The Yale cTAKES extensions for document classification: architecture and application

Garla, Vijay; Re, Vincent Lo; Dorey-Stein, Z.; Kidwai, Farah; Scotch, Matthew; Womack, Julie A.; Justice, Amy C.; Brandt, Cynthia

doi:10.1136/amiajnl-2011-000093

Cited by 82 publications

(57 citation statements)

References 23 publications

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“…All the text processing and analysis utilized the cTAKES (version 3.0) which stores the analysis results in the UIMA Common Analysis Structure (CAS) [18]. The cTAKES outputs CAS in XML representation.…”

Section: Methodology and Materialsmentioning

confidence: 99%

An entry generation pipeline for converting free-text medical document into Clinical Document Architecture document with entry-level

Lin¹,

Lai²,

Lee³

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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The Clinical Document Architecture (CDA) is a widely adopted international standard for clinical documents design; the CDA document with coded entries increases the semantic interoperability in the course of clinical document exchange. This study developed a CDA entry generation pipeline for converting free-text documents into CDA documents with entry-level. The results showed that CDA entry coding could be accomplished using natural language processing.

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Section: Methodology and Materialsmentioning

confidence: 99%

An entry generation pipeline for converting free-text medical document into Clinical Document Architecture document with entry-level

Lin¹,

Lai²,

Lee³

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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“…They concluded that more advanced abbreviation recognition modules are necessary. Osborne et al [24] applied MetaMap and YTEX [25], which is an extension of cTAKES to two concept recognition tasks. Their results suggested that YTEX would be a better system for "off the shelf" concept mapping.…”

Section: Diagnostic Knowledge Extraction Using Metamap and Ctakesmentioning

confidence: 99%

Extracting Diagnostic Knowledge from MedLine Plus: A Comparison between MetaMap and cTAKES Approaches

Rodríguez‐González

Costumero

Martínez-Romero

et al. 2018

CBIO

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Abstract:The development of diagnostic decision support systems (DDSS) requires having a reliable and consistent knowledge base about diseases and their symptoms, signs and diagnostic tests. Physicians are typically the source of this knowledge, but it is not always possible to obtain all the desired information from them. Other valuable sources are medical books and articles describing the diagnosis of diseases, but again, extracting this information is a hard and timeconsuming task. In this paper we present the results of our research, in which we have used Web scraping, natural language processing techniques, a variety of publicly available sources of diagnostic knowledge and two widely known medical concept identifiers, MetaMap and cTAKES, to extract diagnostic criteria for infectious diseases from MedLine Plus articles. A performance comparison of MetaMap and cTAKES is also presented.

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“…Fields include temporal, anatomic, and certainty modifiers for each finding (Fig 7), whose values can be obtained from existing terminologies (eg, the UMLS Metathesaurus) and custom-built dictionaries developed for specific tasks. The opensource NLP package cTAKES similarly relies on significant linguistic components (19,20) in conjunction with statistical and machine learning approaches (discussed in the following section).…”

Section: Linguistic Approachmentioning

confidence: 99%

Natural Language Processing Technologies in Radiology Research and Clinical Applications

et al. 2016

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The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications. After completing this journal-based SA-CME activity, participants will be able to:■ Describe the set of technologies that compose present-day natural language processing in radiology.■ List examples of how these technologies have been combined to achieve specific objectives in radiology research and, potentially, clinical practice.■ Discuss current capabilities and possible future applications of use of natural language processing in radiology.

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The Yale cTAKES extensions for document classification: architecture and application

Abstract: The F(1)-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at http://code.google.com/p/ytex.

Cited by 82 publications

References 23 publications

An entry generation pipeline for converting free-text medical document into Clinical Document Architecture document with entry-level

An entry generation pipeline for converting free-text medical document into Clinical Document Architecture document with entry-level

Extracting Diagnostic Knowledge from MedLine Plus: A Comparison between MetaMap and cTAKES Approaches

Natural Language Processing Technologies in Radiology Research and Clinical Applications

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