A Methodology for Structuring Medical Reports Using Ontologies
Abstract
In computer systems aimed at generating and managing reports in radiology dominates the acquisition and storage of information in an open form of textual annotation. A major challenge faced in the development of these systems lies in the inadequacy of environments characterized by standardized interfaces that severely restrict the freedom of physicians in filling their reports, in contrast to the ineffectiveness of open text environments, which restrict a further analysis by computers. This paper aims to propose a methodology for structuring radiological reports from the Hospital das Clínicas da Faculdade de Medicina at Ribeirão Preto aiming in the future to build semi-automatically a report structure that allows knowledge extraction in order to facilitate the activities involved in teaching and researching in this hospital school. The methodology was evaluated on three bases each containing 5000 reports and the results compared with a standard ontology.References
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Brank, J., Grobelnik, M., and Mladenić, D. (2007). Automatic Evaluation of Ontologies, chapter 11. In [Kao and Poteet 2007].
Feldman, R. and Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.
Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2):199–220.
Heflin, J., Hendler, J., and Luke, S. (1999). Coping with changing ontologies in a distributed environment. In Proceedings of AAAI-99 Workshop on Ontology Management, pages 74–79. AAAI Press.
Honorato, D. F., Monard, M. C., Lee, H. D., Neto, A. P., and Chung, W. F. (2009). Avaliação de uma metodologia de mapaemento de laudos médicos para uma representação estruturada: estudo de caso com laudos de endoscopia digestiva alta. WIM - IX Workshop de Informática Médica.
Kao, A. and Poteet, S. R., editors (2007). Natural Language Processing and Text Mining. Springer.
Mitchell, T. M. (1997). Machine Learning. McGraw–Hill.
Shortliffe, E. H. and Hubbard, S. M. (1989). Information systems in oncology. In: De Vita VT, Hellman S, Rosenberg S, eds. Cancer: principles and practice of oncology.
Soares, M. V., Prati, R. C., and Monard, M. C. (2008). Pretext ii: Descrição da reestruturacão da ferramenta de pré-processamento de textos. Technical Report 333, ICMC-USP, São Carlos - SP.
Sowa, J. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing Co., Pacific Grove, CA.
Taira, R. K., Soderland, S. G., and Jakobovits, R. M. (2001). Automatic structuring of radiology free-text reports. RadioGraphics, 21:237–245.
Zhang, J., Silvescu, A., and Honavar, V. G. (2002). Ontology-driven induction of decision trees at multiple levels of abstraction. In Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation, pages 316–323, London, UK. Springer-Verlag.
Published
2011-07-19
How to Cite
NETTO, Oscar Picchi; MACEDO, Alessandra Alaniz; MARQUES, Paulo Mazzoncini de Azevedo; BARANAUSKAS, José Augusto.
A Methodology for Structuring Medical Reports Using Ontologies. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 11. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1816-1825.
ISSN 2763-8952.
