Modelo de suporte à decisão aplicado ao atendimento das vítimas de acidentes de trânsito na cidade de João Pessoa

• Ano de defesa: 2012
• Autor(a) principal: Soares, Rackynelly Alves Sarmento
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Ciências Exatas e da Saúde; Programa de Pós-Graduação em Modelos de Decisão e Saúde; UFPB
• Palavras-chave em Português: Acidentes de trânsito; Árvores de decisões; Epidemiologia; Traffic accidents; Decision trees; Epidemiology; CIENCIAS DA SAUDE::SAUDE COLETIVA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/6526

Traffic accidents produce high morbidity and mortality in several countries, including Brazil. Initial care to victims of these accidents, by a specialized team, has tools for evaluating severity of trauma, which guide priorities. The purpose of this study is to understand process of decision making to meet victims of traffic accidents and from that develop an understanding of the decision support model that assists medical regulator to decide the severity of injury caused by this condition to health. The study looked at all victims of traffic accidents attended by SAMU of João Pessoa-PB in 2010. It is an epidemiological investigation based on institutional data collection instrument which was the regulation of medical records. Descriptive and spatial statistics was conducted, definition of the decision model was a decision tree whose objective attribute is represented by severity of the injury Abbreviated Injury Scale (AIS). SAMU attended 4.514 TA victims in João Pessoa in 2010. 99% of emergency care to victims were made by teams of basic units. Most victims were male (75.45%), aged between 20 and 39 years (60%) and the mechanism of injury was motorcycle (63%). The most affected body region was limbs (62%) and the more frequent AIS was AIS1 (64.3%). With regard to spatial analysis, the risk map identified the neighborhood Center as the highest risk (10.15) followed by Água Fria (3.23) and Penha (3.15). The spatial scan map that best fitted the risk map was 5% of the population and 5% significance level. The decision model chosen was decision tree that could correctly classify 99.9% of the severity of lesions, with kappa statistics 99.8%. By this model, it was possible to extract 36 rules for classification of the lesion. Given mistakes made by medical regulation on the presumed severity depending on the 192 system information, the use of decision tree makes it possible to reduce subjectivity in decisions to maximize their probability of a hit and consequent reduction in morbidity and mortality brought about by traffic accident.