[1]李 轩,王子为,赵靖萱,等.肿瘤预后预测领域机器学习应用的文献计量与热点可视化分析[J].医学信息,2022,35(09):90-94.[doi:10.3969/j.issn.1006-1959.2022.09.023]
　LI Xuan,WANG Zi-wei,ZHAO Jing-xuan,et al.Visualization Analysis on Bibliometrics and Hot Spots of Machine Learning Applications in the Field of Tumor Prognosis Prediction[J].Medical Information,2022,35(09):90-94.[doi:10.3969/j.issn.1006-1959.2022.09.023]
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肿瘤预后预测领域机器学习应用的文献计量与热点可视化分析()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
35卷
期数:
2022年09期
页码:
90-94
栏目:
论著
出版日期:
2022-05-01

文章信息/Info

Title:
Visualization Analysis on Bibliometrics and Hot Spots of Machine Learning Applications in the Field of Tumor Prognosis Prediction
文章编号:
1006-1959（2022）09-0090-05
作者:
李 轩; 王子为; 赵靖萱; 等
（中国医科大学创新学院1，中英联合学院2，健康管理学院3，辽宁 沈阳 110122）
Author(s):
LI Xuan; WANG Zi-wei; ZHAO Jing-xuan; et al.
(Innovation Institute1,China Medical University-The Queen’s University of Belfast Joint College2,College of Health Management3,China Medical University,Shenyang 110122,Liaoning,China)
关键词:
肿瘤; 预后; 预测; 机器学习
Keywords:
Tumor; Prognosis; Prediction; Machine learning
分类号:
R730.7
DOI:
10.3969/j.issn.1006-1959.2022.09.023
文献标志码:
A
摘要:
目的 分析肿瘤预后预测领域机器学习应用的现况和研究热点，为后续相关研究提供参考。方法 利用PubMed数据库进行初步检索，检索策略为："machine learning"[MeSH Terms] AND "neoplasms"[MeSH Terms] AND "prognosis"[MeSH Terms]，检索时间范围从PubMed建库至2021年7月，最后检索时间为2021年7月20日。人工筛选应用机器学习进行肿瘤预后预测研究的原始论文，统计每年发文量、主要主题词/副主题词的频次，应用共词双聚类方法分析该领域的研究热点。结果 共获得该领域的原始研究论文838篇，每年论文量呈现指数增长的趋势；纳入论文共标引了1265个主要主题词/副主题词；将频次在15次及以上的39个主要主题词/副主题词作为高频词，对高频词进行双聚类可视化分析，得到6大类相关研究热点。结论 肿瘤预后预测领域机器学习的相关研究快速发展，深度学习在该领域表现出最大的研究热度。但该领域研究目前主要集中在脑肿瘤、肺肿瘤、乳腺肿瘤、前列腺肿瘤等方面，研究人员可以关注其他部位的肿瘤，充分运用机器学习的方法开展后续研究。
Abstract:
Objective To analyze the current status and research hot spots of machine learning applications in the field of tumor prognosis prediction, so as to provide references for subsequent related research.Methods PubMed database was used to conduct a preliminary search. The search strategy was "machine learning"[MeSH Terms] AND "neoplasms"[MeSH Terms] AND "prognosis"[MeSH Terms]. The search time range was from the establishment of PubMed to July, 2021, and the final search time was July 20, 2021. The original papers on the prediction of tumor prognosis by machine learning were manually screened. The annual number of papers, the frequency of main subject words/sub-subject words were counted, and the research hotspots in this field were analyzed by co-word biclustering method.Results A total of 838 original research papers in this field were obtained, and the annual number of papers showed an exponential growth trend. A total of 1265 main subject words/sub-subject words were indexed for the included papers. Thirty-nine main subject words/sub-subject words with a frequency of 15 times or more were selected as th ehigh-frequency words. Finally, bi-clustering visualization analysis on high-frequency words was performed, and 6 related research hot spots were obtained.Conclusion The research on applications of machine learning in the field of tumor prognosis prediction has developed rapidly, and deep learning has shown the greatest research interest in this field. However, the current research in this field are mainly about brain, lung, breast, and prostate tumors. Researchers can focus on tumors in other parts and make full use of machine learning methods to carry out subsequent research.

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更新日期/Last Update: 1900-01-01