Uso de redes neurais para o problema de previsão de pacientes de alto custo

• Ano de defesa: 2021
• Autor(a) principal: Franklin Messias Barbosa
• Orientador(a): Renato Porfirio Ishii
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Fundação Universidade Federal de Mato Grosso do Sul
• País: Brasil
• Palavras-chave em Português: redes neurais; pacientes de alto custo; HEP dataset; machine learning; predição
• Link de acesso: https://repositorio.ufms.br/handle/123456789/3959

The growing aging of the world population, along with several environmental, social, and economic factors, end up posing major challenges for public health in general. Within this scenario, it is of interest for both private health insurance operators and public health managers to better manage available resources to reach the largest possible share of society. To do so, keeping in mind the amount of information produced daily, it is also clear the need for data processing and decision support technologies so that such management can be done satisfactorily. This study aims to analyze the application of machine learning and deep learning techniques in health care scenarios. One of the possible applications includes the detection of possible high-cost patients from historical data, to better target interventions that may prevent the transition of regular patients into high-cost ones or, in the case of those who are already in this condition, to allow appropriate approaches, rather than generic ones. In both cases, the detection of such patients can be beneficial, reducing avoidable costs and improving patients’ condition. The final model, chosen to predict the high-cost condition was a fully connected sequential network, with 3 hidden layers and 3 dropout layers. That network had 88% on accuracy and f1 score metrics, 91% on recall, 86% on precision and 84% specificity, showing the model’s capacity to correctly classify examples from both classes. This work also aimed to make the creation and testing of such networks easier, by providing the tools developed during its evolution on GitHub.