Tuberculosis remains a significant health burden, especially in low and middle-income countries, contributing to substantial morbidity and mortality rates. Amid global efforts to reduce tuberculosis incidence and deaths, the COVID-19 pandemic has hindered progress achieved over the years. The severity assessment of tuberculosis patients post-diagnosis is crucial for effective treatment planning due to the disease’s complexity and diverse outcomes. Recent advancements in artificial intelligence (AI) have offered potential aids for healthcare professionals in tuberculosis treatment decisions, aiming to enhance patient outcomes and optimize healthcare resource allocation. In this context, the TITO application, utilizing an SVM-based machine learning (ML) model, was developed to predict tuberculosis prognosis. In this paper, we evaluate TITO usability among healthcare professionals in Amazonas, Brazil, using the System Usability Scale (SUS) questionnaire and results yielded a SUS score of 83.95

The growing availability of large-scale health data has created new opportunities for applying machine learning in public health systems. However, processing these datasets requires scalable computational infrastructures. This paper presents an experience report on the development of cloud-based architectures for scientific health systems using Amazon Web Services (AWS). We analyze three platforms: VALERIA for arboviral disease diagnosis, ANGELS for gestational monitoring, and IAra for malaria forecasting. These systems rely on large epidemiological datasets and machine learning pipelines executed using AWS services such as EC2, S3, ECR, Lambda, and SageMaker. Results show that cloud infrastructures enable scalable data processing, reproducible experimentation, and operational deployment of intelligent healthcare applications.

The neonatal period is marked by high vulnerability and significant morbidity and mortality, especially in the first days of life, requiring innovative strategies to improve the quality of care. This study presents sophIA, a platform under development based on Artificial Intelligence (AI) to support clinical decision-making in the Neonatal Intensive Care Unit. Its architecture integrates real clinical data, rigorous preparation, and predictive modeling using traditional and hybrid techniques, in addition to explainability mechanisms. The interface includes an interactive dashboard for indicator visualization and a support chatbot. The sophIA estimates risks for adverse neonatal outcomes and generates interpretable clinical alerts. Preliminary results indicate technical feasibility and methodological consistency, demonstrating improved performance and reliability. It is expected to contribute to more timely interventions, reduction of preventable complications, and strengthening of neonatal health management.

Purpose : Leprosy (known as Hansen’s disease in Brazil) continues to pose a major public health challenge in several endemic countries. The Simplified Neurological Assessment (from the Portuguese Avalia¸c˜ao Neurol´ogica Simplificada - ANS) is the standard procedure recommended by the Brazilian Ministry of Health to evaluate nerve function in individuals affected by leprosy, and is essential for preventing irreversible disabilities. However, its manual implementation limits data standardization, follow-up, and integration into digital health systems. This study presents the ANSd (Digital Simplified Neurological Assessment, from the Portuguese Avalia¸c˜ao Neurol´ogica Simplificada digital ), a mobile health application designed to support standardized neurological evaluation, improve documentation, and enhance longitudinal monitoring in leprosy care. Methods : The development of ANSd application was conducted as action research, applying Soft Systems Methodology (SSM) through iterative learning cycles with the Global Partnership for Zero Leprosy (GPZL) digital health priorities. The process included domain and requirement modeling, data model design, interface design, and software implementation. Results : ANSd enables digital recording of sensory and motor testing results, aggregates physical disability grading (from Portuguese, Grau de Incapacidade F´ısica - GIF) and the Eye-Hand-Foot (EHF) score, and stores structured records to support longitudinal patient follow-up. The ANSd application runs entirely offline through an offline-first architecture with encrypted on-device storage, and reproduces the official ANS form as a populated PDF, preserving regulatory compliance while enabling structured data capture. Conclusion : By consolidation standardized clinical data in a single digital platform, ANSd strengthens neurological monitoring and supports early detection of nerve impairment in individuals affected by leprosy. The ANSd application aligns with the GPZL’s strategic vision for digital health and offers a scalable solution for improving disability prevention and data-driven decisionmaking in leprosy control programs. Within the Brazilian public healthcare system (from Portuguese Sistema ´ Unico de Sa´ude - SUS), ANSd has the potential to enhance clinical workflows, facilitate integration with national surveillance platforms, and improve the continuity and quality of care for people affected by leprosy.

