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Discovering body site and severity modifiers in clinical texts

Discovering body site and severity modifiers in clinical texts

http://www.wikidata.org/entity/Q28658622

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Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association.Classification of CT pulmonary angiography reports by presence, chronicity, and location of pulmonary embolism with natural language processing.Development and validation of a classification approach for extracting severity automatically from electronic health records Automatic Extraction and Post-coordination of Spatial Relations in Consumer Language.Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.An information model for computable cancer phenotypes.Multilayered temporal modeling for the clinical domain Towards generalizable entity-centric clinical coreference resolution.Automatic health record review to help prioritize gravely ill Social Security disability applicants.Using automatically extracted information from mammography reports for decision-support.Detecting Evidence of Intra-abdominal Surgical Site Infections from Radiology Reports Using Natural Language Processing.Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation.

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Discovering body site and severity modifiers in clinical texts

http://www.wikidata.org/entity/Q28658622

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Discovering body site and severity modifiers in clinical texts

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Discovering body site and severity modifiers in clinical texts

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Discovering body site and severity modifiers in clinical texts

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Discovering body site and severity modifiers in clinical texts

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Discovering body site and severity modifiers in clinical texts

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AMIAJNL-2013-001766

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Journal of the American Medical Informatics Association

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Discovering body site and severity modifiers in clinical texts

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Dmitriy Dligach

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Guergana K Savova

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Lee Becker

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Steven Bethard

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Timothy Miller

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P2860

Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project

Automatic extraction of relations between medical concepts in clinical texts

The MiPACQ clinical question answering system

caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research

Exploring semantic groups through visual approaches.

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

A broad-coverage natural language processing system.

Automatic Extraction of ICD-O-3 Primary Sites from Cancer Pathology Reports.

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448-54

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scholarly article

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10.1136/AMIAJNL-2013-001766

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