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A Machine Learning-Based Predictive Model of Return to Work After Sick Leave.

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This study developed a machine-learning model to predict return to work (RTW) after sick leave. The model accurately predicted binary outcomes but struggled with classifying the specific form of RTW.

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Area of Science:

  • Occupational Health
  • Data Science
  • Biostatistics

Background:

  • Return to work (RTW) is a critical outcome following sick leave, impacting both individual well-being and economic productivity.
  • Predictive modeling can aid in identifying individuals at risk for prolonged absence and facilitate targeted interventions.
  • Machine learning offers advanced capabilities for analyzing complex health and employment data to forecast RTW.

Purpose of the Study:

  • To develop and evaluate a predictive model for return to work (RTW) after sick leave using machine learning.
  • To assess the performance of a gradient boosting machine (GBM) algorithm in predicting RTW outcomes.
  • To explore the utility of machine learning in understanding factors influencing RTW post-sick leave.

Main Methods:

  • Utilized panel data from 2000 participants (1686 males, 314 females) from the Korea Workers' Compensation & Welfare Service.
  • Employed a gradient boosting machine (GBM), a powerful machine learning algorithm, for predictive modeling.
  • Evaluated model performance on binary classification (returned to work vs. not working) and three-group classification tasks.

Main Results:

  • The GBM demonstrated excellent performance in the binary classification task, accurately predicting whether individuals returned to work.
  • The model's performance was suboptimal when attempting a three-group classification, indicating limitations in differentiating specific RTW patterns.
  • The study highlights the potential of machine learning for RTW prediction using common factors.

Conclusions:

  • Machine learning algorithms can effectively predict the likelihood of return to work (RTW) after sick leave based on available data.
  • Current models struggle to differentiate the specific forms or nuances of returning to work.
  • Further research incorporating detailed injury or disease-specific information is necessary to enhance predictive accuracy for RTW patterns.