Survival prediction of heart failure patients using motion-based analysis method.
Comput Methods Programs Biomed
; 236: 107547, 2023 Jun.
Article
in En
| MEDLINE
| ID: mdl-37126888
ABSTRACT
BACKGROUND AND OBJECTIVE:
Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment.METHODS:
We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients.RESULTS:
The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods.CONCLUSIONS:
The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Heart Failure
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Comput Methods Programs Biomed
Journal subject:
INFORMATICA MEDICA
Year:
2023
Document type:
Article
Affiliation country:
China