ArticleJournal of Korean medical science2018
Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques.
Article in Journal of Korean medical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Explainable Prediction of Prolonged Work Disability After Occupational Injury and Disease: Temporal Validation and Sex-Specific Performance.Journal of occupational rehabilitation · 2026Article
- Self-Efficacy and Self-Esteem Predict Return to Work and Buffer the Impact of Treatment Duration: A Longitudinal Panel Study.Journal of occupational rehabilitation · 2026Article
- Development and validation of a machine learning-based return to work predictive model for young and middle-aged maintenance hemodialysis patients.Frontiers in public health · 2026Article
- Article
- A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses.Journal of occupational rehabilitation · 2024Article
- Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort.Journal of occupational rehabilitation · 2023Article
- Exploring predictors of welfare dependency 1, 3, and 5 years after mental health-related absence in danish municipalities between 2010 and 2012 using flexible machine learning modelling.BMC public health · 2023Article
- Return-to-Work Predictions for Chinese Patients With Occupational Upper Extremity Injury: A Prospective Cohort Study.Frontiers in medicine · 2022Article
- Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management.Journal of occupational rehabilitation · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
backgroundMany studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period.
methodsAn initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared.
resultsThe analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent.
conclusionIt is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy.
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Registered trials
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