Leprosy remains a significant public health issue in Brazil, with a high number of cases reported annually, posing challenges in monitoring and control. Data-driven strategies are essential for enhancing disease surveillance and supporting the goals outlined in the Global Leprosy Strategy 2021-2030. Current tools to monitor leprosy in Brazil have several limitations and there is no documentation on the user requirements and empirical evaluation results of such tools. This can compromise leprosy monitoring effectiveness, timely response and data-informed decision-making in public health. This study presents the specification, development and evaluation of an interactive dashboard that leverages data from Brazil’s Disease Notification System (SINAN) to provide a detailed visualization of leprosy cases. The dashboard includes critical metrics on patient demographics, geographical distributions, and disease progression, facilitating data-driven decision-making. The process ensured that the final dashboard aligned with the specific needs and tasks of health professionals managing leprosy cases. The dashboard supports data analysis and trend visualization, empowering users to track leprosy patterns, manage cases more effectively, and anticipate resource needs. Usability tests indicated that users were able to complete analytical tasks using the dashboard with satisfactory degree of success. This work demonstrates the value of data visualization in public health information systems, providing a replicable model for tracking leprosy. The attributes, tasks and visual representations proposed can be reused by other researchers and practitioners to build similar tools. Also, by offering rapid access to actionable insights, the tool can enhance response capabilities and resource planning for leprosy and similar health challenges.

The study aimed to discuss howArtificial Intelligence (AI) has been applied to improve the care of pulmonary diseases in preterm neonates. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Systematic Literature Review(SLR)was conducted across six scientific databases, covering studies published between 2014 and 2024. From 7,565 initially identified studies, 35 were selected for analysis. Most studies focus on predicting Bronchopulmonary Dysplasia (BPD), whereas others address chronic and acute pulmonary diseases and additional clinical outcomes. There is a predominance of well-established AI models, particularly Random Forest and Logistic Regression, likely due to their recognition in the literature and consistent performance across clinical applications. Their familiarity may facilitate adoption in clinical contexts, especially when combined with commonly used evaluation metrics such as the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, and accuracy. The most frequently used data for model training include gestational attributes (e.g., gestational age and gestational diabetes), followed by neonatal attributes (e.g., birth weight and sex) and clinical attributes (e.g., comorbidities and indicators of pulmonary function). Recent studies demonstrate promising progress in applying AI to predict pulmonary diseases in preterm neonates. However, methodological limitations, including small sample sizes and inconsistent handling of missing data, remain prevalent and hinder clinical implementation. Despite these challenges, the trends and findings identified in this review may contribute meaningfully to future research.

During the neonatal period, newborns are more susceptible to developing conditions and diseases due to their fragility of the transition and adaptation to the extrauterine environment. Neonatal sepsis is one of the leading causes of morbidity and mortality in newborns, particularly among preterm and low birth weight infants, requiring early diagnosis to reduce complications and deaths. In this work, we evaluate the performance of artificial intelligence models in predicting neonatal sepsis and also identify the attributes that most contribute impact on models’ learning and their relationship with the disease, using real data from the state of Pernambuco, Brazil. The six machine learning models evaluated were AdaBoost, CatBoost, Gradient Boosting, LightGBM, Random Forest and XGBoost. Performance metrics ranged from 0.7213 to 0.8548, with AdaBoost and LightGBM achieving the best results, reaching a sensitivity above 0.8197 and a specificity of 0.8397 in all three experiments. SHAPley Additive exPlanations (SHAP) analysis revealed strong relationships between sepsis and attributes such as intracranial hemorrhage, prematurity, CPAP use, TTN presence, and epicutaneous access, all of which were highly associated with sepsis cases. We conclude that the artificial intelligence models demonstrated promising results in predicting neonatal sepsis, highlighting critical clinical attributes associated with the disease and identifying the most relevant predictors.

Este trabalho investiga a produção científica brasileira sobre Doenças Tropicais Negligenciadas (DTNs) no Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), entre 2008 e 2025. Utilizando a metodologia PRISMA, foram selecionados 45 artigos de um total de 912 publicações, revelando que menos de 5% dos estudos abordam essas doenças, apesar de seu expressivo impacto na saúde pública. Os resultados evidenciam carência de abordagens integradas para tratamento e prevenção das DTNs, indo além da modelagem preditiva epidemiológica.

BackgroundChikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to progress from the acute to the chronic phase, marked by the prevalence of symptoms of arthralgia. Joint pain compromises the performance of daily activities, including psychological, economic, and physical functioning.MethodsThrough the use of data science techniques, such as data analysis, evaluation, and visualization, the aim is to understand the influence of pain points on disease progression. Furthermore, we also evaluate artificial intelligence models to calculate the likelihood of patients progressing to a chronic phase.ResultsThe data analysis showed that arthralgia was reported by 97.70% of the sample (339 cases), followed by 74.06% edema (257 cases), 36.31% low back pain (126 cases) and 34.58% myalgia (120 cases), being factors that are related to chronicity. The artificial intelligence models have achieved metrics above 60%, demonstrating potential for estimating the likelihood of a patient's progression to the chronic phase.ConclusionsBased on these estimates, healthcare professionals can adopt preventive measures capable of mitigating the disease's impacts. Implementing these models in the decision-making process becomes an important ally in the fight against Chikungunya in Brazil, helping to mitigate the social and economic impacts caused by the chronic phase.

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.

Background and Aims:Maternal and neonatal mortality remain critical global health challenges, particularly in low-resource settings where preventable deaths occur due to inadequate access to timely care. This article explores the potential of Artificial Intelligence (AI) to enhance maternal and child healthcare by improving early risk identification, diagnosis, treatment recommendations, and postpartum monitoring. Methods:It explores the use of AI in identifying pregnancy-related risks, recommending treatments, predicting adverse outcomes, and monitoring postpartum and neonatal care. Various AI models, including supervised machine learning, Large Language Models (LLMs), and Small/Medium Language Models (SLMs/MLMs), are discussed in terms of their feasibility into resource-limited healthcare systems. Results:AI has demonstrated significant potential in identifying pregnancy-related risks, recommending treatments, predicting adverse outcomes, and supporting postpartum and neonatal care. While AI-driven solutions can optimize healthcare decision-making and resource allocation, challenges such as data availability, integration into clinical workflows, and ethical considerations must be addressed for widespread adoption. Conclusion:AI offers promising solutions to reduce maternal and neonatal mortality by enhancing risk detection and clinical decision-making. However, its real-world implementation requires overcoming barriers related to data quality, infrastructure, and equitable deployment. Future efforts should focus on data standardization, AI model optimization for resource-limited settings, and ethical considerations in clinical integration.

Leprosy, caused by Mycobacterium leprae, remains a global challenge, requiring strategies to achieve disease elimination by 2030. In Brazil, the Simplified Neurological Assessment (from Portuguese Avaliação Neurológica Simplificada, ANS) is mandatory for suspected cases; however, the form is still manually fulfilled, which limits the use of data. This study evaluates computer vision models (YOLOv8x, YOLO11x, Faster R-CNN) for detecting hand and foot sensitivity regions from ANS forms. All models were evaluated based on precision, recall, mean average precision (mAP) and confusion matrix. YOLO variants achieved over 94% precision and 84% recall across all classes. Automating ANS data extraction can facilitate the creation of structured datasets, enhancing disease monitoring and enabling the train of predictive models.

A malária é uma doença endêmica na Amazônia Legal e os esforços para combatê-la precisam considerar a dinâmica de infecção-notificação de casos importados — quando a notificação não ocorre no mesmo local da infecção. Esse tipo de análise pode ser facilitado por visualizações interativas de dados organizadas como dashboards. No entanto, poucos trabalhos propuseram dashboard para análise de dados de malária, e nenhum deles se concentra na análise da dinâmica de infecção-notificação dessa doença. Este trabalho apresenta o desenvolvimento e a avaliação de um dashboard focado na análise da dinâmica geolocalizada de infecção-notificação de malária. O dashboard foi validado por meio de casos de uso com dados reais e feedback de um especialista do domínio. Os resultados sugerem que a ferramenta é útil para encontrar padrões em dados de malária e pode ajudar as autoridades a cooperar e distribuir melhor os recursos de saúde entre municípios vizinhos.

