Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms

Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms

Abstract Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acute PE population could significantly impro...

Full description

Saved in:
Bibliographic Details
Main Authors: Mehmet Tahir Huyut, Andrei Velichko, Maksim Belyaev, Yuriy Izotov, Şebnem Karaoğlanoğlu, Bünyamin Sertoğullarından, Sıddık Keskin, Dmitry Korzun
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Right ventricular dysfunction
Pulmonary embolism
Thrombosis
LogNNet
Machine learning
Diagnostic models
Online Access:https://doi.org/10.1038/s41598-025-00274-1
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items