A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism.

ABSTRACT: Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develop a machine learning model to identify patients at risk for long-term consequences after PE. Using data from the RIETE registry, the largest prospective international PE registry, we developed supervised machine learning models to identify patients at increased risk of CTEPH and post-PE syndrome. Our approach involved data preprocessing, model training via random forest algorithm, and validation through Monte-Carlo cross-validation. The performance of the CTEPH prediction model was benchmarked against an

SUBMITTER: Nopp S 

PROVIDER: S-EPMC12460599 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature