Navegando por Autor "Dias Neto, José Bartolomeu Alheiros"

Agora exibindo 1 - 1 de 1

Análise de dados de coinfecção tuberculose/HIV disponíveis no SINAN utilizando o banco de dados Neo4J
(2023-04-27) Dias Neto, José Bartolomeu Alheiros; Melo, Jeane Cecília Bezerra de; Freitas, Nara Suzy Aguiar de; http://lattes.cnpq.br/6891650997818766; http://lattes.cnpq.br/8499459630583005; http://lattes.cnpq.br/5415193488789338
Research carried out in recent decades indicates the need to investigate infection processes by multiple pathogens, called co-infection processes. Some coinfections have a worldwide reach, involving diseases such as: HIV, malaria, hepatitis, dengue and, more recently, COVID-19. In a study carried out with 500 volunteers carrying the HIV virus (Human Immunodeficiency Virus), it was observed that the coinfection between the HIV virus and MTB (Mycobacterium tuberculosis), the bacterium that causes tuberculosis, produced an increase in the chance of death by 4.07 times when compared to other types of co-infection. The panorama presented indicates the need for studies to identify occurrences, map their incidence in geographic terms, and even include aspects that favor the understanding of the biological mechanisms involved in co-infection processes, whether for prevention, diagnosis or treatment. In Brazil, an instrument that helps in health planning, defining and evaluating the impact of interventions, is the Information System for Notifiable Diseases – SINAN, made available by the Department of Informatics of the SUS (DATASUS). The effective use of these databases makes it possible to identify the epidemiological reality of a given geographic area. Free access to all health professionals corroborates the democratization of access to information, allowing it to be made available to the community. In this work, an exploratory analysis was carried out on data relating to TB and HIV co-infection processes, coming from SINAN, with the objective of proposing methods that facilitate the use of data from this system by health professionals who do not have technical training in computing. Considering that such an application is strongly based on data relationships, it was decided to propose a mapping of data in unconventional databases, oriented to graphs, such as Neo4J. Thus, in addition to simplifying modeling, applications of this type tend to be faster when compared to traditional applications (using relational databases). Therefore, the mapping of data available at SINAN to Neo4J allowed a more perceptible visualization of correlations, enabling an analysis of multiple factors and characteristics of co-infection processes, enhancing the information obtained from the bases of SINAN and the Tabulation System of Data made available by the agency, TABNET.