Zhang Rui, Ge Yingying, Xia Lu, Cheng Yun
Department of Nursing, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, People's Republic of China.
Department of Nursing, Fudan University, Shanghai, 200433, People's Republic of China.
J Multidiscip Healthc. 2024 Apr 10;17:1561-1575. doi: 10.2147/JMDH.S459079. eCollection 2024.
With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.
To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.
Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.
According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of , author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.
Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.
随着大数据时代的到来,医院信息系统和移动护理系统等产生了海量的医学数据。数据挖掘作为一种强大的信息处理技术,能够通过处理大规模数据并进行多维度分析来发现不明显的信息。如何从数据库中挖掘有效信息并将其应用于护理临床实践,已越来越受到护理研究者的关注。
回顾护理领域中有关数据挖掘的文献,梳理研究现状,识别热点问题,突出研究趋势,并就数据挖掘技术未来在护理领域的应用提出建议。
从科学引文索引核心合集获取2002年至2023年发表的护理领域数据挖掘相关文献。利用CiteSpace对文献数量、国家/地区、机构、期刊、作者及关键词进行分析。
研究结果显示,护理领域数据挖掘的进展速度并不理想。护理数据挖掘研究以美国和中国为主导。然而,在护理数据挖掘领域尚未形成稳定的核心作者群体或组织。21世纪以来,护理领域的数据挖掘研究逐渐增多,但总体数量不多。哥伦比亚大学、《》期刊、作者戴安娜·J·威尔基、穆罕默德·卡姆兰·洛迪、姚英伟在护理数据挖掘研究中最具影响力。护理数据挖掘研究者目前主要关注电子健康记录、文本挖掘、机器学习和自然语言处理。护理领域数据挖掘未来的研究主题主要包括护理信息学和临床护理质量提升。
研究数据表明,数据挖掘为护理学科的发展提供了更多视角,推动了该学科的发展,但同时也带来了一系列新问题,需要研究者去解决。