Advances in heart failure clinical research based on deep learning.
10.7507/1001-5515.202208060
- Author:
Yingpeng LEI
1
;
Siru LIU
2
;
Yuxuan WU
3
;
Chuan LI
1
;
Jialin LIU
4
Author Information
1. College of Computer Science, Sichuan University, Chengdu 610041, P. R. China.
2. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
3. West China Medical School, Sichuan University, Chengdu 610041, P. R. China.
4. Information Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China.
- Publication Type:Journal Article
- Keywords:
Deep learning;
Diagnoses;
Heart failure;
Prognosis
- MeSH:
Humans;
Artificial Intelligence;
Deep Learning;
Heart Failure/diagnosis*;
Machine Learning;
Diagnostic Imaging
- From:
Journal of Biomedical Engineering
2023;40(2):373-377
- CountryChina
- Language:Chinese
-
Abstract:
Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
