Quantum NLP for Predicting Injury Leaves in Occupational Accident Reports

Work accidents can threaten workers’ health and impose significant costs on organizations, such as the need for work restructuring and the direct or indirect costs associated with employee absence. Accident investigation reports offer valuable information that can help companies propose preventive measures and identify the causes and consequences of injury events. However, this information is often complex, redundant, or incomplete, making thorough human review challenging due to the large volume of reports. In this study, we applied Quantum Natural Language Processing (QNLP) techniques to predict injury leave based on accident reports from a hydroelectric power company. Our cross-validation results showed a median accuracy of 73.33%. We also analyzed reports with high and low classification accuracy to explore potential reasons for these outcomes. This study demonstrates that accident investigation reports can provide essential insights to enhance workplace safety.