Viviana Cotik

Syntactic methods for negation detection in radiology reports in Spanish

Identification of the certainty of events is an important text mining problem. In particular, biomedical texts report medical conditions or findings that might be factual, hedged or negated. Identification of negation and its scope over a term of interest determines whether a finding is reported and is a challenging task. Not much work has been performed for Spanish in this domain. In this work we introduce different algorithms developed to determine if a term of interest is under the scope of negation in radiology reports written in Spanish. The methods include syntactic techniques based in rules derived from PoS tagging patterns, constituent tree patterns and dependency tree patterns, and an adaption of NegEx, a well known rule-based negation detection algorithm (Chapman et al., 2001a). All methods outperform a simple dictionary lookup algorithm developed as baseline. NegEx and the PoS tagging pattern method obtain the best results with 0.92 F1.

Annotation of Entities and Relations in Spanish Radiology Reports.

Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data.

Automatic Detection of Negated Findings with NooJ: First Results

The objective of this study is to develop a methodology for the automatic detection of negated findings in radiological reports which takes into account semantic and syntactic descriptions, as well as morphological and syntactic analysis rules. 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 computerized grammar. Computational framework was carried out with NooJ, a free software developed by Silberztein, which has various utilities for treating natural language. Results show that the detection of negated findings improves if lexical-grammatical information is added.

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

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.