Truace
Subscribe

A 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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1965· cite & verify
A Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study
Functional flow and event-driven methods for predicting system performance by Steward, Victoria. Public domain

In brief

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.

Main points

  1. Background Pulmonary arterial hypertension (PAH) is a rare and progressive disease associated with substantial clinical heterogeneity and high health care costs.
  2. While machine learning (ML) has been increasingly applied to predict clinical outcomes, approaches that integrate clinical prediction with downstream economic modeling remain limited.
  3. 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.

The problem

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.

The rundown

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

  1. Peer-reviewedJMIR Medical Informatics2026-10-15

The debate