Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism.

ABSTRACT:

Background

Pulmonary embolism (PE) is a common and potentially fatal condition. Timely and accurate risk assessment in patients with acute deep vein thrombosis (DVT) is crucial. This study aims to develop a deep learning-based, precise, and efficient PE risk prediction model (PE-Mind) to overcome the limitations of current clinical tools and provide a more targeted risk evaluation solution.

Methods

We analyzed clinical data from patients by first simplifying and organizing the collected features. From these, 37 key clinical features were selected based on their importance. These features were categorized and analyzed to identify potential relationships. Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better perf

SUBMITTER: Tian Y 

PROVIDER: S-EPMC11839595 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature