ArticleBMC medical informatics and decision making2018
Comparison of MetaMap and cTAKES for entity extraction in clinical notes.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
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- Development and evaluation of machine learning models for the detection of emergency department patients with opioid misuse from clinical notes.JAMIA open · 2026Article
- Characterization and comparison of structured and unstructured electronic health record data mapped to MedDRA for post-marketing surveillance.JAMIA open · 2026Article
- Scalable information extraction from free text electronic health records using large language models.BMC medical research methodology · 2025Article
- Computational phenotypes for patients with opioid-related disorders presenting to the emergency department.PloS one · 2023Article
- Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership.JMIR formative research · 2022Article
- Natural Language Processing in Nephrology.Advances in chronic kidney disease · 2022Review
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- An Investigation of the Representation of Social Determinants of Health in the UMLS.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- A reproducible experimental survey on biomedical sentence similarity: A string-based method sets the state of the art.PloS one · 2022Article
- Clinical concept recognition: Evaluation of existing systems on EHRs.Frontiers in artificial intelligence · 2022Article
- Natural language inference for curation of structured clinical registries from unstructured text.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.JMIR mHealth and uHealth · 2021Article
- Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.Journal of biomedical informatics · 2021Article
- Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.JAMIA open · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical 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.
methodsWe 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.
resultsResults 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.
conclusionsThe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.