Fusion of online assessment methods for gynecological examination training

• Ano de defesa: 2019
• Autor(a) principal: Soares, Elaine Anita de Melo Gomes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: eng
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Ciências Exatas e da Saúde; Programa de Pós-Graduação em Modelos de Decisão e Saúde; UFPB
• Palavras-chave em Português: Fusion of assessment methods; Online assessment; Computational granularity; Fuzzy naive bayes; Virtual reality; Gynecological examination; Fusão de métodos de avaliação; Avaliação online; Granularidade computacional; Realidade virtual; Exame ginecológico; CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/19381

The Gynecological Exam is important for women’s health because, in addition to allowing the treatment of HPV and Herpes, it helps identifying cervical cancer in its early stages. It is well known that the more a given task is performed, the more expertise will be achieved. For some areas, particularly in medicine, the lack of practice in certain procedures can have consequences ranging from simple complications to the patient’s death. A solution proposed two decades ago is the use of virtual reality (VR) simulators for the training of certain medical procedures. This work presents a VR simulator for the Gynecological Exam with a new approach for assessing the performance of health students. The assessment system is a fusion of various Fuzzy Naive Bayes assessment methods, using Computational Granularity as their combiner. The results show that this assessment system fulfills the proposed objective and presents better results than the individual methods on their own.