Lin Cheng-Pei, Chen Lu-Yen Anny
PhD, RN, Assistant Professor, Institute of Community Health Care, National Yang Ming Chiao Tung University, Taiwan, ROC.
PhD, RN, Assistant Professor, Institute of Clinical Nursing, National Yang Ming Chiao Tung University, Taiwan, ROC.
Hu Li Za Zhi. 2024 Oct;71(5):14-20. doi: 10.6224/JN.202410_71(5).03.
In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.
近年来，人工智能的快速发展提高了医疗服务效率、疾病预测准确性，并推动了医疗行业的创新。在众多进展中，机器学习已成为各个领域的发展焦点。尽管其在护理研究和临床护理中的应用一直有限，但技术进步预示着机器学习未来在这些领域将有更广泛的应用。在本文中，作者探讨了机器学习在护理研究和护理中的应用。首先，介绍了机器学习的类型和分类。接下来，描述并分析了常见的神经机器学习模型，包括循环神经网络、变换器和自然语言处理。随后，探讨了机器学习的原理和步骤，并与传统统计方法进行比较，突出了机器学习模型使用的质量监测策略以及使用机器学习的潜在局限性和挑战。最后，鼓励跨学科合作，以在信息技术和护理学科之间共享知识，分析各种分析模型的优缺点，不断审视研究过程，并反思方法学上的局限性。遵循这一过程，有助于最大限度地发挥基于人工智能的技术在推动护理研究创新和进步方面的潜力。
