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cTakes specifications


Unique identifier OMICS_17680
Name cTakes
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java
License Apache License version 2.0
Computer skills Medium
Version 4.0.0
Stability Stable
Oracle Java, Apache Subversion, Apache Maven
Source code URL http://ctakes.apache.org/downloads.cgi
Maintained Yes


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cTakes citations


Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives

PLoS One
PMCID: 5813927
PMID: 29447188
DOI: 10.1371/journal.pone.0192360

[…] and outperform approaches to classification problems in other domains [–]. we compare cnns to entity extraction systems using the mayo clinical text analysis and knowledge extraction system (ctakes) [], and other nlp methods such as logistic regression models using n-gram features. using a corpus of 1,610 discharge summaries that were annotated for ten different phenotypes, we show […]


Medical subdomain classification of clinical notes using a machine learning based natural language processing approach

BMC Med Inform Decis Mak
PMCID: 5709846
PMID: 29191207
DOI: 10.1186/s12911-017-0556-8

[…] and developed medical subdomain classifiers based on the content of the note., we constructed the pipeline using the clinical nlp system, clinical text analysis and knowledge extraction system (ctakes), the unified medical language system (umls) metathesaurus, semantic network, and learning algorithms to extract features from two datasets — clinical notes from integrating data for analysis, […]


A semantic based workflow for biomedical literature annotation

PMCID: 5691355
PMID: 29220478
DOI: 10.1093/database/bax088

[…] users to configure the processing of documents according to their specific objectives and goals, providing very rich and complete information about concepts., the second tool used in this example is ctakes (), an open-source nlp system for information extraction from free text of electronic medical records. the system was designed to semantically extract information to support heterogeneous […]


Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PMCID: 5635478
PMID: 29090077
DOI: 10.1155/2017/7575280

[…] can be referred through umls concept unique identifier (cui) (https://www.nlm.nih.gov). many researchers endeavor to deal with medical ner and normalization by developing computational tools such as ctakes (http://ctakes.apache.org), freeling-med, metamap (https://metamap.nlm.nih.gov), medlee (http://www.medlingmap.org/taxonomy/term/80), tmchem […]


Design of an extensive information representation scheme for clinical narratives

J Biomed Semantics
PMCID: 5594525
PMID: 28893314
DOI: 10.1186/s13326-017-0135-z

[…] actionable way. within the context of the strategic health it advanced research project (sharpn), [] and [] developed a higher-level formal (owl) clinical ehr representation (implemented in ctakes []). this representation is based on the low-level annotation framework explained in []. the sharpn normalized data has been thus converted automatically to the resource description framework […]


Word2Vec inversion and traditional text classifiers for phenotyping lupus

BMC Med Inform Decis Mak
PMCID: 5568290
PMID: 28830409
DOI: 10.1186/s12911-017-0518-1

[…] for assimilating synonyms in order to better represent and annotate textual data []. cuis, when generated through a pipeline such as the clinical text analysis and knowledge extraction system (ctakes), are able to detect negation in order to more accurately engineer relevant features [, ]. a textual note saying a patient does not have lupus would result in the concept for lupus […]

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