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Doctoral Thesis
DOI
Document
Author
Full name
Leandro Zerbinatti
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2010
Supervisor
Committee
Moura Junior, Lincoln de Assis (President)
Castilla, André Coutinho
Gutierrez, Marco Antonio
Nardon, Fabiane Bizinella
Pisa, Ivan Torres
Title in Portuguese
Extração de conhecimento de laudos de radiologia torácica utilizando técnicas de processamento estatístico de linguagem natural.
Keywords in Portuguese
Informática em saúde
Processamento estatístico de linguagem natural
Representação do conhecimento em saúde
Abstract in Portuguese
Este trabalho promove um estudo em informática em saúde no qual se analisam laudos de radiologia torácica através de métodos de processamento estatístico de linguagem natural com o intuito de subsidiar a interoperabilidade entre sistemas de saúde. Foram utilizados 2000 laudos de radiologia do tórax para a extração de conhecimento identificando-se as palavras, n-gramas e frases que os compõem. Foi calculado o índice de Zipf e verificou-se que poucas palavras compõem a maioria dos laudos e que a maioria das palavras não tem representatividade estatística A partir dos termos identificados foi realizada a tradução e a comparação da existência desses em um vocabulário médico padronizado com terminologia internacional, o SNOMEDCT. Os termos que tinham uma relação completa e direta com os termos traduzidos foram incorporados nos termos de referência juntamente com a classe à qual o termo pertence e seu identificador. Foram selecionados outros 200 laudos de radiologia de tórax para realizar o experimento de rotulação dos termos em relação à referência. A eficiência obtida neste estágio, que é o percentual de rotulação dos laudos, foi de 45,55%. A partir de então foram incorporados aos termos de referência, sob a classe de conceito de ligação, artigos, preposições e pronomes. É importante ressaltar que esses termos não adicionam conhecimento de saúde ao texto. A eficiência obtida foi de 73,23%, aumentando significativamente a eficiência obtida anteriormente. Finalizamos o trabalho com algumas formas de aplicação dos laudos rotulados para a interoperabilidade de sistemas, utilizando para isto ontologias, o HL7 CDA (Clinical Documents Architecture) e o modelo de arquétipos da Fundação OpenEHR.
Title in English
Knowledge extraction from reports of radiology thoracic using techniques of statistical processing of natural language.
Keywords in English
Health informatics
Health knowledge representation
Statistical natural language processing
Abstract in English
This work promotes a study in health informatics technology which analyses reports of chest X-ray through statistical natural language processing methods for the purpose of supporting the interoperability between health systems. Two thousand radiology reports were used for the extraction of knowledge by identifying the words, n-grams and phrases of reports. Zipfs constant was studied and it was determined that few words make up the majority of the reports and that most of the words do not have statistical significance. The translation and comparison with exisiting standardized medical vocabulary with international terminology, called SNOMED-CT, was done based on the terms identified. The terms that had a complete and direct correlation with the translated terms were incorporated into the reference terms along with its class and the word identifier. Another 200 reports of chest x-rays were selected to perform the terms tagging experiment of with respect to the reference. The efficiency obtained, which is the percentage of labeling of the reports, was 45.55%. Subsequentely, articles, prepositions and pronouns were incorporated into the terms of reference under the linkage concept of class. It is important to note that these terms do not carry health knowledge to the text. Thus, the efficiency ratio was 73.23%, significantly increasing the efficiency obtained previously. The study was concluded with some forms of application of the reports tagged for system interoperability, using different ontologies, the HL7 CDA (Clinical Documents Architecture) and the archetypes at OpenEHR Fondation.
 
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Publishing Date
2010-09-24
 
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