Sistema inteligente para monitoramento e predição do estado clínico de pacientes baseado em lógica fuzzy e redes neurais

• Ano de defesa: 2014
• Autor(a) principal: Schatz, Cecilia Haydee Vallejos de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial
• Palavras-chave em Português: Redes neurais (Computação); Lógica difusa; Sistemas de controle inteligente; Sistemas de transmissão de dados; Software - Desenvolvimento; Simulação (Computadores); Engenharia elétrica; Neural networks (Computer science); Fuzzy logic; Intelligent control systems; Data transmission systems; Computer software - Development; Computer simulation; Electric engineering
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/1022

The comfort and freedom of movements of patients that have to be continually monitored is a theme that has motivated the development of new technologies such as networks of wireless body sensors (WBAN) and new research areas such as telemedicine. In addition, the incorporation of intelligent software to simulate the reasoning of experts, assist them in decision making and in early detection of abnormal conditions or tendencies to develop certain diseases, opens an even larger field of research, such as the field of Artificial Intelligence in Medicine (AIM beings its acronym in English). Patient monitoring through wireless equipment and AIM technology allows to develop practical solutions to control patients in environments outside of clinics or hospitals. In this thesis, intelligent tools were used for the development of an application that allows monitoring of five vital signs of patients without them being present in a hospital bed. In a first step, typical medical procedures used by specialists for evaluating a patient were studied and transformed into rules for the fuzzy model. The proposed fuzzy model allows the analysis of the current state of the patient to create the desired outputs (targets) that are used to train the artificial neural networks. Then, a neural model was developed which, by analysing current and historic patient data, forecasts patients’ clinical status in the near future. In order to find the most exact methodology, five artificial neural networks were analyzed and compared with each other using thousands of real patient data sets. Elman MISO, Elman MIMO and NNARX – fully connected and pruned – were tested. The fuzzy model answered in a excelent form, agreeing in 99.76% to the answers given by the experts. After analizing the proposed networks in the validation dataset, it was discovered that the pruned NNARX can offer the highest overall accuracy of 99.82%, whereas the others show a decrease of up to 35%. Through techniques such as early stopping for the training with the search of the mean of MSE, FPE and correlation coefficients it was possible to achieve the best topologies of every network type, making their pruning almost unnecessary. The fully connected NNARX and the P-NNARX achieved much better results than other networks, but an increase of 1.27% was observed in the overall accuracy of the pruned network with respect to the NNARX. It can be said that for this particular case, NNARX networks capture the essence of the non-linear dynamic system much better than Elman. Finally, the P-NNARX model was chosen for the implementation of the proposed smart system. Its overall acuracy was of 99.25%, for the prediction time (t + d), with d = 1 second, by using unseen data of 30 new patients. More tests made with longer prediction periods demonstrate a slight decrease in the overall accuracy reaching up to 94.58% for d = 60 seconds. Nevertheless, it still remained over 90%. Results demonstrate the high generalization level of the system and its excellent performance in predicting the three possible patient conditions (stable, semi-stable, unstable). The next step is to turn this intelligent system into an usefull tool for preventive medicine for chronic patients.