Abstract
This paper presents a comprehensive exploration of the use of machine learning (ML) in identifying risk factors for adverse events in healthcare. It delves into the challenges and opportunities associated with ML techniques, emphasizing the potential impact of ML-based risk factor identification on clinical decision-making. The paper also discusses the need for interdisciplinary collaboration and continuous innovation to maximize the potential of ML in enhancing patient safety and healthcare quality. By examining various ML techniques, challenges in utilization, opportunities and impact, interdisciplinary collaboration, and future prospects, this paper provides valuable insights for researchers, healthcare professionals, and policymakers.
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