Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models.

ABSTRACT:

Background

Pulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes.

Materials and methods

In this retrospective registry-based design, all consecutive hospitalized patients diagnosed with pulmonary thromboembolism (based on pulmonary CT angiography) from 2011 to 2019 were recruited. ML based algorithms [Gradient Boosting (GB) and Deep Learning (DL)] were applied and compared with logistic regression (LR) to predict hemodynamic instability and/or all-cause mortality.

Results

A total number of 1,017 patients were finally enrolled in

SUBMITTER: Jenab Y 

PROVIDER: S-EPMC10043172 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature