Evidence map›Paper›PMID 30097747›Full record

ArticleJournal of digital imaging2019

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

Walter Koza, Darío Filippo, Viviana Cotik, Vanesa Stricker, Mirian Muñoz, Ninoska Godoy, Natalia Rivas, Ricardo Martínez-Gamboa

Abstract read
In one paragraph

Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Walter KozaInstituto de Literatura y Ciencias del Lenguaje, Pontificia Universidad Católica de Valparaíso, Proyecto FONDECyT 1171033, Valparaíso, Chile. walter.koza@pucv.cl.ORCID 0000-0002-5043-6561
Darío FilippoHospital Pediátrico Garrahan, Buenos Aires, Argentina.
Viviana CotikDepartamento de Ciencias de la Computación, FCEyN, Universidad de Buenos Aires, Buenos Aires, Argentina.
Vanesa StrickerDepartamento de Ciencias de la Computación, FCEyN, Universidad de Buenos Aires, Buenos Aires, Argentina.
Mirian MuñozInstituto de Literatura y Ciencias del Lenguaje, Pontificia Universidad Católica de Valparaíso, Proyecto FONDECyT 1171033, Valparaíso, Chile.
Ninoska GodoyInstituto de Literatura y Ciencias del Lenguaje, Pontificia Universidad Católica de Valparaíso, Proyecto FONDECyT 1171033, Valparaíso, Chile.
Natalia RivasInstituto de Literatura y Ciencias del Lenguaje, Pontificia Universidad Católica de Valparaíso, Proyecto FONDECyT 1171033, Valparaíso, Chile.
Ricardo Martínez-GamboaUniversidad Diego Portales, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Electronic Health RecordsTerminology as TopicElectronic Data ProcessingHumansLanguageLinguisticsRadiologyAutomatic recognitionLexicon-grammarNegated findingsNegExNooJ

Identifiers

PMID30097747
PMCPMC6382643

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.