This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted from attributes with machine learning. Like in the original study, we use Natural Language Processing (NLP) to extract fundamental attributes from raw incident reports and machine learning models are trained to predict safety outcomes. The outcomes predicted here are injury severity, injury type, body part impacted, and incident type. However, unlike in the original study, safety outcomes were not extracted via NLP but were provided by independent human annotations, eliminating any potential source of artificial correlation between predictors and predictands. Results show that attributes are still highly predictive, confirming the validity of the original approach. Other improvements brought by the current study include the use of (1) a much larger dataset featuring more than 90,000 reports, (2) two new models, XGBoost and linear SVM (Support Vector Machines), (3) model stacking, (4) a more straightforward experimental setup with more appropriate performance metrics, and (5) an analysis of per-category attribute importance scores. Finally, the injury severity outcome is well predicted, which was not the case in the original study. This is a significant advancement.

中文翻译：

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基于人工智能的通用属性独立施工安全结果预测

本文显着改进并验证了先前研究中提出的方法，该方法通过机器学习从属性中预测安全结果。与原始研究一样，我们使用自然语言处理 (NLP) 从原始事件报告中提取基本属性，并对机器学习模型进行训练以预测安全结果。此处预测的结果是伤害严重程度、伤害类型、受影响的身体部位和事件类型。然而，与原始研究不同的是，安全性结果不是通过 NLP 提取的，而是由独立的人工注释提供的，消除了预测变量和预测变量之间的任何潜在的人工相关性来源。结果表明，属性仍然具有高度的预测性，证实了原始方法的有效性。当前研究带来的其他改进包括使用 (1) 包含超过 90,000 个报告的更大数据集，(2) 两个新模型，XGBoost 和线性 SVM（支持向量机），(3) 模型堆叠，(4)具有更合适的性能指标的更直接的实验设置，以及 (5) 对每个类别的属性重要性分数的分析。最后，可以很好地预测损伤严重程度的结果，这在原始研究中并非如此。这是一个重大的进步。损伤严重程度的结果可以很好地预测，这在原始研究中并非如此。这是一个重大的进步。损伤严重程度的结果可以很好地预测，这在原始研究中并非如此。这是一个重大的进步。