目的:基于CiteSpace软件绘制知识图谱，进行早发型子痫前期发病预测模型相关研究的可视化分析。方法:检索2004年1月1日-2023年12月31日中国知网(CNKI)、万方(Wanfang)、维普(VIP)、PubMed及Web of Science(WoS)数据库关于早发型子痫前期发病预测模型相关研究，利用CiteSpace软件对文献的作者、机构、关键词进行可视化分析，采用对数似然率(logarithmic likelihood rate，LLR)聚类对中文、英文文献关键词进行聚类分析。结果:共纳入693篇文献，中文文献327篇，英文文献366篇。国内及国外发文量大体均呈上升趋势，英文文献数据库作者及机构合作相对紧密，中文文献数据库作者及机构合作相对分散。中文文献得到2个聚类，为早发型及预测;英文文献得到7个聚类，为DNA甲基化、早孕期筛查、早发型子痫前期、氧化应激、多重免疫测定、胎儿体质量估计及HELLP综合征。突现词分析显示中文文献数据库2019年及以前主要侧重于分析早发型子痫前期疾病临床特点及治疗策略;2020年及之后，着重于用子痫发病传统标志物构建预测模型。英文文献数据库以早发型子痫前期独立性风险因素为重点，不仅明确了母体因素(子痫前期病史、妊娠期高血压及胎儿生长受限等)、传统指标(子宫动脉多普勒超声、生化标志物、血管生长因子及胎盘生长因子等)等临床可靠且实用特异性指标，还尝试从免疫、DNA甲基化、氧化应激等方向着手发掘临床可广泛应用的特异性指标，且在传统统计方法基础上创新性融合机器学习算法构建模型预测早发型子痫前期的发生。结论:目前构建早发型子痫前期发病预测模型仍为国内外的研究热点，所发掘的特异性指标在融合机器学习算法大背景下构建的预测模型的可靠效力有望进一步提升。
Visual Analysis of Correlative Studies on Prediction Model of Early-Onset Preeclampsia Based on Knowledge Graph
Objective:To visually analyze the prediction model of early-onset preeclampsia based on the knowledge map drawn by CiteSpace software.Methods:CNKI,Wanfang,VIP,PubMed and Web of Science(WoS)databases from January 1st,2004 to December 31st,2023,were searched on the relevant research on the prediction model of early-onset preeclampsia.CiteSpace software was used to visually analyze the authors,institutions and keywords of the literature.Logarithmic likelihood rate(LLR)clustering is used to perform cluster analysis on Chinese and English data keywords.Results:A total of 693 articles were included,including 327 articles in Chinese and 366 in English.The number of published studies on the prediction of early-onset preeclampsia at home and abroad was generally on the rise.The cooperation between authors and institutions of the English literature database was relatively close,while the cooperation between authors and institutions of the Chinese literature database was relatively scattered.Two clusters were obtained from Chinese literatures,which were early onset and prediction.Seven clusters were obtained from English literatures.which were DNA methylation,early pregnancy screening,early onset preeclampsia,oxidative stress,multiple immunoassay,fetal body mass estimation and HELLP syndrome.The outburst word analysis showed that the Chinese literature database mainly focused on the analysis of clinical characteristics and treatment strategies of early-onset preeclampsia in 2019 and before,and focused on the construction of prediction models using traditional markers of eclampsia in 2020 and after.The English literature database mainly focused on the independent risk factors of early-onset preeclampsia,not only including maternal factors(history of onset preeclampsia,gestational hypertension,fetal growth restriction,etc.),traditional markers(uterine artery Doppler ultrasound,biochemical markers,vascular growth factor,placental growth factor,etc.)and other clinically reliable and practical specific indicators,but also using some specific indicators of immunity,DNA methylation,oxidative stress that could be widely used in future clinical practice.In this paper,a model was built for predicting the occurrence of early-onset preeclampsia,based on the traditional statistical methods and innovative integration of machine learning algorithms.Conclusions:At present,the construction of predictive models for the onset of early-onset preeclampsia is still a research hotspot at home and abroad.To discover the specific indicators will further enhance the reliability of the predictive models constructed under the background of fusion machine learning algorithms.
万方数据
维普
