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

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.

Electronic health records in Brazil: Prospects and technological challenges.

Electronic Health Records (EHR) are critical tools for advancing digital health worldwide. In Brazil, EHR development must follow specific standards, laws, and guidelines that contribute to implementing beneficial resources for population health monitoring. This paper presents an audit of the main approaches used for EHR development in Brazil, thus highlighting prospects, challenges, and existing gaps in the field. We applied a systematic review protocol to search for articles published from 2011 to 2021 in seven databases (Science Direct, Web of Science, PubMed, Springer, IEEE Xplore, ACM Digital Library, and SciELO). Subsequently, we analyzed 14 articles that met the inclusion and quality criteria and answered our research questions. According to this analysis, 78.58% (11) of the articles state that interoperability between systems is essential for improving patient care. Moreover, many resources are being designed and deployed to achieve this communication between EHRs and other healthcare systems in the Brazilian landscape. Besides interoperability, the articles report other considerable elements: (i) the need for increased security with the deployment of permission resources for viewing patient data, (ii) the absence of accurate data for testing EHRs, and (iii) the relevance of defining a methodology for EHR development. Our review provides an overview of EHR development in Brazil and discusses current gaps, innovative approaches, and technological solutions that could potentially address the related challenges. Lastly, our study also addresses primary elements that could contribute to relevant components of EHR development in the context of Brazil's public health system. Systematic review registration: PROSPERO, identifier CRD42021233219, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233219.

Virtual Learning Environment of the Brazilian Health System (AVASUS): Efficiency of Results, Impacts, and Contributions.

The Virtual Learning Environment of the Brazilian Health System (AVASUS) is a free and open distance education platform of the Ministry of Health (MS). AVASUS is a scalable virtual learning environment that has surpassed 800,000 users, 2 million enrollments, and 310 courses in its catalog. The objective of this paper was to assess the impacts of the educational offerings on health services and AVASUS course participants' professional practice. This study analyzed data from AVASUS, the Brazilian National Registry of Health Care Facilities (CNES), the Brazilian Occupational Classification (CBO), and a questionnaire applied to 720-course participants from five regions of Brazil. After acquiring and extracting data, computational methods were used for the evaluation process. Only the responses of 462 participants were considered for data analysis, as they had a formal link to CNES. The results showed that respondents recommended 76.2% of AVASUS courses to peers. Accordingly, the quality of educational offerings motivated 81.3% of such recommendations. In addition, 75.6% of course participants who answered the questionnaire also indicated that AVASUS course contents contribute to enhancing existing health services in the health facilities where they work. Finally, 24.6% of all responses mentioned that courses available in AVASUS were essential in offering new health services in such facilities.

Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.

Dengue is recognized as a health problem that causes significant socioeconomic impacts throughout the world, affecting millions of people each year. A commonly used method for monitoring the dengue vector is to count the eggs that Aedes aegypti mosquitoes have laid in spatially distributed ovitraps. Given this approach, the present study uses a database collected from 397 ovitraps allocated across the city of Natal, RN-Brazil. The Egg Density Index for each neighborhood was computed weekly, over four complete years (from 2016 to 2019), and simultaneously analyzed with the dengue case incidence. Our results illustrate that the incidence of dengue is related to the socioeconomic level of the neighborhoods in the city of Natal. A deep learning algorithm was used to predict future dengue case incidence, either based on the previous weeks of dengue incidence or the number of eggs present in the ovitraps. The analysis reveals that ovitrap data allows earlier prediction (four to six weeks) compared to dengue incidence itself (one week). Therefore, the results validate that the quantification of Aedes aegypti eggs can be valuable for the early planning of public health interventions.

The Text Mining Technique Applied to the Analysis of Health Interventions to Combat Congenital Syphilis in Brazil: The Case of the "Syphilis No!" Project.

Congenital syphilis (CS) remains a threat to public health worldwide, especially in developing countries. To mitigate the impacts of the CS epidemic, the Brazilian government has developed a national intervention project called "Syphilis No." Thus, among its range of actions is the production of thousands of writings featuring the experiences of research and intervention supporters (RIS) of the project, called field researchers. In addition, this large volume of base data was subjected to analysis through data mining, which may contribute to better strategies for combating syphilis. Natural language processing is a form of knowledge extraction. First, the database extracted from the "LUES Platform" with 4,874 documents between 2018 and 2020 was employed. This was followed by text preprocessing, selecting texts referring to the field researchers' reports for analysis. Finally, for analyzing the documents, N-grams extraction (N = 2,3,4) was performed. The combination of the TF-IDF metric with the BoW algorithm was applied to assess terms' importance and frequency and text clustering. In total, 1019 field activity reports were mined. Word extraction from the text mining method set out the following guiding axioms from the bigrams: "confronting syphilis in primary health care;" "investigation committee for congenital syphilis in the territory;" "municipal plan for monitoring and investigating syphilis cases through health surveillance;" "women's healthcare networks for syphilis in pregnant;" "diagnosis and treatment with a focus on rapid testing." Text mining may serve public health research subjects when used in parallel with the conventional content analysis method. The computational method extracted intervention activities from field researchers, also providing inferences on how the strategies of the "Syphilis No" Project influenced the decrease in congenital syphilis cases in the territory.

Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil.

INTRODUCTION: Syphilis is a sexually transmitted disease (STD) caused by Treponema pallidum subspecies pallidum. In 2016, it was declared an epidemic in Brazil due to its high morbidity and mortality rates, mainly in cases of maternal syphilis (MS) and congenital syphilis (CS) with unfavorable outcomes. This paper aimed to mathematically describe the relationship between MS and CS cases reported in Brazil over the interval from 2010 to 2020, considering the likelihood of diagnosis and effective and timely maternal treatment during prenatal care, thus supporting the decision-making and coordination of syphilis response efforts. METHODS: The model used in this paper was based on stochastic Petri net (SPN) theory. Three different regressions, including linear, polynomial, and logistic regression, were used to obtain the weights of an SPN model. To validate the model, we ran 100 independent simulations for each probability of an untreated MS case leading to CS case (PUMLC) and performed a statistical t-test to reinforce the results reported herein. RESULTS: According to our analysis, the model for predicting congenital syphilis cases consistently achieved an average accuracy of 93% or more for all tested probabilities of an untreated MS case leading to CS case. CONCLUSIONS: The SPN approach proved to be suitable for explaining the Notifiable Diseases Information System (SINAN) dataset using the range of 75-95% for the probability of an untreated MS case leading to a CS case (PUMLC). In addition, the model's predictive power can help plan actions to fight against the disease.

Machine learning applied to retinal image processing for glaucoma detection: review and perspective.

INTRODUCTION: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. METHODS: The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized. DISCUSSION: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques. CONCLUSIONS: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.