首页|National and Kapodistrian University of Athens Reports Findings in Machine Learn ing (The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem R esistance: A Comprehensive Review)
National and Kapodistrian University of Athens Reports Findings in Machine Learn ing (The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem R esistance: A Comprehensive Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Machine Learning is th e subject of a report. According to newsreporting from Athens, Greece, by NewsR x journalists, research stated, "Carbapenem resistance poses asignificant threa t to public health by undermining the efficacy of one of the last lines of antib iotic defense.Addressing this challenge requires innovative approaches that can enhance our understanding and abilityto combat resistant pathogens."The news correspondents obtained a quote from the research from the National and KapodistrianUniversity of Athens, "This review aims to explore the integration of machine learning (ML) and epidemiologicalapproaches to understand, predict, and combat carbapenem-resistant pathogens. It examines howleveraging large dat asets and advanced computational techniques can identify patterns, predict outbr eaks,and inform targeted intervention strategies. The review synthesizes curren t knowledge on the mechanismsof carbapenem resistance, highlights the strengths and limitations of traditional epidemiological methods,and evaluates the trans formative potential of ML. Real-world applications and case studies are used todemonstrate the practical benefits of combining ML and epidemiology. Technical a nd ethical challenges,such as data quality, model interpretability, and biases, are also addressed, with recommendations providedfor overcoming these obstacle s. By integrating ML with epidemiological analysis, significant improvementscan be made in predictive accuracy, identifying novel patterns in disease transmiss ion, and designing effectivepublic health interventions. Case studies illustrat e the benefits of interdisciplinary collaboration intackling carbapenem resista nce, though challenges such as model interpretability and data biases must bema naged. The combination of ML and epidemiology holds great promise for enhancing our capacity topredict and prevent carbapenem-resistant infections. Future rese arch should focus on overcoming technicaland ethical challenges to fully realiz e the potential of these approaches."
AthensGreeceEuropeCyborgsEmergin g TechnologiesEpidemiologyHealth and MedicineMachine LearningPublic Health
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