Research hotspots and trends of artificial intelligence applied in elderly delirium based on CiteSpace
10.3760/cma.j.cn115682-20240816-04601
- VernacularTitle:基于CiteSpace的人工智能在老年谵妄领域中应用的研究热点及趋势
- Author:
Shan ZHANG
1
;
Lu LIU
;
Shu DING
;
Ying WU
Author Information
1. 首都医科大学护理学院，北京 100069
- Publication Type:Journal Article
- Keywords:
Aged;
Artificial intelligence;
Delirium;
Visual analysis;
Research hotspots
- From:
Chinese Journal of Modern Nursing
2025;31(19):2594-2600
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the research hotspots and trends of artificial intelligence in elderly delirium, and to provide reference for clinical healthcare professionals.Methods:The Web of Science Core Collection database was used as the data source to obtain literature on "artificial intelligence" and "elderly delirium". The search period was from January 1, 2017 to July 29, 2024. CiteSpace software was used for visual analysis of the included literature.Results:A total of 99 articles were included, with an overall upward trend in the number of publications. The United States had the most publications ( n=43), followed by China ( n=25). The institution with the most publications was Harvard Medical School ( n=8), followed by Sichuan University ( n=7). Journal of the American Geriatrics Society was the most co-cited journal. Based on the keywords, research hotspots mainly focused on the construction and validation of the risk prediction model of elderly delirium, intelligent diagnosis of elderly delirium, and intelligent elderly delirium nursing program. Future studies could explore reliability, ICU and validity. Conclusions:Artificial intelligence is increasingly being researched in elderly delirium to facilitate personalized nursing programs for elderly delirium in all aspects, from disease prediction, risk assessment, early diagnostic monitoring to intervention strategy development. In the future, trends in artificial intelligence research in the field of elderly delirium will revolve around improving the reliability of diagnosis, spreading its use in ICU, and optimizing predictive models.
