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.

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.