SynthesisBiomedical engineering online2021
Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.
Synthesis in Biomedical engineering online, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Computational methods applied to syphilis: where are we, and where are we going?Frontiers in public health · 2023Pooled it
- Electronic health records in Brazil: Prospects and technological challenges.Frontiers in public health · 2022Pooled it
- National registry for amyotrophic lateral sclerosis: a systematic review for structuring population registries of motor neuron diseases.BMC neurology · 2021Pooled it
- Magnetic Resonance Neuroimaging in Amyotrophic Lateral Sclerosis: A Comprehensive Umbrella Review of 18 Studies.Brain sciences · 2025Review
- Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2025Review
- Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.Journal of clinical medicine · 2023Review
- Digital health solution for monitoring and surveillance of Amyotrophic Lateral Sclerosis in Brazil.Frontiers in public health · 2023Article
- A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis.Open life sciences · 2022Review
- Translational research in health technologies: A scoping review.Frontiers in digital health · 2022Article
- Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases.Frontiers in neurology · 2021Review
- ALSGate: An Efficient Gated Mixture-of-Experts Model for Reliable ALS Detection Using EMG Signals.Healthcare technology lettersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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.
methodsThis 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%).
conclusionsDistinct 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.