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TRUVACE RECORD VERSION
record: TRV-2026-1965
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T01:10:45.928371Z
status: published
lens: p_space
sector: health
headline: A Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study
dek: 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…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
quick_read: 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.

Methods In this retrospective cohort study, data from 181 patients with PAH were analyzed. A total of 6 supervised ML algorithms (Least Absolute Shrinkage and Selection Operator [LASSO] regression, ridge regression, k-nearest neighbors, decision trees, random forest, and gradient boosting) were trained to predict the relative change in World Health Organization functional class (WHO-FC) from baseline to 12-month follow-up using baseline clinical variables.
limitation: 
tag: Evidence-backed problem
key_points: 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 patients with PAH.
rundown: 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 patients with PAH. Methods In this retrospective cohort study, data from 181 patients with PAH were analyzed.
sources:
- peer_reviewed | JMIR Medical Informatics | https://doi.org/10.2196/97372 | 2026-10-15
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