TY - JOUR
T1 - Automatic Detection of Negated Findings in Radiological Reports for Spanish Language
T2 - Methodology Based on Lexicon-Grammatical Information Processing
AU - Koza, Walter
AU - Filippo, Darío
AU - Cotik, Viviana
AU - Stricker, Vanesa
AU - Muñoz, Mirian
AU - Godoy, Ninoska
AU - Rivas, Natalia
AU - Martínez-Gamboa, Ricardo
N1 - Publisher Copyright:
© 2018, Society for Imaging Informatics in Medicine.
PY - 2019/2/15
Y1 - 2019/2/15
N2 - 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.
AB - 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.
KW - Automatic recognition
KW - Lexicon-grammar
KW - NegEx
KW - Negated findings
KW - NooJ
UR - http://www.scopus.com/inward/record.url?scp=85051667846&partnerID=8YFLogxK
U2 - 10.1007/s10278-018-0113-8
DO - 10.1007/s10278-018-0113-8
M3 - Article
C2 - 30097747
AN - SCOPUS:85051667846
SN - 0897-1889
VL - 32
SP - 19
EP - 29
JO - Journal of Digital Imaging
JF - Journal of Digital Imaging
IS - 1
ER -