Beyond the Black-Box: Understanding XGBoost to Predict Pediatric Hospital Readmission

Hospital readmission is a complex health outcome, which burdens the patient, their family network, and the health system. The black box machine-learning techniques present challenges in validation, regulation, and understanding of health outcomes, such as hospital readmissions. Complex models based on deep learning generally achieve greater accuracy, although they generate tension between accuracy and interpretability. XGBoost outperforms traditional statistical methods and can potentially improve the prediction of negative health outcomes. We conducted a retrospective cohort study with patients under 18 in a tertiary university hospital. We extracted demographic, clinical, and nutritional data from the electronic hospital system. We used extreme gradient boosting (XGBoost) to build a predictive model for potentially avoidable 30-day readmissions. We use methods to calculate the importance scores of each variable for the generated model. Our study showed that it is possible to develop an interpretable prediction model for potentially preventable pediatric readmissions using the XGBoost algorithm.