Novel Surveillance of Occupational Injury Powered by Machine Learning Using Chief Complaint at Emergency Triage

Abstract

Background: Underreporting of occupational injuries remains a global health issue, and warrants social awareness. To assist surveillance of occupational injuries, we developed an automatic screening model using chief complaint at emergency department to improve detection and reporting of occupational injuries.Methods: A total of 181,843 emergency visits aged 15 to 65 year-old were included at a medical center in Taiwan from April, 2015 to March, 2018. The retrospective cohort composed of 9,307 cases of occupational injuries and 172,536 controls. We applied the first 30 months as training dataset, and the following 6 months for prospective testing. Natural Language Processing (NLP) was applied to analyze patients’ chief complaints. The sentences were processed by JiebaR, a Chinese text segmentation technique, reviewed by two occupational physicians, and transformed by a word-embedding model using Glove. Logistic regression was conducted to predict suspected cases of occupational injuries.Results: The prediction model using the chief complaint alone can achieve an overall AUC of 0.936 [95% confidence interval (CI): 0.931 to 0.941], with high sensitivity (93.1%, 95% CI: 91.8 to 94.4) and adequate specificity (84.8%, 95% CI: 84.4 to 85.2). Patients in the most urgent or severe conditions showed the highest accuracy around 90%. We also observed increasing trends of referral to specialists of occupational medicine, and reimbursement rate.Conclusions: Interdepartmental coordination and integration of machine learning may augment the detection of occupational injuries at the emergency triage and improve the reporting, compensation, and prevention of occupational injuries.Contributions to the literaturel This novel surveillance powered by supervised machine learning can help to identify occupational injuries at the emergency triage, and it achieves 93.1% sensitivity and 84.8% specificity.l Along with the system implementation, there are trends of increasing reimbursement rate in the National Labor Insurance and increasing case referrals to specialists of occupational medicine.l Combining the use of Artificial Intelligence (AI) with conventional medical information, such as chief complaints, may provide an effective surveillance system for occupational injuries at hospitals. l Effective screening of labors who suffer from occupational injuries will promote Total Worker Health.