arXiv:1908.05972v3 Announce Type: replace Abstract: 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 e
The continuous improvement in NLP and machine learning capabilities enables more sophisticated analysis of unstructured data like incident reports, leading to practical applications in various industries.
This development indicates a growing capability for AI to proactively identify and predict safety risks, potentially reducing incidents and improving workplace conditions across hazardous sectors.
The ability to predict specific safety outcomes from universal attributes using AI could shift safety management from reactive incident response to proactive risk mitigation and prevention.
- · Construction companies
- · Safety technology providers
- · Insurance companies
- · AI/ML researchers
- · Companies with poor safety records
AI-driven platforms for workplace safety monitoring and prediction will become more prevalent.
Insurance premiums for construction and industrial sectors may be adjusted based on AI-predicted safety risks and mitigation efforts.
Predictive safety analysis could inform regulatory standards and lead to new industry-wide best practices for risk management.
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