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Literature DB >> 16779275

A grammar-based classification of negations in clinical radiology reports.

Abstract

We describe the development of a classification scheme of negations based on syntactical categories of negative words, and patterns of negation. Each category contains a negative word or phrase and a pattern to locate negated terms. The classification was derived through literature study and manual inspection of 500 radiology reports of six common imaging modalities.

Entities: Disease

Mesh：

Year: 2005 PMID： 16779275 PMCID： PMC1560512

Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076

3 in total

1. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS.

Authors: P G Mutalik; A Deshpande; P M Nadkarni
Journal: J Am Med Inform Assoc Date: 2001 Nov-Dec Impact factor: 4.497

2. A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports.

Authors: Yang Huang; Henry J Lowe; William R Hersh
Journal: J Am Med Inform Assoc Date: 2003-08-04 Impact factor: 4.497

3. Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon.

Authors: Yang Huang; Henry J Lowe; Dan Klein; Russell J Cucina
Journal: J Am Med Inform Assoc Date: 2005-01-31 Impact factor: 4.497

3 in total

5 in total

1. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

Authors: Min Jiang; Yukun Chen; Mei Liu; S Trent Rosenbloom; Subramani Mani; Joshua C Denny; Hua Xu
Journal: J Am Med Inform Assoc Date: 2011-04-20 Impact factor: 4.497

A grammar-based classification of negations in clinical radiology reports.

1. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS.

2. A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports.

3. Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon.

1. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

2. Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm.

3. Intelligent image retrieval based on radiology reports.

4. Identifying patient smoking status from medical discharge records.

5. Evaluation of negation and uncertainty detection and its impact on precision and recall in search.