Identification and Patient Benefit Evaluation of Machine Learning Models for Predicting 90-Day Mortality After Endovascular Thrombectomy Based on Routinely Ready Clinical Information

Identification and Patient Benefit Evaluation of Machine Learning Models for Predicting 90-Day Mortality After Endovascular Thrombectomy Based on Routinely Ready Clinical Information

Endovascular thrombectomy (EVT) is regarded as the standard of care for acute ischemic stroke (AIS) patients with large vessel occlusion (LVO). However, the mortality rates for these patients remain alarmingly high. Dependable mortality prediction based on timely clinical information is of great imp...

Full description

Saved in:
Bibliographic Details
Main Authors: Andrew Tik Ho Ng, Lawrence Wing Chi Chan
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Bioengineering
Subjects:
machine learning
thrombectomy
mortality
Online Access:https://www.mdpi.com/2306-5354/12/5/468
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2306-5354/12/5/468

Similar Items