Predicting burnout in radiology nurses: an interpretable machine learning model developed and externally validated in a multi-center study.

Objective: To develop and externally validate an interpretable machine learning model for predicting individual burnout risk among radiology nurses, and to identify modifiable, non-linear risk thresholds to guide precision prevention. Methods: A two-center, two-stage design was used. A development cohort ( Results: The XGBoost model achieved strong discrimination in external validation (AUC = 0.859). In the internal test set, XGBoost outperformed logistic regression and other machine learning algorithms (AUC: 0.963 vs. 0.910 for logistic regression). SHAP analysis identified Effort-Reward Imbalance and Overcommitment as dominant predictors, and revealed a potential inflection point at approximately 65 weekly work hours, beyond which burnout risk escalated disproportionately in the SHAP analysis. Decision curve analysis confirmed net clinical benefit across threshold probabilities of 5 -65%. Individualized risk profiles were generated for actionable nurse management. Conclusion: This externally validated, interpretable XGBoost-SHAP framework moves beyond population-averaged associations. By uncovering specific non-linear thresholds (e.g., 65 h/week workload) and providing personalized risk profiles, it enables nurse managers to shift from reactive crisis management to data-driven, precision prevention of burnout in radiology nurses.