越来越多,信息技术促进了对风险分析和事件预测有用的数据的存储和管理。与职业健康和安全相关的数据提取研究越来越多;然而,由于其可变性,建筑业值得特别关注。这项审查是在国家职业意外保险研究所(Inail)的研究计划下进行的。
目标:研究问题的重点是确定哪些数据挖掘(DM)方法,在监督中,无人监督,和其他人,最适合某些调查目标,类型,和数据来源,由作者定义。
方法:Scopus和ProQuest是我们提取建筑领域研究的主要来源,2014年至2023年出版。选择研究的资格标准基于系统评价和荟萃分析的首选报告项目(PRISMA)。出于探索目的,我们应用了层次聚类,而为了深入分析,我们使用主成分分析(PCA)和荟萃分析。
结果:基于PRISMA资格标准的搜索策略为我们提供了2234篇潜在文章中的63篇,206项意见,89种方法,4调查目的,3个数据源,7种数据类型,和3种资源类型。聚类分析和PCA将论文数据集中的信息分为两个维度和标签:“监督方法,机构数据集,以及预测和分类目的“(相关性0.97-8.18×10-1;p值7.67×10-55-1.28×10-22)和第二个,Dim2“非监督方法;项目,模拟,文学,文本数据;监控,决策过程;机械与环境\“(Corr.0.84-0.47;p值5.79×10-25-3.59×10-6)。我们回答了关于哪种方法的研究问题,在监督中,无人监督,或其他,最适合应用于建筑行业的数据。
结论:荟萃分析提供了监督方法(赔率比=0.71,置信区间0.53-0.96)比非监督方法更好的有效性的总体估计。
Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail).
OBJECTIVE: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors.
METHODS: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis.
RESULTS: The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: \"supervised methods, institutional dataset, and predictive and classificatory purposes\" (correlation 0.97-8.18 × 10-1; p-value 7.67 × 10-55-1.28 × 10-22) and the second, Dim2 \"not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment\" (corr. 0.84-0.47; p-value 5.79 × 10-25--3.59 × 10-6). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry.
CONCLUSIONS: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53-0.96) compared to not-supervised methods.