Background: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses.Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models.Method: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified.Results: Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika.Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.

Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm—namely, the convolutional extreme learning machine (CELM)—we present a systematic review that investigates alternative deep learning architectures that use the extreme learning machine (ELM) for faster training to solve problems that are based on image analysis. We detail each of the architectures that are found in the literature along with their application scenarios, benchmark datasets, main results, and advantages, and then present the open challenges for CELM. We followed a well-structured methodology and established relevant research questions that guided our findings. Based on 81 primary studies, we found that object recognition is the most common problem that is solved by CELM, and CCN with predefined kernels is the most common CELM architecture proposed in the literature. The results from experiments show that CELM models present good precision, convergence, and computational performance, and they are able to decrease the total processing time that is required by the learning process. The results presented in this systematic review are expected to contribute to the research area of CELM, providing a good starting point for dealing with some of the current problems in the analysis of computer vision based on images.

A dengue é uma infecção viral que se espalha rapidamente e é endêmica em mais de 100 países tropicais e subtropicais da África, América e regiões da Ásia-Pacífico. Geralmente, a epidemiologia da dengue é influenciada por uma complexa interação de fatores que incluem rápida urbanização e aumento da densidade populacional, capacidade dos sistemas de saúde, eficácia dos sistemas de controle de vetores, limpeza urbana etc. No Brasil que é considerado um país tropical, há uma incidência de casos crescente nos últimos anos, em específico na capital Pernambucana Recife há um ambiente propício para o aumento expressivo dos casos de dengue como são apresentados pelo Boletim Epidemiológico da Secretaria de Vigilância em Saúde do Ministério da Saúde. O presente trabalho apresenta um estudo comparativo de técnicas de Mineração de Dados seguindo a abordagem CRISP-DM, para um modelo de previsão de casos de Dengue no Recife-PE.

The reduction of fetal, neonatal, and infant mortality rates is crucial in maternal and neonatal care. The UN’s 2030 Agenda aims for a better world by 2030, highlighting goal 3: healthy lives for all ages, with a focus on reducing global maternal and child mortality. In this context, prenatal care plays a vital role in identifying pregnant women at risk and enabling proactive interventions to minimize adverse outcomes, whether mortality or morbidity. This work presents the ANGELS (An iNtelligent GEstational foLlow-up System). This work presents ANGELS, an intelligent gestational follow-up platform designed to integrate and operationalize multiple machine learning models within a unified decision support system for maternal and neonatal care. The platform provides integrated services through a modular, API-based architecture, enabling the incorporation of different predictive models throughout pregnancy, childbirth, and postpartum periods. The system is validated in this study through an exemplar case focused on congenital syphilis risk prediction using real-world Brazilian data, demonstrating howpreviously validated models can be integrated, extended, and deployed within the ANGELS platform. In the exemplar case of congenital syphilis risk prediction, the complementary version of ANGELS achieved substantial improvements, with specificity and precision increasing from approximately 60% in the basic version to values close to 95%, reflecting known trade-offs across evaluation metrics. The approach proved promising for practical incorporation into healthcare systems, aiming to provide more accurate and personalized monitoring tailored to individual needs. It is hoped that the ANGELS system may contribute to reduce preventable stillbirths, improve the quality of maternal and child care, and help achieve the UN’s 2030 Agenda goals. Additionally, the integration of ANGELS services into healthcare systems and/or programs will strengthen efforts to promote the well-being of pregnant women and children.

Context: This research is set within the domain of neglected tropical diseases, specifically focusing on Chikungunya, a mosquito-borne viral disease. The study is motivated by the prevalence of Chikungunya in Brazil and the challenges associated with its chronic symptoms. Problem: The primary issue addressed is the difficulty in predicting which patients with acute Chikungunya will progress to the chronic phase. This progression leads to prolonged joint pain and other severe symptoms, affecting quality of life. Solution: We evaluate machine learning models, trained to predict the likelihood of acute Chikungunya cases progressing to the chronic phase. Theory of IS: The Information Processing Theory serves as the theoretical foundation, explaining how data is transformed into actionable insights. In this context, machine learning models act as information processors, learning from a real dataset how to produce predictions that can aid clinical decisions. Method: The methodology involves a quantitative analysis of different machine learning models, using documented data from Chikungunya diagnosed patients. The goal is to assess the models’ performance in predicting disease progression. Summary of Results: Preliminary results show that the XGBoost model demonstrates 79.31% of recall and 74.19% of f1-score in distinguishing patients likely to develop chronic conditions. Contributions and impact on the field of IS: This study contributes to both information systems and healthcare by enhancing the process of decision-making for health professionals. The implications are significant for academic research and practical application (including SUS applicability), providing information that facilitate earlier and more targeted treatments, focusing on patient quality of life.

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.