Berger Anne M, Berger Charles R
Southern New Hampshire Medical Center, Nashua, NH, USA.
Comput Inform Nurs. 2004 May-Jun;22(3):123-31. doi: 10.1097/00024665-200405000-00006.
The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.
在过去二十年中，各学科收集和存储数据的能力都以惊人的速度增长。医疗保健领域也不例外。向循证实践和结果研究的转变为从海量临床数据中提取有意义的信息带来了重大机遇和挑战，以便将其转化为可用于指导护理实践的最佳知识。数据挖掘是数据库知识发现过程中的一个步骤，是一种从大型数据集中挖掘信息的方法。基于统计分析、人工智能和机器学习技术，数据挖掘可以分析海量数据，并提供有关数据中存在的模式和关系的有用且有趣的信息，否则这些信息可能会被遗漏。作为领域专家，护士研究人员处于理想位置，可以利用这一经过验证的技术，将现有数据存储库中的可用信息转化为有用且易于理解的知识，以指导护理实践，并促进积极的跨学科合作与研究。
