Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
Open accessAbstract
We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies-the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.
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Dictionary- and rule-based annotation systems are often limited in their report coverage and generalizability across institutions.
“While analysis tools [12-14] have been developed to extract key clinical concepts and their attributes from biomedical text and convert them into a structured format, such dictionary- and rule-based annotation systems are often limited in their report coverage and generalizability across institutions.”
From RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (2021) · cites this paper as [12]
The Remaining challenges section says cTAKES’ dictionary-based recognition misses complex synonyms, supporting a limitation in concept coverage. It does not establish that such systems are often limited in report coverage or generalizability across institutions; the Global evaluation and applications section instead highlights cTAKES’ portability to data from other institutions. The copy provides no page numbers for these passages. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn the work by Afshar et al., clinical concepts were extracted from all available notes within each patient encounter using cTAKES.
“In the work by Afshar et al.50, clinical concepts, such as diseases, symptoms, anatomical sites, medications, and procedures, were extracted from all available notes within each patient encounter using the NLP engine, clinical Text And Knowledge Extraction System (cTAKES)86.”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [86]
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Gilani, K., Verket, M., Peters, C., Dumontier, M., Rocca, H.-P. B.-L., & Urovi, V. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110745 · Cited by 5 · see citing works
Mobility functional status ascertainment in electronic health records using large language models
Liu, X., Garg, M., Jia, H., Sauver, J. S., Pagali, S. R., & Sohn, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-37025-9 · Cited by 2 · see citing works
“A limitation of this study is the lack of a direct comparison with conventional clinical NLP systems like cTAKES 27 or CLAMP 28 .”
Self-citationMoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling
Gao, J., Rahman, M., Caskey, J., Oguss, M., O’Rourke, A., Brown, R., Stey, A., Mayampurath, A., Churpek, M. M., Chen, G., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02219-4 · Cited by 2 · see citing works
“In the work by Afshar et al. 50 , clinical concepts, such as diseases, symptoms, anatomical sites, medications, and procedures, were extracted fromallavailablenoteswithineachpatientencounterusingtheNLPengine, clinicalTextAndKnowledgeExtractionSystem(cTAKES) 86 .”
- Authors
- Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G.
- Affiliations
- Mayo Clinic (US)
- University of Colorado Denver (US)
- Corresponding author
- Savova, Guergana K
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-09
- Volume
- 17
- Issue
- 5
- Pages
- 507-513
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC2995668
- OpenAlex
- W2146089916
- Cited by
- 1,967
- References
- 53
- Citations by year
- 2026: 89 · 2025: 99 · 2024: 103 · 2023: 139 · 2022: 120 · 2021: 169 · 2020: 192 · 2019: 202 · 2018: 135 · 2017: 129 · 2016: 118 · 2015: 130 · 2014: 108 · 2013: 118 · 2012: 70
- FWCI
- 30.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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 › Natural Language Processing Techniques
- Keywords
- cTAKES, clinical text mining, clinical information extraction, electronic health records, clinical text, OpenNLP, sentence segmentation, tokenization, part-of-speech tagging, shallow parsing, named entity recognition, concept mapping, negation detection, semantic annotation
- MeSH terms
- Natural Language Processing, Information Storage and Retrieval, Biomedical Research, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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