Apache cTAKES

Development of cTAKES began at the Mayo Clinic in 2006. The development team, led by Dr. Guergana Savova and Dr. Christopher Chute, included physicians, computer scientists and software engineers. After its deployment, cTAKES became an integral part of Mayo's clinical data management infrastructure, processing more than 80 million clinical notes.{{cite web |date=2015-06-22 |title=History |website=Apache cTAKES™ - clinical Text Analysis Knowledge Extraction System |url=http://ctakes.apache.org/history.html |access-date=2018-01-11 }}

When Dr. Savova's moved to Boston Children's Hospital in early 2010, the core development team grew to include members there. Further external collaborations include:

• University of Colorado
• Brandeis University
• University of Pittsburgh
• University of California at San Diego

Such collaborations have extended cTAKES' capabilities into other areas such as Temporal Reasoning, Clinical Question Answering, and coreference resolution for the clinical domain.

In 2010, cTAKES was adopted by the [http://www.i2b2.org i2b2] program and is a central component of the [https://web.archive.org/web/20170430025922/https://www.healthit.gov/policy-researchers-implementers/secondary-use-ehr-data SHARP Area 4].

In 2013, cTAKES released their first release as an Apache Software Foundation incubator project: [http://incubator.apache.org/ctakes/ cTAKES 3.0].{{citation needed|date=July 2020}}

In March 2013, cTAKES became an Apache Software Foundation Top Level Project (TLP).

• OpenNLP
• UIMA
• Electronic Health Record
• Unified Medical Language System

{{Reflist}}

• [https://github.com/apache/ctakes?tab=readme-ov-file#apache-ctakes cTAKES Official Website]
• [https://projects.apache.org/project.html?ctakes Apache cTAKES Project Information page] from ASF
• [http://jamia.bmj.com/content/17/5/507.abstract Abstract (JAMIA)]
• [http://ohnlp.org/ Open Health Natural Language Processing (OHNLP) Consortium]
• [https://web.archive.org/web/20111007234355/http://healthit.hhs.gov/portal/server.pt?open=512&mode=2&objID=3128&PageID=20708 Strategic Health IT Advanced Research Projects (SHARP) Program]
• [http://sharpn.org/ SHARP Area 4 - Secondary Use of EHR Data]
• [https://web.archive.org/web/20130712065633/http://maveric.org/mig/arc.html The Automated Retrieval Console (ARC)]
• [https://www.i2b2.org/software/projects/hitex/hitex_manual.html Health Information Text Extraction (HITEx)]) was developed as part of the i2b2 project. It is a rule-based NLP pipeline based on the GATE framework developed by [http://www.i2b2.org Informatics for Integrating Biology and the Bedside].
• [http://code.google.com/p/cleartk/ Computational Language and Education Research toolkit (cleartk)] (No longer maintained) has been developed at the University of Colorado at Boulder, and provides a framework for developing statistical NLP components in Java. It is built on top of Apache UIMA.
• [http://code.google.com/p/negex/ NegEx] - is a tool developed at the University of Pittsburgh to detect negated terms from clinical text. The system utilizes trigger terms as a method to determine likely negation scenarios within a sentence.
• [https://web.archive.org/web/20111204211529/http://www.dbmi.pitt.edu/blulab/ConText.html ConText]): an extension to NegEx, and is also developed by the University of Pittsburgh. ConText extends NegEx to not only detect negated concepts, but to also find temporal (recent, historical or hypothetical scenarios) and who the Subject (of experience) is (patient or other).
• [http://metamap.nlm.nih.gov/ MetaMap] (by United States National Library of Medicine): is a comprehensive concept tagging system which is built on top of the Unified Medical Language System. It requires an active UMLS Metathesaurus License Agreement (and account) for use.
• [https://web.archive.org/web/20150913120033/https://knowledgemap.mc.vanderbilt.edu/research/content/medex-tool-finding-medication-information MedEx] - a tool for extraction medication information from clinical text. MedEx processes free-text clinical records to recognize medication names and signature information, such as drug dose, frequency, route, and duration. Use is free with a UMLS license. It is a standalone application for Linux and Windows.
• [https://web.archive.org/web/20150626172745/http://knowledgemap.mc.vanderbilt.edu/research/content/sectag-tagging-clinical-note-section-headers SecTag] (section tagging hierarchy): recognizes note section headers using NLP, Bayesian, spelling correction, and scoring techniques. Use is free with either a UMLS or LOINC license.
• ([http://nlp.stanford.edu/software/CRF-NER.shtml Stanford Named Entity Recognizer (NER)]): Stanford’s NER is a Conditional Random Field sequence model, together with well-engineered features for Named Entity Recognition in English and German.
• ([http://nlp.stanford.edu/software/corenlp.shtml Stanford CoreNLP]) is an integrated suite of natural language processing tools for English in Java, including tokenization, part-of-speech tagging, named entity recognition, parsing, and coreference.

{{Apache Software Foundation}}

{{Health software}}

cTAKES

Category:Electronic health record software

Category:Natural language processing software

Category:Free health care software

Category:Free bioinformatics software