Explainable Artificial Intelligence for Rehospitalization and Financial Burden of Fertile Women in Orthopedic Care

Fertile women represent a socially and medically significant patient group, yet little research has examined their rehospitalization behavior and financial burden in clinical settings. This study develops predictive and explainable artificial intelligence (AI) to forecast retention and medical costs among reproductive-age orthopedic patients. Electronic health records of 83 women (aged 15–49) at a major university hospital in Korea were analyzed. Six machine learning models were tested, and model performance was assessed using accuracy and the area under the curve (AUC). Shapley Additive Explanations (SHAP) were applied to interpret predictors of rehospitalization. Additional analyses explored determinants of patients’ total and uncovered medical costs. Random forest outperformed other models in predicting rehospitalization (AUC 0.92 vs. 0.73 for logistic regression). Key predictors included major disease, systolic blood pressure, platelet count, age, and treatment costs. Random forest also yielded lower error rates than linear regression in forecasting patients’ financial burden (RMSE/IQR for total cost: 1.05 vs. 1.14). Several factors—such as blood pressure, pulse, and hematocrit—were influential for both retention and costs. Predictive and explainable AI can support medical centers in anticipating rehospitalization and financial barriers for fertile women. By integrating medical and socioeconomic determinants, hospitals may design strategies that enhance patient retention while addressing broader societal priorities in women’s health.