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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Machine learning-based prediction of dissatisfaction after occupational injury: a retrospective cohort study using
Youjin Lee1, Taeyeon Kim1, Dahyeon Koo1
1Pohang Stroke and Spine Hospital, Pohang-si, Korea (the Republic of).
Objectives:
To develop a machine learning (ML)-based predictive model to determine the key predictors of dissatisfaction after occupational injury (OI).
Design:
A retrospective cohort study.
Setting:
Nationwide 5-year panel data (2018-2022) from the Panel Study of Workers' Compensation Insurance in South Korea.
Participants:
A total of 2298 workers who completed compensation-related medical care in 2017.
Methods:
Predictive modelling was conducted with extreme gradient (XG) Boost, light gradient boosting machine (GBM), CatBoost and random forest. SHapley Additive Explanations (SHAPs) analysis was conducted to interpret the feature importance. Further, logistic regression was conducted for comparison.
Primary Outcome Measures:
This study evaluated postinjury satisfaction among workers using survey items associated with satisfaction levels. We adopted a 5-year follow-up period.
Results:
Of the 2298 participants, 570 were dissatisfied. The logistic regression model indicated that dissatisfaction was significantly associated with unemployment (adjusted OR (aOR) 1.701; 95% CI: 1.296 to 2.233), lack of private health insurance (aOR 1.347; 95% CI 1.042 to 1.741) and lower perceived socioeconomic status (aOR 2.097; 95% CI 1.109 to 3.965). Among the ML models, light GBM exhibited the highest area under the receiver operating characteristics curve (0.770 (95% CI 0.718 to 0.819)), followed by CatBoost (0.768 (95% CI 0.718 to 0.815)), random forest (0.766 (95% CI 0.715 to 0.814)) and XGBoost (0.765 (95% CI 0.717 to 0.811)). The SHAP analysis demonstrated the total number of household members, extent of pain interference with daily life, perceived health status before injury and financial factors as the strongest predictors.
Conclusion:
This study developed and demonstrated robust predictive performance of an ML-based model for determining dissatisfaction after OI. The key features included employment status, financial stability, chronic pain and cognitive function, highlighting the multifaceted nature of worker satisfaction.