Objective:To systematically analyze the status quo, research hotspots and development trends of symptom cluster of breast cancer patients at home and abroad.Methods:We searched the PubMed, Embase, Web of Science, China National Knowledge Infrastructure, WanFang Data, VIP and China Biomedical Literature Database by computer, and screened the original studies on symptom clusters of breast cancer patients at home and abroad from the establishment of the database to October 31, 2021. Endnote, Excel and VOSviewer were used to conduct bibliometric analysis on the distribution characteristics, development trends and research hotspots of the included research. The content analysis method was used to summarize and analyze the symptom evaluation tools, cluster methods and common symptom clusters.Results:A total of 76 articles were included, and the results of symptom cluster were quite different. The most common symptom cluster in the cluster study of presupposition method was composed of fatigue, sleep disorder and other symptoms. The most common symptom cluster in the cluster study of non-prediction method was the gastrointestinal symptom cluster.Conclusions:The number of symptom cluster studies of breast cancer patients is on the rise, but it is still in the initial stage of development. The number of related studies is small, the cluster method is not uniform, and there is a lack of specific symptom evaluation tools for patients with breast cancer. In the future, we can develop specific symptom evaluation tools and carry out longitudinal research to explore symptom clusters with clinical application value, so as to develop targeted and effective symptom improvement programs.

Artificial intelligence has shown broad application prospects in the field of nursing management, and is expected to become an important breakthrough point in improving the efficiency and level of nursing management. This study reviewed the current research status of artificial intelligence in the field of nursing management, summarized the progress of artificial intelligence in nursing personnel scheduling, disease risk management and optimizing nursing management processes, analyzed the opportunities and challenges of artificial intelligence application in nursing management, and provides reference for the development and application of artificial intelligence in nursing management in the future.