Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery

Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery

Abstract Background Postoperative respiratory failure following cardiac surgery (CS-PRF) remains a critical complication with substantial morbidity and mortality. Current risk prediction models are limited by static assessments and suboptimal accuracy. This study aimed to develop and validate a dyna...

Full description

Saved in:
Bibliographic Details
Main Authors: Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si
Format: Article
Language:English
Published: BMC 2025-08-01
Series:BMC Anesthesiology
Subjects:
Cardiac surgery
Postoperative respiratory failure
Machine learning
Deep neural network
Risk prediction
SHAP
Online Access:https://doi.org/10.1186/s12871-025-03239-z
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s12871-025-03239-z

Similar Items