Inferência de atividades clínicas na arquitetura ClinicSpace a partir de propriedades do contexto
To improve the system usability and assist the user during the execution of their daily clinical tasks, were designed new components and services to realize the task inference in the ClinicSpace architecture. The ClinicSpace project, currently being developed by the GMob of the PPGI/UFSM, aims to bu...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | Brasil |
| Institución: | Universidade Federal de Santa Maria (UFSM) |
| Repositorio: | Manancial - Repositório Digital da UFSM |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufsm.br:1/5363 |
| Acceso en línea: | http://repositorio.ufsm.br/handle/1/5363 |
| Access Level: | acceso abierto |
| Palabra clave: | Computação pervasiva Computação ubíqua Middleware Computação orientada a atividades Tarefas clínicas Gerenciamento de tarefas Pervasive computing Ubiquitous computing Task-driven computing Clinical activities Task management CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| Sumario: | To improve the system usability and assist the user during the execution of their daily clinical tasks, were designed new components and services to realize the task inference in the ClinicSpace architecture. The ClinicSpace project, currently being developed by the GMob of the PPGI/UFSM, aims to build a pilot tool that allows the modeling of the clinical tasks by the physician and their automatic management. To model and develop an inference service to this architecture is the main goal of the work described in the current dissertation. To realize the task inference, were used the task execution history of each user together with the present characteristics of the environment during the tasks executions. In this way, is possible to trace the profile of each user, knowing which functionalities will be necessary for him in the near future. With the capture of the environment information during the task execution, was used the C4.5 algorithm to infer, foresee, the next task to be executed. Together with the constant environment monitoring, were detected patterns that allows suppose the future execution of a task, helping the system utilization. The system presents the inferred tasks as suggestion in the graphical interface to not take automatic decisions, taking the user role, predicting a general improvement in the system usability. To validate the developed architecture, were made performance analyses of the inference mechanism, resulting in a small interference of the execution time of the whole system. |
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