List of Apache cTAKES Customers
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Since 2010, our global team of researchers has been studying Apache cTAKES customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Apache cTAKES for Natural Language Processing from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Apache cTAKES for Natural Language Processing include: Mayo Clinic, a United States based Healthcare organisation with 80221 employees and revenues of $17.90 billion, Vanderbilt University Medical Center, a United States based Healthcare organisation with 43000 employees and revenues of $10.00 billion, Boston Children’s Hospital, a United States based Healthcare organisation with 15422 employees and revenues of $2.49 billion and many others.
Contact us if you need a completed and verified list of companies using Apache cTAKES, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Boston Children’s Hospital | Healthcare | 15422 | $2.5B | United States | Apache Software | Apache cTAKES | Natural Language Processing | 2019 | n/a |
In 2019, Boston Children’s Hospital deployed Apache cTAKES to construct a scalable, containerized cTAKES-based NLP pipeline supporting the clinical/phenotyping process area. The implementation used Apache cTAKES to extract clinical concepts from EHR free text, with a focus on enabling biobank participant phenotyping and clinical research workflows in the United States.
The deployment was architected as a scalable, containerized pipeline that ingested clinical notes, executed concept extraction and normalization using Apache cTAKES components, and produced indexed NLP-derived data for researcher access. The implementation processed over 1.2 million notes and extracted approximately 154 million concepts, delivering high-throughput phenotyping capabilities and searchable NLP-derived datasets to clinical research teams engaged in biobanking and phenotyping.
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Mayo Clinic | Healthcare | 80221 | $17.9B | United States | Apache Software | Apache cTAKES | Natural Language Processing | 2006 | n/a |
In 2006, Mayo Clinic developed and deployed Apache cTAKES within its clinical data management and natural language processing program in the clinical/healthcare process area. Apache cTAKES was built to extract structured clinical concepts from unstructured electronic health record notes and to support clinical research and decision support workflows across the institution in the United States. The project processed tens of millions of clinical notes, reported as over 80 million, and was put into production as an integral component of Mayo Clinic infrastructure. This deployment established Apache cTAKES as a core clinical NLP capability for research and operational use.
Apache cTAKES was implemented with standard clinical NLP pipelines, including sentence and token processing, clinical concept extraction, named entity recognition, and assertion and negation detection consistent with clinical natural language processing practices. Configuration focused on mapping extracted concepts to clinical terminologies and structuring note content for downstream analysis, enabling the application to produce normalized, queryable outputs for research cohorts and decision support use cases. The narrative restates Apache cTAKES to reflect its role as the application driving these capabilities in the clinical/healthcare process area.
Operational integration centered on ingesting EHR note repositories and routing normalized outputs into research data stores and decision support layers, without naming specific vendor systems. The implementation covered Mayo Clinic research and clinical teams that consume structured concept data for cohort identification, phenotype development, and operational decision support, maintaining production throughput at large scale. Governance was coordinated through clinical data management and NLP program owners who operationalized monitoring, pipeline management, and quality controls to sustain the production service.
The Mayo Clinic deployment of Apache cTAKES in 2006 demonstrated large scale clinical NLP outcomes for research and operational use and became a persistent production asset within their infrastructure. The program emphasized continuous pipeline operation and integration with clinical and research data workflows in the clinical/healthcare process area, positioning Apache cTAKES as a foundational element for clinical concept extraction across Mayo Clinic.
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Vanderbilt University Medical Center | Healthcare | 43000 | $10.0B | United States | Apache Software | Apache cTAKES | Natural Language Processing | 2011 | n/a |
In 2011, Vanderbilt University Medical Center evaluated and adapted Apache cTAKES for smoking status detection on its EMR within the clinical/research process area in the United States. The work focused on embedding the Apache cTAKES smoking status detection module into clinical research and cohort identification workflows to surface tobacco use attributes for downstream research use.
The implementation centered on the smoking status detection module with modest customizations, including targeted note filtering, creation of new annotated training data, and addition of rule-based post processing. These adjustments were applied to the cTAKES pipeline configuration and classifier training, with iterative tuning to align extraction and classification to Vanderbilt note styles and documentation conventions.
Integration activity connected Apache cTAKES outputs into the institutional EMR note ingestion and NLP processing pipeline so that extracted smoking status attributes could be consumed by clinical research teams and cohort identification processes. Deployment used a local cTAKES instance integrated into existing NLP infrastructure, supporting staged processing from note filtering through extraction and rule application.
Governance included a controlled annotation program, iterative evaluation cycles, and transportability testing across Vanderbilt datasets to manage classifier updates and rule changes. The effort demonstrated that modest customization substantially improved classifier F measures when transporting the Apache cTAKES module to Vanderbilt data, validating an approach for adapting the application within the clinical/research process area.
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