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We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source(More)
BACKGROUND The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help(More)
Negation of clinical named entities is common in clinical documents and is a crucial factor to accurately compile patients' clinical conditions and to further support complex phenotype detection. In 2009, Mayo Clinic released the clinical Text Analysis and Knowledge Extraction System (cTAKES), which includes a negation annotator that identifies negation(More)
In Electronic Health Records (EHRs), much of valuable information regarding patients' conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such(More)
OBJECTIVE To extract physician-asserted drug side effects from electronic medical record clinical narratives. MATERIALS AND METHODS Pattern matching rules were manually developed through examining keywords and expression patterns of side effects to discover an individual side effect and causative drug relationship. A combination of machine learning (C4.5)(More)
This paper describes improvements of and extensions to the Mayo Clinic 2006 smoking status classification system. The new system aims at addressing some of the limitations of the previous one. The performance improvements were mainly achieved through remodeling the negation detection for non-smoker, temporal resolution to distinguish a past and current(More)
OBJECTIVE This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity. MATERIALS AND METHODS The task organizers provided progress notes and discharge summaries that were(More)