Summary
Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source Natural Language Processing (NLP) system that extracts clinical information from electronic health record unstructured text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, context (family history of, current, unrelated to patient), and negated/not negated.[1]
 | |
| Developer(s) | Apache Software Foundation |
|---|---|
| Stable release | 4.0.0.1
/ January 20, 2021 |
| Repository | cTakes Repository |
| Written in | Java, Scala |
| Operating system | Cross-platform |
| Type | Natural language processing, Bioinformatics, Text mining, Information Extraction |
| License | Apache License 2.0 |
| Website | ctakes |
cTAKES was built using the UIMA Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit.[2][3]
Components edit
Components of cTAKES are specifically trained for the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems and clinical research.[4]
These components include:
- Named Section identifier
- Sentence boundary detector
- Rule-based tokenizer
- Formatted list identifier
- Normalizer
- Context dependent tokenizer
- Part-of-speech tagger
- Phrasal chunker
- Dictionary lookup annotator
- Context annotator
- Negation detector
- Uncertainty detector
- Subject detector
- Dependency parser
- patient smoking status identifier
- Drug mention annotator
History edit
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.[5]
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:[5]
- 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.[5]
In 2010, cTAKES was adopted by the i2b2 program and is a central component of the SHARP Area 4.[5]
In 2013, cTAKES released their first release as an Apache incubator project: cTAKES 3.0.[citation needed]
In March 2013, cTAKES became an Apache Top Level Project (TLP).[5]
See also edit
References edit
- ^ Denecke, Kerstin (2015-08-31). "Tools and Resources for Information Extraction". Health Web Science: Social Media Data for Healthcare. Springer. p. 67. ISBN 978-3-319-20582-3 – via Google Books.
- ^ Khalifa, Abdulrahman; Meystre, Stéphane (2015-12-01). "Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes". Journal of Biomedical Informatics. Proceedings of the 2014 i2b2/UTHealth Shared-Tasks and Workshop on Challenges in Natural Language Processing for Clinical Data. 58 (Supplement): S128–S132. doi:10.1016/j.jbi.2015.08.002. PMC 4983192. PMID 26318122.
- ^ Khudairi, Sally (2017-04-25). "The Apache Software Foundation Announces Apache® cTAKES™ v4.0" (Press release). Forest Hill, MD: The Apache Software Foundation. Globe Newswire. Retrieved 2017-09-20.
- ^ Savova, Guergana K; Masanz, James J; Ogren, Philip V; Zheng, Jiaping; Sohn, Sunghwan; Kipper-Schuler, Karin C; Chute, Christopher G (2010). "Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications". Journal of the American Medical Informatics Association. 17 (5): 507–513. doi:10.1136/jamia.2009.001560. ISSN 1067-5027. PMC 2995668. PMID 20819853.
- ^ a b c d e "History". Apache cTAKES™ - clinical Text Analysis Knowledge Extraction System. 2015-06-22. Retrieved 2018-01-11.
External links edit
- cTAKES Official Website
- Apache cTAKES Project Information page from ASF
- Abstract (JAMIA)
- Open Health Natural Language Processing (OHNLP) Consortium
- Strategic Health IT Advanced Research Projects (SHARP) Program
- SHARP Area 4 - Secondary Use of EHR Data
- The Automated Retrieval Console (ARC)
- 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 Informatics for Integrating Biology and the Bedside.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- (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.
- (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.