aceA Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study
Background Pulmonary arterial hypertension (PAH) is a rare and progressive disease associated with substantial clinical heterogeneity and high health care costs. While machine learning (ML) has been increasingly applied to predict clinical outcomes, approaches that integrate clinical prediction with downstream economic modeling remain limited. Objective This study proposes and evaluates an ML-based framework that combines prediction of future functional status with estimation of associated health care costs in p…
A Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study: Model performance was evaluated using Pearson correlation coefficient (r), and error metrics (RMSE, MAE, and MedAE), based on cross-validation and independent test set validation.
Evidence
- Peer-reviewedJMIR Medical Informatics2026-10-15
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Truvace Impact Record TRV-2026-1965, v1: “A Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study.” Truvace, 2026-10-20. /record/TRV-2026-1965 (accessed at citation time). sha256 9f8e2eaa310a6aa3…
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