An Approach for Automatic Classification of Radiology Reports in Spanish
Automatic detection of relevant terms in medical reports is useful for educational purposes and for clinical research. Natural language processing (NLP) techniques can be applied in order to identify them. In this work we present an approach to classify radiology reports written in Spanish into two...
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09269630_v216_n_p634_Cotik http://hdl.handle.net/20.500.12110/paper_09269630_v216_n_p634_Cotik |
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paper:paper_09269630_v216_n_p634_Cotik2023-06-08T15:51:43Z An Approach for Automatic Classification of Radiology Reports in Spanish Cotik, Viviana Erica Natural language processing Negation detection Pathological findings Radiology reports Text classification Bioinformatics Classification (of information) Clinical research Radiation Radiology Text processing Automatic classification Automatic Detection Classification tasks Nlp techniques Pathological findings Radiology reports SIMPLE algorithm Text classification Natural language processing systems classification human human experiment natural language processing radiology algorithm computer assisted diagnosis controlled vocabulary data mining machine learning natural language processing nomenclature procedures radiology information system semantics Spain translating (language) Algorithms Data Mining Machine Learning Natural Language Processing Radiographic Image Interpretation, Computer-Assisted Radiology Information Systems Semantics Spain Terminology as Topic Translating Vocabulary, Controlled Automatic detection of relevant terms in medical reports is useful for educational purposes and for clinical research. Natural language processing (NLP) techniques can be applied in order to identify them. In this work we present an approach to classify radiology reports written in Spanish into two sets: the ones that indicate pathological findings and the ones that do not. In addition, the entities corresponding to pathological findings are identified in the reports. We use RadLex, a lexicon of English radiology terms, and NLP techniques to identify the occurrence of pathological findings. Reports are classified using a simple algorithm based on the presence of pathological findings, negation and hedge terms. The implemented algorithms were tested with a test set of 248 reports annotated by an expert, obtaining a best result of 0.72 F1 measure. The output of the classification task can be used to look for specific occurrences of pathological findings. © 2015 IMIA and IOS Press. Fil:Cotik, V. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2015 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09269630_v216_n_p634_Cotik http://hdl.handle.net/20.500.12110/paper_09269630_v216_n_p634_Cotik |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Natural language processing Negation detection Pathological findings Radiology reports Text classification Bioinformatics Classification (of information) Clinical research Radiation Radiology Text processing Automatic classification Automatic Detection Classification tasks Nlp techniques Pathological findings Radiology reports SIMPLE algorithm Text classification Natural language processing systems classification human human experiment natural language processing radiology algorithm computer assisted diagnosis controlled vocabulary data mining machine learning natural language processing nomenclature procedures radiology information system semantics Spain translating (language) Algorithms Data Mining Machine Learning Natural Language Processing Radiographic Image Interpretation, Computer-Assisted Radiology Information Systems Semantics Spain Terminology as Topic Translating Vocabulary, Controlled |
| spellingShingle |
Natural language processing Negation detection Pathological findings Radiology reports Text classification Bioinformatics Classification (of information) Clinical research Radiation Radiology Text processing Automatic classification Automatic Detection Classification tasks Nlp techniques Pathological findings Radiology reports SIMPLE algorithm Text classification Natural language processing systems classification human human experiment natural language processing radiology algorithm computer assisted diagnosis controlled vocabulary data mining machine learning natural language processing nomenclature procedures radiology information system semantics Spain translating (language) Algorithms Data Mining Machine Learning Natural Language Processing Radiographic Image Interpretation, Computer-Assisted Radiology Information Systems Semantics Spain Terminology as Topic Translating Vocabulary, Controlled Cotik, Viviana Erica An Approach for Automatic Classification of Radiology Reports in Spanish |
| topic_facet |
Natural language processing Negation detection Pathological findings Radiology reports Text classification Bioinformatics Classification (of information) Clinical research Radiation Radiology Text processing Automatic classification Automatic Detection Classification tasks Nlp techniques Pathological findings Radiology reports SIMPLE algorithm Text classification Natural language processing systems classification human human experiment natural language processing radiology algorithm computer assisted diagnosis controlled vocabulary data mining machine learning natural language processing nomenclature procedures radiology information system semantics Spain translating (language) Algorithms Data Mining Machine Learning Natural Language Processing Radiographic Image Interpretation, Computer-Assisted Radiology Information Systems Semantics Spain Terminology as Topic Translating Vocabulary, Controlled |
| description |
Automatic detection of relevant terms in medical reports is useful for educational purposes and for clinical research. Natural language processing (NLP) techniques can be applied in order to identify them. In this work we present an approach to classify radiology reports written in Spanish into two sets: the ones that indicate pathological findings and the ones that do not. In addition, the entities corresponding to pathological findings are identified in the reports. We use RadLex, a lexicon of English radiology terms, and NLP techniques to identify the occurrence of pathological findings. Reports are classified using a simple algorithm based on the presence of pathological findings, negation and hedge terms. The implemented algorithms were tested with a test set of 248 reports annotated by an expert, obtaining a best result of 0.72 F1 measure. The output of the classification task can be used to look for specific occurrences of pathological findings. © 2015 IMIA and IOS Press. |
| author |
Cotik, Viviana Erica |
| author_facet |
Cotik, Viviana Erica |
| author_sort |
Cotik, Viviana Erica |
| title |
An Approach for Automatic Classification of Radiology Reports in Spanish |
| title_short |
An Approach for Automatic Classification of Radiology Reports in Spanish |
| title_full |
An Approach for Automatic Classification of Radiology Reports in Spanish |
| title_fullStr |
An Approach for Automatic Classification of Radiology Reports in Spanish |
| title_full_unstemmed |
An Approach for Automatic Classification of Radiology Reports in Spanish |
| title_sort |
approach for automatic classification of radiology reports in spanish |
| publishDate |
2015 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09269630_v216_n_p634_Cotik http://hdl.handle.net/20.500.12110/paper_09269630_v216_n_p634_Cotik |
| work_keys_str_mv |
AT cotikvivianaerica anapproachforautomaticclassificationofradiologyreportsinspanish AT cotikvivianaerica approachforautomaticclassificationofradiologyreportsinspanish |
| _version_ |
1768546635382521856 |