Return-to-Work Predictions for Chinese Patients With Occupational Upper Extremity Injury: A Prospective Cohort Study

Objective: We created predictive models using machine learning algorithms for return-to-work (RTW) in patients with traumatic upper extremity injuries. Methods: Data were obtained immediately before patient discharge and patients were followed up for 1 year. K-nearest neighbor, logistic regression, support vector machine, and decision tree algorithms were used to create our predictive models for RTW. Results: In total, 163 patients with traumatic upper extremity injury were enrolled, and 107/163 (65.6%) had successfully returned to work at 1-year of follow-up. The decision tree model had a lower F1-score than any of the other models (t values: 7.93–8.67, p < 0.001), while the others had comparable F1-scores. Furthermore, the logistic regression and support vector machine models were significantly superior to the k-nearest neighbors and decision tree models in the area under the receiver operating characteristic curve (t values: 6.64–13.71, p < 0.001). Compared with the support vector machine, logistical regression selected only two essential factors, namely, the patient's expectation of RTW and carrying strength at the waist, suggesting its superior efficiency in the prediction of RTW. Conclusion: Our study demonstrated that high predictability for RTW can be achieved through use of machine learning models, which is helpful development of individualized vocational rehabilitation strategies and relevant policymaking.