PURPOSE:This study aims to evaluate the performance of machine learning models using different data imputation techniques in different balancing scenarios, employing sociodemographic attributes and maternal health history, using data of a population from the state of Pernambuco, Brazil, to predict fetal death during pregnancy. METHODS:We used a dataset from a social program in Pernambuco, Brazil, covering the period from 2008 to 2022, that includes sociodemographic, prenatal, maternal and family health history data. We separated two scenarios with two balancing techniques to train the models, Random Undersampling (RU scenario) and Hybrid Undersampling 2x (H2X scenario) and we explored using four tree-based machine learning models, each of which was evaluated based on their performance and feature importance. RESULTS:The models were evaluated under different metrics. The XGBoost model stood out with 81.06% specificity and the Random Forest model stood out with 67.73% sensitivity, in different scenarios. The attributes that most impacted the learning process were first prenatal care, age, education and interpregnancy interval. CONCLUSION:This application is particularly valuable in the context of social projects, such as those in Brazil, where innovative solutions can contribute to achieving the SDGs offering a unique perspective on the intersection of technology, healthcare, and social impact.

Purpose: Care and attention during the neonatal period are crucial to preventing negative outcomes. The literature presents artificial intelligence models as promising tools to assist healthcare professionals in disease prediction and support clinical decision-making. Methods: This study conducts a bibliometric review of the use of artificial intelligence models in predicting neonatal diseases, conditions and mortality. The review analyzed publications from 2014 to 2024. A total of 629 studies were selected after applying selection criteria. Subsequently, analyses of collaboration networks, keyword co-occurrence, citations and cluster analysis were performed. Results: The results show that the United States, China and the United Kingdom lead scientific production and international collaborations. 12 neonatal diseases were identified, with emphasis on “retinopathy of prematurity”, “necrotizing enterocolitis” and “bronchopulmonary dysplasia”; 7 clinical conditions, including “prematurity”, “perinatal asphyxia” and “jaundice”; and 5 neonatal outcomes, mainly “sepsis”, “mortality” and “cerebral palsy.” Cluster analysis revealed that studies predominantly use clinical, laboratory, genetic and imaging data, with Logistic Regression, Random Forest and Convolutional. Conclusion: The study has growing interest in applying artificial intelligence to neonatal care. The models are increasingly used with clinical, laboratory, genetic and imaging data, enabling earlier and more accurate diagnoses. However, the study also underscores important ethical considerations, such as data quality, algorithmic transparency and equitable access to these technologies, particularly in underrepresented regions, with scientific production uneven and limited participation from low- and middle-income countries.

Changes in telecommunication services demand the development of a new infrastructure to attend new network applications' requirements. The traditional approach to network functions running over dedicated equipment can no longer handle all the dynamics of these new services. The network function virtualization (NFV) paradigm decouples a function from the underlying dedicated hardware thus making networks more flexible and agile. A set of virtual network functions (VNFs) can be deployed as virtual machines or containers across common servers, and orchestrated to compose a service function chain (SFC).Despite the many benefits of NFV, it raises several challenges. SFC placement is a complex task, since it requires taking into consideration the characteristics of VNFs, the SFC requirements, and the state of network infrastructure. This poses a challenge for large scale networks. Information regarding network resources are stored in a large database, and retrieving such data in order to perform SFC placement according to some strategy can be a problem. Limited memory capacity and the presence of a large number of disk operations can compromise the performance of SFC placement algorithms and make it unfeasible. In addition, the amount of memory available to run the allocation algorithm may not be sufficient to load the large amount of information that describe the network resources (occasionally in the hundreds of gigabytes). We address this problem by using a cluster based solution that stores and retrieves data for large scale infrastructures in order to perform SFC placement. The results demonstrate that the use of clusters in the preprocessing step can drastically reduce the size of the resulting database, as well as the execution time to select candidate nodes for a scalable SFC allocation.

Virtualization platforms like Proxmox VE, which extend hypervisors such as KVM with orchestration tools, are increasingly adopted in High-performance computing (HPC) for improved flexibility and resource management. This study evaluates Proxmox VE against KVM and bare-metal environments to quantify virtualization overheads in CPU-bound and inter-process communication (IPC) workloads. Using HPL and NetPIPE benchmarks, competitive runs show Proxmox VE's 0.5%-1.1% compute overhead versus bare-metal (KVM: 0.9%-1.2%), rising to 1.8%-2.0% for cooperative MPI-based HPL. Proxmox VE reduces RAM usage by 15%-20% over KVM via dynamic ballooning. Intra-VM IPC achieves native performance ( 120 Gbps, < 0.005 mu s), while intra-host IPC sustains 50 Gbps (vs. KVM's 60 Gbps) with 20%-30% latency increases. Inter-host IPC under high contention suffers 30% bandwidth loss and 25%-30% latency degradation. CPU utilization remains stable ( 50%) across platforms. Results demonstrate Proxmox VE's viability for CPU-bound/single-host HPC, with network optimizations needed for distributed workflows.

