Diagnosis model for assessing chronic thromboembolic pulmonary hypertension in high-altitude pulmonary embolism patients: a machine learning approach.

ABSTRACT:

Background

Patients with pulmonary embolism (PE) at high altitude face an increased risk of developing chronic thromboembolic pulmonary hypertension (CTEPH). This study aims to establish a diagnosis model of CTEPH patients at high altitude to optimize early screening.

Methods

A retrospective cohort of CTEPH and PE patients was rigorously selected through inclusion/exclusion criteria. Clinical data encompassing biochemical profiles, echocardiography, and CT angiography (CTA) were collected, yielding 103 candidate variables. Feature parameters were screened using the Boruta algorithm, followed by predictive model development with seven machine learning architectures. The optimal model was identified based on area under the curve (AUC). The optimal Random Forest model was subse

SUBMITTER: Fan E 

PROVIDER: S-EPMC12537356 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature