A methodology for mapping non-structured medical findings to the attribute-value table format / Metodologia para mapeamento de informações não estruturadas descritas em laudos médicos para uma representação atributo-valor

AUTOR(ES)

Daniel de Faveri Honorato

DATA DE PUBLICAÇÃO

2008

RESUMO

The information retrieval from text stored in computer-based patient records is an important open-ended research problem, as the ease in which biomedical information recorded and stored in digital form grows. Thus, means to extract structured information (for example, in the so-called attribute-value format) from free-text records is an important research endeavor. Furthermore, by representing the free-text records in the attribute-value format, available pattern extraction methods can be directly applied. To map free-text medical records into the attribute-value format, we propose a methodology that can be used to automatically (or semi-automatically, with the help of a medical expert) map the important medical information stored in patient records which are described in natural language into an structured format. This methodology has been implemented in a computational system called TP-DISCOVER, which generates a database in the attribute-value format from a set of patient records (documents). In order to identify important entities in the set of documents, as well as significant relations among these entities, we propose a hybrid linguistic/statistical terminology extraction approach which filters out words and phrases that appear with a frequency higher than a given threshold by applying statistical measures. The underlying assumption of this hybrid approach to terminology extraction is that specialized documents are characterized by repeated use of certain lexical units or morpho-syntactic constructions. Our goal is to reduce the effort spent in manual modelling by observing regularities in the texts and by mapping them into suitable attribute names in the attribute-value representation format. The proposed methodology was evaluated to automatically structure a collection of 6000 documents which contains High Digestive Endoscopies exams´ results described in natural language. The experimental results, all of which can be considered lower bound results as they would greatly improve in case the methodology is applied semi-automatically together with a medical expert, show that the proposed methodology is suitable to reduce the medical expert workload in analysing large amounts of medical records

ASSUNTO(S)

text pre-processing mineração de textos terminology extraction extração de terminologia pré-processamento de textos text mining

Documentos Relacionados

• Managing Attribute—Value Clinical Trials Data Using the ACT/DB Client—Server Database System
• Uma Metodologia para Mineração de Regras de Associação Usando Ontologias para Integração de Dados Estruturados e Não-Estruturados
• A technique for interactive shape deformation on non-structured objects
• ON THE PERFORMANCE OF DIFFERENT RANS BASED MODELS TO DESCRIBE THE TURBULENT FLOW IN AN AGITATED VESSEL USING NON-STRUCTURED GRIDS AND PIV VALIDATION
• Aprendizagem por reforço na adaptação a obstáculos em navegação robótica autônoma não-estruturada baseada em imagens