工作场所伤害的预测
Prediction of Workplace Injuries
摘要：
工伤造成大量的人力和财力损失。据国际劳工组织（劳工组织）报告，每年有超过3.74亿起工伤报告。在这项研究中，我们调查的问题，在工作环境中的伤害风险预测和预防。虽然受伤在所有组织中都占很大比例，但在一个组织中却很少发生。因此，从单个组织收集足够大的数据集是非常困难的。此外，收集的数据集往往是高度不平衡的，这增加了问题的难度。最后，风险预测需要为预防伤害提供额外的背景。我们提出并评估了以下完整的解决方案：1）几种基于集合的重新分配方法来解决类不平衡问题，2）一种新的迁移学习方法来跨组织转移知识，以及3）各种技术来揭示不同变量对伤害风险的关联和因果关系，同时控制相关的混杂因素。
英文摘要：
Workplace injuries result in substantial human and financial losses. As reported by the International Labour Organization (ILO), there are more than 374 million work-related injuries reported every year. In this study, we investigate the problem of injury risk prediction and prevention in a work environment. While injuries represent a significant number across all organizations, they are rare events within a single organization. Hence, collecting a sufficiently large dataset from a single organization is extremely difficult. In addition, the collected datasets are often highly imbalanced which increases the problem difficulty. Finally, risk predictions need to provide additional context for injuries to be prevented. We propose and evaluate the following for a complete solution: 1) several ensemble-based resampling methods to address the class imbalance issues, 2) a novel transfer learning approach to transfer the knowledge across organizations, and 3) various techniques to uncover the association and causal effect of different variables on injury risk, while controlling for relevant confounding factors.
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。
