caTIES: A Grid Based System for Coding and Retrieval of Surgical Pathology Reports and Tissue Specimens in Support of Translational Research

Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15232, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 05/2010; 17(3):253-64. DOI: 10.1136/jamia.2009.002295
Source: PubMed


The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)--an application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for text-derived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multi-center collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multi-institutional translational research programs.

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Available from: Girish Chavan
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    • "There is an extensive amount of existing work in creating clinical natural language processing (NLP) systems to extract information from free text in specific disease domains. Two salient examples are the Cancer Text Information System (caTIES) [1] and SymText [2]. caTIES has been developed at the University of Pittsburgh to extract coded information from surgical pathology reports using terms from the National Cancer Institute (NCI) Thesaurus . "
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