地下煤矿工作日损失影响因素因果关系网络
Causal Relationship Network of Risk Factors Impacting Workday Loss in Underground Coal Mines
AI总结：
本研究采用新颖的因果AI方法GGES，基于NIOSH数据建立地下煤矿工作日损失影响因素的因果关系网络，发现总采矿经验为最关键因素，模型性能AP、AR、AHP、AHR分别达0.694、0.653、0.386、0.345。
摘要：
本研究旨在利用一种新颖的因果人工智能（AI）方法，建立导致地下煤矿工作日损失的各因素之间的因果关系网络。分析利用了美国国家职业安全与健康研究所（NIOSH）获得的数据。从NIOSH数据库中提取了1990年至2020年间来自3,982个独特地下煤矿的共计101,010条工伤记录。使用一种名为分组贪婪等价搜索（GGES）的新颖因果AI方法对因果关系进行了分析和可视化。通过干预do-演算调整（IDA）评分评估了每个变量对工作日损失的影响。使用10折交叉验证技术进行了模型训练和验证。采用包括邻接精度（AP）、邻接召回率（AR）、箭头精度（AHP）和箭头召回率（AHR）在内的性能指标来评估模型。研究结果显示，2006年之后，矿工工作日损失的关键直接原因包括总采矿经验、平均办公室员工数、平均地下员工数、县以及总采矿经验（年）。总采矿经验成为影响最大的因素，而每个矿的平均员工数影响最小。分析强调了总采矿经验在决定工作日损失中的重要作用。模型达到了最佳性能，AP、AR、AHP和AHR值分别为0.694、0.653、0.386和0.345。本研究证明了利用新的GGES方法通过分析就业人口统计和工伤记录来阐明工作日损失背后的因果因素并建立其因果关系网络的可行性。
英文摘要：
This study aims to establish the causal relationship network between various factors leading to workday loss in underground coal mines using a novel causal artificial intelligence (AI) method. The analysis utilizes data obtained from the National Institute for Occupational Safety and Health (NIOSH). A total of 101,010 injury records from 3,982 unique underground coal mines spanning the years from 1990 to 2020 were extracted from the NIOSH database. Causal relationships were analyzed and visualized using a novel causal AI method called Grouped Greedy Equivalence Search (GGES). The impact of each variable on workday loss was assessed through intervention do-calculus adjustment (IDA) scores. Model training and validation were performed using the 10-fold cross-validation technique. Performance metrics, including adjacency precision (AP), adjacency recall (AR), arrowhead precision (AHP), and arrowhead recall (AHR), were utilized to evaluate the models. Findings revealed that after 2006, key direct causes of workday loss among mining employees included total mining experience, mean office employees, mean underground employees, county, and total mining experience (years). Total mining experience emerged as the most influential factor, whereas mean employees per mine exhibited the least influence. The analyses emphasized the significant role of total mining experience in determining workday loss. The models achieved optimal performance, with AP, AR, AHP, and AHR values measuring 0.694, 0.653, 0.386, and 0.345, respectively. This study demonstrates the feasibility of utilizing the new GGES method to clarify the causal factors behind the workday loss by analyzing employment demographics and injury records and establish their causal relationship network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
