DCARE: Um Modelo Computacional para Acompanhamento de Pessoas com Doença de Alzheimer baseado na Análise de Históricos de Contextos
The aging of the population generates the incidence of diseases characteristic of advancing age, among them Alzheimer’s Disease (AD). Patients with this illness, which affects neurological functions, need support to maintain maximum independence and security during this stage of life, as the cure and reversal of symptoms have not yet been discovered. This work aims to propose a model that, based on physiological data received from external applications, makes it possible to identify possible dangerous behaviors of patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, is the only one that uses analysis of Context Histories to perform predictions. The computational model used in its structure an ontology developed by this project for the treatment of contexts within Alzheimer’s. In addition, a simulator called DCARE Dataset Simulator, of Activity Daily Living (ADLs) which generates datasets were developed to perform tests of the model and an ontology has been proposed for the treatment of contexts in the subject of Alzheimer’s. DCARE is based on the experimental research method, to understand the disease and find solutions to minimize its impact on the daily monitoring of patients. Scenarios used in the construction of the model were created over interviews with five specialists in the care for patients with AD. Tests were performed with the mass of data with 1026 scenarios generated by the proposed simulator by this work. The results revealed that the predictions of the model scenarios reached the objective of the work, achieving 97.44% of the average accuracy rate.
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