Introduction: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease.Methods: This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions.Discussions: Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%).Conclusions: Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.

(1) Background: With the increasing digitalization of healthcare systems, data security and privacy have become crucial issues. In parallel, blockchain technology has gradually proven to be an innovative solution to address this challenge, as its ability to provide an immutable and secure record of transactions offers significant promise for healthcare information management. This systematic review aims to explore the applications of blockchain in health information systems, highlighting its advantages and challenges. (2) Methods: The publications chosen to compose this review were collected from six databases, resulting in the initial identification of 4864 studies. Of these, 73 were selected for in-depth analysis. (3) Results: The main results show that blockchain has been used mainly in electronic health records (63%). Furthermore, it was used in the Internet of Medical Things (8.2%) and for data sharing during the COVID-19 pandemic (6.8%). As advantages, greater security, privacy, and data integrity were identified, while the challenges point to the need for standardization and regulatory issues. (4) Conclusions: Despite the difficulties encountered, blockchain has significant potential to improve healthcare data management. However, more research and continued collaboration between those involved are needed to maximize its benefits.

Background: This article comprises a systematic review of the literature that aims at researching and analyzing the frequently applied guidelines for structuring national databases of epidemiological surveillance for motor neuron diseases, especially Amyotrophic Lateral Sclerosis (ALS). Methods: We searched for articles published from January 2015 to September 2019 on online databases as PubMed - U.S. National Institutes of Health’s National Library of Medicine, Scopus, Science Direct, and Springer. Subsequently, we analyzed studies that considered risk factors, demographic data, and other strategic data for directing techno-scientific research, calibrating public health policies, and supporting decision-making by managers through a systemic panorama of ALS. Results: 2850 studies were identified. 2400 were discarded for not satisfying the inclusion criteria, and 435 being duplicated or published in books or conferences. Hence, 15 articles were elected. By applying quality criteria, we then selected six studies to compose this review. Such researches featured registries from the American (3), European (2), and Oceania (1) continent. All the studies specified the methods for data capture and the patients’ recruitment process for the registers. Discussions: From the analysis of the selected papers and reported models, it is noticeable that most studies focused on the prospect of obtaining data to characterize research on epidemiological studies. Demographic data (ID01) are present in all the registries, representing the main collected data category. Furthermore, the general health history (ID02) is present in 50% of the registries analyzed. Characteristics such as access control, confidentiality and data curation. We observed that 50% of the registries comprise a patient-focused web-based self-report system. Conclusion: The development of robust, interoperable, and secure electronic registries that generate value for research and patients presents itself as a solution and a challenge. This systematic review demonstrated the success of a population register requires actions with well-defined development methods, as well as the involvement of various actors of civil society.

Introduction: Brazil has one of the largest prison populations globally, with over 682,000 imprisoned people. Prison health is a public health emergency as it presents increasingly aggravating disease rates, mainly sexually transmitted infections (STI). And this problem already affects both developed and developing nations. Therefore, when thinking about intervention strategies to improve this scenario in Brazil, the course “Health Care for People Deprived of Freedom” (ASPPL), aimed at prison health, was developed. This course was implemented in the Virtual Learning Environment of the Brazilian Health System (AVASUS). Given this context, this study analyzed the aspects associated with massive training through technological mediation and its impacts on prison health.Methods: This cross-sectional study analyzed data from 8,118 ASPPL course participants. The data analyzed were collected from six sources, namely: (i) AVASUS, (ii) National Registry of Health Care Facilities (CNES), (iii) Brazilian Occupational Classification (CBO), (iv) National Prison Department (DEPEN); (v) Brazilian Institute of Geography and Statistics (IBGE); and the (iv) Brazilian Ministry of Health (MoH), through the Outpatient Information System of the Brazilian National Health System (SIA/SUS). A data processing pipeline was conducted using Python 3.8.9.Results: The ASPPL course had 8,118 participants distributed across the five Brazilian regions. The analysis of course evaluation by participants who completed it shows that 5,190 (63.93%) reported a significant level of satisfaction (arithmetic mean = 4.9, median = 5, and standard deviation = 0.35). The analysis revealed that 3,272 participants (40.31%) are health workers operating in distinct levels of care. The prison system epidemiological data shows an increase in syphilis diagnosis in correctional facilities.Conclusions: The course enabled the development of a massive training model for various health professionals at all care levels and regions of Brazil. This is particularly important in a country with a continental size and a large health workforce like Brazil. As a result, social and prison health impacts were observed.

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.