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TRUVACE RECORD VERSION record: TRV-2026-1163 version: 1 kind: certified reason: Certified into the record timestamp: 2026-09-22T06:53:28.259234Z status: published lens: p_space sector: health headline: An application of random forest regression for predicting healthcare costs using administrative databases dek: 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… gain_title: (none) problem_title: 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). trace_subject: (none) gain_reading: (none) gain_evidence: (none) problem_reading: 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). problem_evidence: (none) quick_read: 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. We traced 5-year healthcare resource utilization to predict individual's healthcare costs in the following year using random forest regression algorithms. Results At the individual-level, algorithms had a poor performance, systematically underestimating high-cost users. limitation: tag: Evidence-backed problem key_points: 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). rundown: 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 in the following year using random forest regression algorithms. sources: - peer_reviewed | Expert Review of Pharmacoeconomics & Outcomes Research | https://doi.org/10.1080/14737167.2026.2738028 | 2026-09-21 prev: 0000000000000000000000000000000000000000000000000000000000000000
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