基于CiteSpace的国内术后并发症预测模型研究的可视化分析-东南大学学报医学版

基于CiteSpace的国内术后并发症预测模型研究的可视化分析

单位：1. 河北大学附属医院麻醉科, 河北保定 071002;
2. 河北大学管理学院, 河北保定 071002

分类号：R619

出版年·卷·期（页码）：2024·43·第五期（669-677）

摘要：

目的: 通过使用CiteSpace对国内关于术后并发症预测模型的研究进行可视化分析,对国内术后并发症预测模型的研究现状进行分析,为未来国内术后并发症预测模型的研究提供参考和方向。方法: 应用CiteSpace知识图谱可视化工具,基于中国知网2014年1月1日至2024年6月27日关于术后并发症预测模型的研究文献,统计年发文量,绘制作者合作网络图谱,并根据关键词共线、聚类、时序、突现图谱对术后并发症研究模型主题下的研究热点进行分析,探究该主题研究的演进过程,分析发展趋势及意义。结果: 通过使用CiteSpace绘制的图谱及表格分析得出,术后并发症预测模型的研究在未来将会成为热门研究方向。结论: 以不同疾病、不同手术方式、不同特征的患者为基点使用机器学习构建精细化的术后并发症预测模型或将成为研究热点,研究者应基于多种机器学习方法进行多中心、大样本、高质量的前瞻性研究,并将构建的预测模型尝试应用于临床实践以构建本土化、个体化的术后并发症预测模型。

Objective: By using CiteSpace to conduct the visual analysis of the domestic research of the postoperative complications prediction model, and analyze the research status of the domestic postoperative complications prediction model, so as to provide reference and direction for the future research of the domestic postoperative complications prediction model. Methods: This paper apply CiteSpace knowledge graph visualization tool, based on the CNKI from January 1, 2014 to June 27, 2024 for postoperative complications prediction model research literature, counted annual publications, drew the author cooperation network map, and according to the keywords collinear, clustering, timing, emergent map of postoperative complications research model under the topic hot analysis, explored the evolution of the subject research process, analyzed the development trend and significance. Results: The map and table analysis using CiteSpace showed, the study of postoperative complication prediction model would become a popular research direction in the future. Conclusion: With different diseases, different surgical methods, different characteristics of the machine learning to build refined postoperative complications prediction model or will become a research hotspot. Researchers should base on various machine learning methods to conduct multicenter, large samples, high quality prospective study, and try to apply the prediction model to clinical practice to build localization, individualized postoperative complications prediction model.

参考文献：

[1] TANG B,WANG X T,CHEN W J,et al.Experts consensus on the management of delirium in critically ill patients[J].Zhonghua Nei Ke Za Zhi,2019(2):108-118.
[2] TEVIS S E,KENNEDY G D.Postoperative complications and implications on patient-centered outcomes[J].J Surg Res,2013,181(1):106-113.
[3] ENDO I,KUMAMOTO T,MATSUYAMA R.Postoperative complications and mortality:are they unavoidable?[J].Ann Gastroenterol Surg,2017,1(3):160-163.
[4] COOK J L.Surgical complications[J].Dermatologic Therapy,2011,24(6):513-514.
[5] BHATTACHARYA K,BHATTACHARYA N.Surgeon's guilt after postoperative complication[J].Pol Przegl Chir 2022,94(4):45-48.
[6] MASON J A,FRIEDMAN E E,ROJAS J C,et al.No-show prediction model performance among people with hiv:external validation study[J].J Med Internet Res,2023,25:e43277.
[7] 陈悦,陈超美,刘则渊,等.CiteSpace知识图谱的方法论功能[J].科学学研究,2015,33(2):242-253.
[8] 张紫卉,杨小会,胡庆元,等.基于CiteSpace的国内人工智能医疗器械研究的可视化分析[J].中国医疗设备,2024,39(6):88-95.
[9] 孙松蔚,高玉芳,王刚,等.中长静脉导管研究热点的可视化分析[J].中华急危重症护理杂志,2022,3(1):67-72.
[10] 白胶胶,张欢,侯妍,等.基于CiteSpace可视化分析国内脊髓损伤并发低钠血症的研究热点与趋势[J].现代医学,2024,52(1):121-126.
[11] 屈睿升,宋润泽,刘宝坤,等.基于CiteSpace的近十年下腔静脉滤器领域文献计量可视化分析[J].临床放射学杂志,2022,41(2):334-339.
[12] LIU F,HUANG C,XU Z,et al.Morbidity and mortality of laparoscopic vs open total gastrectomy for clinical stageⅠgastric cancer:the CLASS02 multicenter randomized clinical trial[J].JAMA Oncol,2020,6(10):1590-1597.
[13] TOKUNAGA M,KUROKAWA Y,MACHIDA R,et al.Impact of postoperative complications on survival outcomes in patients with gastric cancer:exploratory analysis of a randomized controlled JCOG1001 trial[J].Gastric Cancer,2021,24(1):214-223.
[14] 彭一耘,杨国渊,黄亚龙等.临床预测模型在胃癌术后并发症中应用的研究进展[J].中国普外基础与临床杂志,2024(5):619-624.
[15] ZHOU Z R,WANG W W,LI Y,et al .In-depth mining of clinical data:the construction of clinical prediction model with R[J].Ann Transl Med,2019,7(23):796.
[16] WILMORE D W,KEHLET H.Management of patients in fast track surgery[J].BMJ,2001,322(7284):473-476.
[17] NICHOLSON A,LOWE M C,et al.Systematic review and meta-analysis of enhanced recovery programmes in surgical patients[J].Br J Surg,2014,101(3):172-188.
[18] CHOI R Y,COYNER A S,KALPATHY-CRAMER J,et al.Introduction to machine learning,neural networks,and deep learning[J].Transl Vis Sci Technol,2020,9(2):14.
[19] DEO R C.Machine learning in medicine[J].Circulation,2015,132(20):1920-1930.
[20] HANDELMAN G S,KOK H K,CHANDRA R V,et al.eDoctor:machine learning and the future of medicine[J].J Int Med,2018,284(6):603-619.
[21] 浦洁,周庆,侯小会,等.机器学习在构建危重患者病情变化预测模型中的研究进展[J].当代护士,2023,30(4):31-34.
[22] MATTISON M L P.Delirium[J].Ann Intern Med,2020,173(7):ITC49-ITC64.
[23] 郭畔旭,阳晓娟,马跃,等.基于机器学习的术后患者谵妄风险预测模型研究进展[J].当代护士,2024,31(18):11-14.

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