aceAn application of random forest regression for predicting healthcare costs using administrative databases
Background Accurate healthcare cost predictions are essential for health economic decision-making. We propose a bottom-up approach that aggregates individual-level predictions to estimate expenditures for specific population segments without relying on a priori segmentation. Methods This retrospective population-based study used administrative healthcare data (2011-2023) from the Health Protection Agency of Bergamo (Italy). We traced 5-year healthcare resource utilization to predict individual's healthcare costs…
An application of random forest regression for predicting healthcare costs using administrative databases: Performance was assessed at the individual-level using error metrics, and at the population-level using prediction error (PE).
Evidence
- Peer-reviewedExpert Review of Pharmacoeconomics & Outcomes Research2026-09-21
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Truvace Impact Record TRV-2026-1163, v1: “An application of random forest regression for predicting healthcare costs using administrative databases.” Truvace, 2026-09-22. /record/TRV-2026-1163 (accessed at citation time). sha256 05b58d6aa25520cb…
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