity extraction in clinical notes
Open accessAbstract
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.
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Tools like MetaMap must have their parameters tuned, as using them with default settings may result in the extraction of irrelevant terms.
“Tools like MetaMap must also have their parameters tuned, as using them with default settings may result in the extraction of irrelevant terms.”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [29]
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Research works printed but not linked: 1
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Challenges of developing a digital scribe to reduce clinical documentation burden
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- Authors
- Reátegui, R., & Ratté, S.
- Affiliations
- Universidad Técnica Particular de Loja (EC)
- École de Technologie Supérieure (CA)
- Corresponding author
- Reátegui, Ruth
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2018-09
- Volume
- 18
- Issue
- S3
- Pages
- 74-74
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC6157281
- OpenAlex
- W2891469329
- Cited by
- 83
- References
- 21
- Citations by year
- 2026: 8 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 11 · 2021: 19 · 2020: 27 · 2019: 10 · 2018: 1
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Espace ÉTS (ETS) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- MetaMap, cTAKES, clinical entity extraction, clinical notes, discharge summaries, natural language processing, clinical information extraction
- MeSH terms
- Humans, Natural Language Processing, Information Storage and Retrieval, Unified Medical Language System
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