Low birth weight (LBW) is a health condition that affects over 20 million gestational outcomes worldwide. The current literature indicates that machine learning models have the potential to assist healthcare professionals in predicting LBW and giving them the opportunity to intervene earlier in the pregnancy, which might include adjusting medical treatments or suggesting changes in diet. This study proposes the evaluation of machine learning models to predict which pregnant women are at risk of neonatal outcomes with LBW. The methodology involves six phases, including data analysis and attribute selection through different techniques, which generated four distinct scenarios. We used five machine learning models and validated them through cross-validation and hyper-parameter optimization and evaluated their performance considering seven distinct metrics and statistical analysis, focusing on the effectiveness of the models in predicting LBW. The results revealed that the models achieved varying levels of performance across the scenarios, with the removal of duplicate data resulting in improvements in recall (0.83) and f1-score (0.64). Statistical analysis confirmed significant differences (p < 0.05) among most models. The conclusions of this study indicate that the removal of duplicate data and careful attribute selection positively influenced the performance of the machine learning models in predicting low birth weight. Additionally, the analysis of attribute importance highlighted socio-demographic characteristics and gestational history as the most influential in the training of the models.

Leprosy, or Hansen's disease, is a Neglected Tropical Disease (NTD) caused by Mycobacterium leprae that mainly affects the skin and peripheral nerves, causing neuropathy to varying degrees. It can result in physical disabilities and functional loss and is particularly prevalent amongst the most vulnerable populations in tropical and subtropical regions worldwide. The persistent stigma and social exclusion associated with leprosy complicate eradication efforts exacerbate the wider challenges faced by NTDs in sourcing the necessary resources and attention for control and elimination. The introduction of Multidrug Therapy (MDT) significantly lowers the global disease burden. Despite this breakthrough in the treatment of leprosy, over 200,000 new leprosy cases are reported annually across more than 120 countries, emphasizing the need for ongoing detection and management efforts. Artificial Intelligence (AI) has the potential to transform leprosy care by accelerating early detection, improving accurate diagnosis, and enabling predictive modeling to improve the quality for those affected. The potential of AI to provide information to assist healthcare professionals in interventions that reduce the risk of disability, and consequently stigma, particularly in endemic regions, presents a promising path to reducing the incidence of leprosy and improving integration social status of patients. This systematic literature review (SLR) examines the state of the art in research on the use of AI for leprosy care. From an initial 657 works from six scientific databases (ACM Digital Library, IEEE Xplore, PubMed, Scopus, Science Direct and Springer), only 30 relevant works were identified, after analysis of three independent reviewers. We have excluded works due duplication, couldn't be retrieved and quality assessment. Results show that current research is focused primarily on the identification of symptoms using image based classification using three main techniques, neural networks, convolutional neural networks, and support vector machines; a small number of studies focus on other thematic areas of leprosy care. A comprehensive systematic approach to research on the application of AI to leprosy care can make a meaningful contribution to a leprosy-free world and help deliver on the promise of the Sustainable Development Goals (SDG).

Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal deaths. In this work, we evaluate machine learning models for predicting premature birth using Brazilian sociodemographic and obstetric data, focusing on the challenge of data imbalance, a common problem that can lead to biased predictions. We evaluate five data balancing techniques: Undersampling, Oversampling, and three Hybridsampling configurations where the minority class was increased by factors 2, 3, and 4. The machine learning models, including Decision Tree, Random Forest, and AdaBoost, are trained and evaluated on a dataset of over 483,000 cases. The use of the Hybridsampling approach resulted in an accuracy of 70%, a recall of 64%, and a precision of 74% in the Decision Tree model. Results show that Hybridsampling techniques significantly improves models' performance compared to Undersampling and Oversampling, highlighting the importance of a proper data balancing in predictive models for preterm birth. The relevance of our work is particularly significant for the Brazilian Unified Health System (SUS). By improving the accuracy of premature birth predictions, our models could assist healthcare providers in identifying at-risk pregnancies earlier, allowing for timely interventions. This integration could enhance maternal and neonatal care, reduce the incidence of preterm births, and potentially decrease neonatal mortality, especially in underserved regions.