Computational methods applied to syphilis: where are we, and where are we going?

Autor:	Albuquerque G; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Fernandes F; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Barbalho IMP; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Barros DMS; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Morais PSG; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Morais AHF; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Santos MM; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Galvão-Lima LJ; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Sales-Moioli AIL; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Santos JPQ; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Gil P; Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal., Henriques J; Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal., Teixeira C; Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal., Lima TS; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.; Ministry of Health, Esplanada dos Ministérios, Brasília, Brazil., Coutinho KD; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Pinto TKB; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil., Valentim RAM; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.
Jazyk:	angličtina
Zdroj:	Frontiers in public health [Front Public Health] 2023 Aug 23; Vol. 11, pp. 1201725. Date of Electronic Publication: 2023 Aug 23 (Print Publication: 2023).
DOI:	10.3389/fpubh.2023.1201725
Abstrakt:	Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Albuquerque, Fernandes, Barbalho, Barros, Morais, Morais, Santos, Galvão-Lima, Sales-Moioli, Santos, Gil, Henriques, Teixeira, Lima, Coutinho, Pinto and Valentim.)
Databáze:	MEDLINE
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