Paulo Ricardo Muniz Barros

Influence Diagram for Selection of Pedagogical Strategies in a Multi-Agent System Learning

An Influence Diagram is a simple visual representation of a decision problem that provides an intuitive way to identify and display the essential elements, including decisions, uncertainties, and objectives, and on how they influence each other. This paper discusses its use in the selection of pedagogical strategies in a multi-agent learning system for the health care practitioners: SimDeCS (Simulation for Decision Making in the Health Care Service). A clinical case is also presented and discussed.

Leveraging the learning process in health through clinical cases simulator

This paper presents a multi-agent learning system for health care practitioners named SimDeCS (Simulation for Decision Making in the Health Care Service). The main contribution of this work is the system architecture, model-learning environment supported by artificial intelligence techniques, and its evaluation as a educational software. The SimDeCS was designed as a multi-agent system, composed by three intelligent agents: Domain Agent, Learning Agent and Mediator Agent. The Domain Agent implements the knowledge model by probabilistic reasoning (Bayesian networks), with the knowledge encoded by human experts. The pedagogical strategies emerge from an influence diagram, based on the students conduct during the simulation. Some results related with the SimDeCs evaluation are presented.

Analyzing Softwares in Medical Education Focusing on Quality Standards

With the strong growth and evolution in systems for medical education, there is an increasing concern with the quality of available software products. This article describes a methodology for evaluating software based on the NBR ISO / IEC 9126 standards and the ten golden rules for software in medical education. To validate this method, we considered as a case study the SimDeCS system.

Development of a Recommender System to the Virtual Patient Simulator Health Simulator

This paper presents a recommender system on clinical cases and materials that may help the student in the learning process. The experiment involves the development of collaborative and content based filters and also three hybrid methods. The system was qualitatively evaluated, using accuracy metrics in prediction and classification tasks. The obtained results show promising values on the collaborative filters.

Bayes Editor: Desenvolvimento e validação de um editor de Redes Bayesianas

Este artigo tem como objetivo apresentar o desenvolvimento e validacao de um editor de redes bayesianas, denominado Bayes Editor, a ser utilizado no processo de ensino e aprendizado na area da saude. Inicia com um referencial teorico que explica os principais conceitos da area de redes bayesianas, seguido de um comparativo de ferramentas com o mesmo proposito disponiveis no mercado. O processo de desenvolvimento do editor e descrito em detalhes. A validacao do editor desenvolvido ocorreu com alunos dos cursos de enfermagem, biomedicina e informatica. A partir dos resultados pode-se melhorar a ferramenta, bem como, destacar aspectos relevantes da ferramenta, como a usabilidade e facilidade de uso, quando comparada com o uso de outro aplicativo similar.

Aprendizagem baseada em projetos: o caso health simulator

This article presents an example of using Project Based Learning (PBL) for the development of a kind of Virtual Patient Simulator - Health Simulator. It presents the theory of PBL that based structuring an extra-class work that has been done over the past two years, as well as the product generated and the results achieved to date.

SimDeCS: Architecture of a Multiagent System for Simulation of Decision Making in Health Care

This paper presents the architecture of the multiagent system SimDeCS (Intelligent Simulators for Decision Making in Health Care), describing the interaction between the agents that compose it and its practical application in the field of health education.

A Framework for Reengineering AMPLIA Enviroment

The AMPLIA - Probabilistic Multiagent Learning Environment is an educational tool aimed at creating an additional resource for assistance to medical education, supporting the development of diagnostic reasoning. The first version has some limitations when it comes to access only local network (LAN) as a function of applied technologies, and provide students with an unfriendly interface. The proposed framework aims to enable the use of Internet resources, and to propose an interface for a computer game, we developed a new technology framework that will enable the provision of knowledge to other university.