The Apache Software Foundation Announces Apache® Ctakes™ V4.0
Source: The Apache Software Foundation April 25, 2017 06:00 ET The Apache Software Foundation Announces Apache® cTAKES™ v4.0 Widely adopted Open Source biomedical data extraction, annotation, and clinical information management platform now faster and easier to use. Forest Hill, MD, April 25, 2017 (GLOBE NEWSWIRE) -- The Apache Software Foundation (ASF), the all- volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today the availability of Apache® cTAKES™ v4.0, the latest version of the Open Source natural language processing system for information extraction from health-related free-text. Apache cTAKES (clinical Text Analysis Knowledge Extraction System) is a natural-language processing based information extraction platform for health-related text that identifies signals important for the biomedical domain including types of clinical named entities mapped to various biomedical terminologies/ontologies such as the Unified Medical Language System (UMLS) -- drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures along with their associated attributes such as negation, uncertainty, and more. "Apache cTAKES has helped considerably advance biomedical data extraction and clinical information management over the last several years," said Pei Chen, Vice President of Apache cTAKES. "We are proud to lead the development of a widely adopted, interoperable, community-driven solution for clinical decision support systems and clinical research. The improvements in v4 makes cTAKES easier to use, thereby benefiting the greater medical community." cTAKES originated in 2006 by a team of physicians, computer scientists, and software engineers at Mayo Clinic, and was submitted to the Apache Incubator in June 2012. cTAKES was built using the Apache UIMA (Unstructured Information Management Architecture) framework and Apache OpenNLP machine-learning based toolkit for the processing of health-related natural language text.
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