Multi-agent model of hepatitis C virus infection

Objectives: The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice. Methods and materials: In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared. Results: Important advantages of the proposed method are noted and demonstrated; these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%. Conclusions: The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections

Sistema Inteligente de Ayuda a la Decisión para el Diagnóstico Temprano de la Meningitis

Fecha de lectura de Tesis Doctoral: 18 febrero 2020La meningitis es una enfermedad pandémica que sufren muchos países poco desarrollados, principalmente debido a la falta de recursos económicos. El tipo más grave de meningitis, la enfermedad meningocócica, exige una atención médica inmediata ya que retrasos en su diagnóstico aumentan el riesgo de mortalidad. Esta tesis propone un sistema inteligente de ayuda a la decisión, basado en una arquitectura de Sistemas Multiagente, con el objetivo de ayudar a los médicos en las diferentes etapas del diagnóstico precoz de la meningitis, a través, principalmente, de síntomas observables. El sistema integra tres componentes inteligentes que aplican técnicas de aprendizaje automático basadas en árboles y técnicas de ingeniería del conocimiento. En los estudios realizados en el marco de este trabajo para obtener estos modelos y validarlos, se emplearon un conjunto de datos reales constituido por 26.228 registros de pacientes con diagnóstico de meningitis, procedentes de Brasil. Los resultados ponen de manifiesto que el sistema es capaz de determinar con éxito si el paciente tiene meningitis, si esta es meningocócica y si es viral o bacteriana