A decision-making mechanism for context inference in pervasive healthcare environments

Authors:
Alessandro Copetti;J. C. B. Leite;O. Loques;Mario Fritsch Neves
Affiliations:
Departamento de Computação, Universidade Federal Fluminense, Rio das Ostras-RJ, Brazil;Instituto de Computação, Universidade Federal Fluminense, Niterói-RJ, Brazil;Instituto de Computação, Universidade Federal Fluminense, Niterói-RJ, Brazil;Departamento de Clínica Médica, Universidade do Estado do Rio de Janeiro, Rio de Janeiro-RJ, Brazil
Venue:
Decision Support Systems
Year:
2013

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Abstract

This paper presents a Fuzzy approach to health-monitoring of patients in pervasive computing environments. A decision model considers three classes of variables that represent the context information being collected: environmental, physiological, and behavioral. A case study of blood pressure monitoring was developed to identify critical situations based on medical knowledge. The solution maintains the interpretability of the decision rules, even after a learning phase which may propose adjustments in these rules. In this phase, the Fuzzy c-Means clustering was chosen to adjust membership functions, using the cluster centers. A medical team evaluated data from 24-h monitoring of 30 patients and the rating was compared with the results of the system. The proposed approach proved to be individualized, identifying critical events in patients with different levels of blood pressure with an accuracy of 90% and low number of false negatives.