Oncologia Matemática: usando a teoria do controle ótimo para o cronograma de dosagens em quimioterapia

• Autor(a) principal: Sousa, Ialy Cordeiro de
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/123456789/19406
• topic: Oncologia matemática; Câncer; Tratamento quimioterápico; Modelagem matemática; Otimização; Mathematical oncology; Chemotherapy treatment; Mathematical modeling; Optimization; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO

Cancer is the second noncommunicable disease with the highest number of cases and deaths in the world, behind only cardiovascular diseases. And it does not depend on the socio-economic development of the country to manifest itself, differently, from the epidemiological diseases that are more prone in low- and middle-level regions of development. It is estimated that in 2030 will be diagnosed more than 21 million new cases in the world. With regard to ndustrial Engineering, this has a negative impact on the economy and the labor market due to premature retirements, deaths and even retirements within the scope of global and national collective health. Several studies and treatments have been developed over the years to combat cancer, from surgeries, radiotherapies, chemotherapies, to targeted biological therapies. Among them, chemotherapy is widely used, bringing good results, but accompanied by many side effects. Therefore, it is relevant to quantitatively evaluate the chemotherapy treatment process. Given this scene, this work aims to study the problems of evolution and fight against cancer by analyzing through the mathematical models the strategies of administration of chemotherapeutics. As a result, a mathematical model of mixed integer nonlinear programming in the literature was analyzed and, from it, generated an integer linear programming model that was able to present an optimal schedule of chemotherapy dosages and speci_c for each patient. For this, we used Matlab software for numerical resolution and the Cplex resolver for model optimization.