Percorrer por autor "Pellison, Felipe"
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- Decision Support Systems for Tuberculosis: Protocol for a Scoping ReviewPublication . Lima, Vinícius; Bernardi, Filipe; Pellison, Felipe; Rijo, Rui; Kritski, Afrânio; Galliez, Rafael; Alves, DomingosTuberculosis (TB) represents a global challenge in terms of prevention, care and control. Decision support systems (DSS) can supply the necessary knowledge basis to underpin investigators, policy makers and health personnel actions and to provide crucial elements that can help reducing TB burden. Thus, the objectives of this work are to present the protocol to be followed for carrying out a scoping review to identify topics where DSSs are used, to define appropriate categories and to clarify main outcomes and research gaps. As part of the protocol, five electronic bibliographic databases will be searched for articles from 2006 to 2019 and two investigators will independently screen each work using the study inclusion criteria. Data extraction will be performed, and findings will be reported. The results will be used to provide a broad understanding of how DSSs for TB are being used.
- Proposal of an integrated decision support system for Tuberculosis based on Semantic WebPublication . Lima, Vinícius; Pellison, Felipe; Bernardi, Filipe; Carvalho, Isabelle; Alves, Domingos; Rijo, Rui Pedro Charters LopesEpidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly carried out to increase the integrality of healthcare. Otherwise, knowledge extraction and clinical and administrative decision-making processes are significantly hampered, directly affecting the management and quality of health services. Thus, this work aims to establish a computerized decision support system capable of collecting, integrating and sharing TB health data in Brazilian Unified Public Health System. Also, it will allow the monitoring of infected patients and the visualization of consolidated information of regular TB and its resistant variants for health professionals and managers. The data will be made available from heterogeneous, disconnected and unstructured sources by combining traditional web services, Semantic Web resources and security algorithms. A solid knowledge base applied to epidemiological surveillance, health information governance and clinical support will be enabled to integrate the multiple areas of TB patients care, as well as to support the creation of more accurate operational and diagnostics models