Architecture and Organization of a Computational System for the Management of Data from Individuals with Parkinson’s Disease

Abstract

The volume of data for monitoring wellbeing and health of individuals, and the number of devices used to perform tests in patients remotely, have grown substantially. Following this trend, over the past years we have seen an increase in the number of studies reporting the monitoring of the cardinal signs (e.g., tremor, stiffness and bradykinesia) of Parkinson’s disease (PD) during prolonged activities for hours or days. A major challenge in the area is to monitor the progress of the disorder objectively so that treatments can be customized. In addition, patients suffer from the lack of predictive information regarding their health condition in the future. In this context, customized systems, i.e., database, that can group information from the motor symptoms of PD are of paramount relevance. By using such systems, one can track the progress of the disorder and more importantly can use data mining for seeking hidden patterns in the data. To contribute for the organization and management of information obtained from patients with PD this research proposed the architecture and organization of a multiplatform system with customized user control, modules and permissions to manipulate information on each screen. This system has three modules, being the first for storing and organizing information from data collection with distinct types of data; the second for the management of information from the application of the Unified Parkinson’s Disease Rating Scale (UPDRS); and the third for promoting technological innovation in the area. These three modules in a single system can be part of the clinical routine of hospitals and research centers dedicated to the understanding, treatment and research in PD.