Literature DB >> 30097747

Automatic Detection of Negated Findings in Radiological Reports for Spanish Language: Methodology Based on Lexicon-Grammatical Information Processing.

Walter Koza1, Darío Filippo2, Viviana Cotik3, Vanesa Stricker3, Mirian Muñoz4, Ninoska Godoy4, Natalia Rivas4, Ricardo Martínez-Gamboa5.   

Abstract

We present a methodology for the automatic recognition of negated findings in radiological reports considering morphological, syntactic, and semantic information. In order to achieve this goal, a series of rules for processing lexical and syntactic information was elaborated. This required development of an electronic dictionary of medical terminology and informatics grammars. Pertinent information for the assembly of the specialized dictionary was extracted from the ontology SNOMED CT and a medical dictionary (RANM, 2012). Likewise, a general language dictionary was also included. Lexicon-Grammar (LG), proposed by Gross (1975; Cahiers de l'institut de linguistique de Louvain, 24. 23-41 1998), was used to set up the database, which allowed an exhaustive description of the argument structure of predicates projected by lexical units. Computational framework was carried out with NooJ, a free software developed by Silberztein (Silberztein and Noo 2018, 2016), which has various utilities for treating natural language, such as morphological and syntactic grammar, as well as dictionaries. This methodology was compared with a Spanish version of NegEx (Chapman et al. Journal of Biomedical Informatics, 34(5):301-310 2001; Stricker 2016). Results show that there are minimal differences in favor of the algorithm developed using NooJ, but the quality and specificity of the data improves if lexical-grammatical information is added.

Keywords:  Automatic recognition; Lexicon-grammar; NegEx; Negated findings; NooJ

Year:  2019        PMID: 30097747      PMCID: PMC6382643          DOI: 10.1007/s10278-018-0113-8

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  7 in total

1.  A simple algorithm for identifying negated findings and diseases in discharge summaries.

Authors:  W W Chapman; W Bridewell; P Hanbury; G F Cooper; B G Buchanan
Journal:  J Biomed Inform       Date:  2001-10       Impact factor: 6.317

2.  Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT).

Authors:  Ronilda Lacson; Katherine P Andriole; Luciano M Prevedello; Ramin Khorasani
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

3.  Extracting BI-RADS Features from Portuguese Clinical Texts.

Authors:  Houssam Nassif; Filipe Cunha; Inês C Moreira; Ricardo Cruz-Correia; Eliana Sousa; David Page; Elizabeth Burnside; Inês Dutra
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2012

4.  ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.

Authors:  Henk Harkema; John N Dowling; Tyler Thornblade; Wendy W Chapman
Journal:  J Biomed Inform       Date:  2009-05-10       Impact factor: 6.317

5.  Information Extraction for Clinical Data Mining: A Mammography Case Study.

Authors:  Houssam Nassif; Ryan Woods; Elizabeth Burnside; Mehmet Ayvaci; Jude Shavlik; David Page
Journal:  Proc IEEE Int Conf Data Min       Date:  2009

6.  Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

Authors:  Qing T Zeng; Sergey Goryachev; Scott Weiss; Margarita Sordo; Shawn N Murphy; Ross Lazarus
Journal:  BMC Med Inform Decis Mak       Date:  2006-07-26       Impact factor: 2.796

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

Authors:  Andrew S Wu; Bao H Do; Jinsuh Kim; Daniel L Rubin
Journal:  J Digit Imaging       Date:  2009-11-10       Impact factor: 4.056

  7 in total
  1 in total

1.  Supporting the classification of patients in public hospitals in Chile by designing, deploying and validating a system based on natural language processing.

Authors:  Jocelyn Dunstan; Fabián Villena; Jorge Pérez; René Lagos
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-01       Impact factor: 2.796

  1 in total

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