首页|Studies from Pontifical Catholic University in the Area of Machine Learning Desc ribed (Craving for a Robust Methodology: a Systematic Review of Machine Learning Algorithms On Substance-use Disorders Treatment Outcomes)
Studies from Pontifical Catholic University in the Area of Machine Learning Desc ribed (Craving for a Robust Methodology: a Systematic Review of Machine Learning Algorithms On Substance-use Disorders Treatment Outcomes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting originating from Porto Alegre, Brazil, by NewsR x correspondents, research stated, "Substance use disorders (SUDs) pose signific ant mental health challenges due to their chronic nature, health implications, i mpact on quality of life, and variability of treatment response. This systematic review critically examines the application of machine learning (ML) algorithms in predicting and analyzing treatment outcomes in SUDs." Financial supporters for this research include Aarhus Universitet, Conselho Naci onal de Desenvolvimento Cientifico e Tecnologico (CNPQ), Coordenacao de Aperfeic oamento de Pessoal de Nivel Superior (CAPES), National Institutes of Health (NIH ) - USA. Our news editors obtained a quote from the research from Pontifical Catholic Uni versity, "Conducting a thorough search across PubMed, Embase, Scopus, and Web of Science, we identified 28 studies that met our inclusion criteria from an initi al pool of 362 articles. The MI-CLand CHARMS instruments were utilized for metho dological quality and bias assessment. Reviewed studies encompass an array of SU Ds, mainly opioids, cocaine, and alcohol use, predicting outcomes such as treatm ent adherence, relapse, and severity assessment. Our analysis reveals a signific ant potential of ML models in enhancing predictive accuracy and clinical decisio n-making in SUD treatment. However, we also identify critical gaps in methodolog ical consistency, transparency, and external validation among the studies review ed."
Porto AlegreBrazilSouth AmericaAlg orithmsCyborgsEmerging TechnologiesMachine LearningPontifical Catholic U niversity
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