Transferring healthcare risk prediction models between Medicaid populations: a transfer learning evaluation

Abstract Cross-state deployment of Medicaid risk prediction models is challenged by demographic, policy, and care-delivery differences that create domain shift. We evaluated transfer learning methods for predicting acute care utilisation between Washington (source; n = 20,744) and Virginia (target; n = 28,901) Medicaid populations enrolled in high-risk care management, where outcome prevalence differed markedly (9.4% vs 25.6%). Nine approaches were compared: source-only and target-only logistic