cTakes statistics

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Citations per year

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Tool usage distribution map

This map represents all the scientific publications referring to cTakes per scientific context
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Protocols

cTakes specifications

Information


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
Requirements
Oracle Java, Apache Subversion, Apache Maven
Source code URL http://ctakes.apache.org/downloads.cgi
Maintained Yes

Subtool


  • YTEX

Versioning


No version available

Documentation


cTakes citations

 (81)
call_split

Clinical Natural Language Processing in languages other than English: opportunities and challenges

2018
J Biomed Semantics
PMCID: 5877394
PMID: 29602312
DOI: 10.1186/s13326-018-0179-8
call_split See protocol

[…] ng an entirely new system for the new language. This experience suggests that a system that is designed to be as modular as possible, may be more easily adapted to new languages. As a modular system, cTAKES raises interest for adaptation to languages other than English. Initial experiments in Spanish for sentence boundary detection, part-of-speech tagging and chunking yielded promising results []. […]

library_books

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

2018
PLoS One
PMCID: 5813927
PMID: 29447188
DOI: 10.1371/journal.pone.0192360

[…] f phrases or concepts that are most salient for a particular prediction; this list either manifests as an actual list or as highlights of the original text [, ].As a baseline we consider the filtered cTAKES random forest approach (other baselines can be treated equivalently). In the filtered cTAKES approach, clinicians ensure that all features directly pertain to the specific phenotype []. We rank […]

library_books

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

2017
PMCID: 5741828
PMID: 29222076
DOI: 10.2196/medinform.9170

[…] is then measured to find the best fitness class for a sentence. Once we have the labeled sentences as ADEs or No-ADEs, we focus on ADE sentences and find the positive adverse-drug interactions using cTAKES []. […]

library_books

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

2017
BMC Med Inform Decis Mak
PMCID: 5709846
PMID: 29191207
DOI: 10.1186/s12911-017-0556-8

[…] The pipeline was built on cTAKES and python version 2.7.11. The Natural Language Toolkit (‘nltk’) package was used for lexical normalization (word tokenization and stemming process) of bag-of-words features generation, and for […]

library_books

A semantic based workflow for biomedical literature annotation

2017
PMCID: 5691355
PMID: 29220478
DOI: 10.1093/database/bax088

[…] ws 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 clini […]

library_books

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

2017
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 (https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tm […]


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