AUTHOR=Bai Zhongfei , Zhang Jiaqi , Tang Chaozheng , Wang Lejun , Xia Weili , Qi Qi , Lu Jiani , Fang Yuan , Fong Kenneth N. K. , Niu Wenxin TITLE=Return-to-Work Predictions for Chinese Patients With Occupational Upper Extremity Injury: A Prospective Cohort Study JOURNAL=Frontiers in Medicine VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.805230 DOI=10.3389/fmed.2022.805230 ISSN=2296-858X ABSTRACT=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.