2 research outputs found

    Multi-agent model of hepatitis C virus infection

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    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 Decisi贸n para el Diagn贸stico Temprano de la Meningitis

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    Fecha de lectura de Tesis Doctoral: 18 febrero 2020La meningitis es una enfermedad pand茅mica que sufren muchos pa铆ses poco desarrollados, principalmente debido a la falta de recursos econ贸micos. El tipo m谩s grave de meningitis, la enfermedad meningoc贸cica, exige una atenci贸n m茅dica inmediata ya que retrasos en su diagn贸stico aumentan el riesgo de mortalidad. Esta tesis propone un sistema inteligente de ayuda a la decisi贸n, basado en una arquitectura de Sistemas Multiagente, con el objetivo de ayudar a los m茅dicos en las diferentes etapas del diagn贸stico precoz de la meningitis, a trav茅s, principalmente, de s铆ntomas observables. El sistema integra tres componentes inteligentes que aplican t茅cnicas de aprendizaje autom谩tico basadas en 谩rboles y t茅cnicas de ingenier铆a 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 diagn贸stico de meningitis, procedentes de Brasil. Los resultados ponen de manifiesto que el sistema es capaz de determinar con 茅xito si el paciente tiene meningitis, si esta es meningoc贸cica y si es viral o bacteriana
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