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...

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Guardado en:
Detalles Bibliográficos
Publicado: 2017
Materias:
Artificial intelligence
Data handling
Deep learning
Information retrieval
Information use
Learning algorithms
Natural language processing systems
Radiation
Radiology
Supervised learning
Annotated datasets
Classification models
Information extraction techniques
Manual annotation
Medical doctors
Radiology reports
Spanish Radiology
Supervised machine learning
Data mining
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13138502_v2017-September_n_p177_Cotik
http://hdl.handle.net/20.500.12110/paper_13138502_v2017-September_n_p177_Cotik
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_13138502_v2017-September_n_p177_Cotik
record_format dspace
spelling paper:paper_13138502_v2017-September_n_p177_Cotik2023-06-08T16:10:19Z Annotation of entities and relations in Spanish radiology reports Artificial intelligence Data handling Deep learning Information retrieval Information use Learning algorithms Natural language processing systems Radiation Radiology Supervised learning Annotated datasets Classification models Information extraction techniques Manual annotation Medical doctors Radiology reports Spanish Radiology Supervised machine learning Data mining 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. © 2018 Association for Computational Linguistics (ACL). All rights reserved. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13138502_v2017-September_n_p177_Cotik http://hdl.handle.net/20.500.12110/paper_13138502_v2017-September_n_p177_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 Artificial intelligence
Data handling
Deep learning
Information retrieval
Information use
Learning algorithms
Natural language processing systems
Radiation
Radiology
Supervised learning
Annotated datasets
Classification models
Information extraction techniques
Manual annotation
Medical doctors
Radiology reports
Spanish Radiology
Supervised machine learning
Data mining
spellingShingle Artificial intelligence
Data handling
Deep learning
Information retrieval
Information use
Learning algorithms
Natural language processing systems
Radiation
Radiology
Supervised learning
Annotated datasets
Classification models
Information extraction techniques
Manual annotation
Medical doctors
Radiology reports
Spanish Radiology
Supervised machine learning
Data mining
Annotation of entities and relations in Spanish radiology reports
topic_facet Artificial intelligence
Data handling
Deep learning
Information retrieval
Information use
Learning algorithms
Natural language processing systems
Radiation
Radiology
Supervised learning
Annotated datasets
Classification models
Information extraction techniques
Manual annotation
Medical doctors
Radiology reports
Spanish Radiology
Supervised machine learning
Data mining
description 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. © 2018 Association for Computational Linguistics (ACL). All rights reserved.
title Annotation of entities and relations in Spanish radiology reports
title_short Annotation of entities and relations in Spanish radiology reports
title_full Annotation of entities and relations in Spanish radiology reports
title_fullStr Annotation of entities and relations in Spanish radiology reports
title_full_unstemmed Annotation of entities and relations in Spanish radiology reports
title_sort annotation of entities and relations in spanish radiology reports
publishDate 2017
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13138502_v2017-September_n_p177_Cotik
http://hdl.handle.net/20.500.12110/paper_13138502_v2017-September_n_p177_Cotik
_version_ 1768545800438153216

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