PDF(Pubmed)
Abstract：
In the research published in the World Journal of Clinical Cases, Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness (ICU-AW) utilizing advanced machine learning methodologies. The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW, focusing on critical variables such as ICU stay duration and mechanical ventilation. This research marks a significant advancement in applying machine learning to clinical diagnostics, offering a new paradigm for predictive medicine in critical care. It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.
摘要：
在《世界临床病例杂志》上发表的这项研究中，Wang和Long利用先进的机器学习方法进行了定量分析，以描绘重症监护病房获得性虚弱（ICU-AW）的风险因素。该研究采用多层感知器神经网络来准确预测ICU-AW的发生率，重点关注ICU住院时间和机械通气等关键变量。这项研究标志着将机器学习应用于临床诊断的重大进展。为重症监护中的预测医学提供了新的范例。它强调了在临床实践中整合人工智能技术以增强患者管理策略的重要性，并呼吁跨学科合作以推动医疗保健创新。
