Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/100511
Title: Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: A Review
Authors: Papaiz, Fabiano
Dourado, Mario Emílio Teixeira
Valentim, Ricardo Alexsandro de Medeiros
de Morais, Antonio Higor Freire
Arrais, Joel Perdiz 
Keywords: Amyotrophic Lateral Sclerosis; prognosis; Machine Learning; health informatics; literature review
Issue Date: 2022
Project: Brazilian Ministry of Health - Scientific and Technological Development Applied to ALS project, carried out by the Laboratory of Technological Innovation in Health (LAIS), of the Federal University of Rio Grande do Norte. 
Serial title, monograph or event: Frontiers in Computer Science
Volume: 4
Abstract: The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients’ quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities.
URI: http://hdl.handle.net/10316/100511
ISSN: 2624-9898
DOI: 10.3389/fcomp.2022.869140
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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