Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction
# Background Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). # Objectives This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart...
Saved in:
| Main Authors: | Zhibo Wang, Xin Chen, Xi Tan, Lingfeng Yang, Kartik Kannapur, Justin L. Vincent, Garin N. Kessler, Boshu Ru, Mei Yang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Columbia Data Analytics, LLC
2021-07-01
|
| Series: | Journal of Health Economics and Outcomes Research |
| Online Access: | https://doi.org/10.36469/jheor.2021.25753 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Heart failure with mildly reduced ejection fraction: retrospective study of ejection fraction trajectory risk
by: Robert J.H. Miller, et al.
Published: (2022-06-01) -
Plasma metabolomics identifies signatures that distinguish heart failure with reduced and preserved ejection fraction
by: Fawaz Naeem, et al.
Published: (2025-08-01) -
Role of interleukins in heart failure with reduced ejection fraction
by: Oliwia Anna Segiet, et al.
Published: (2019-11-01) -
Comparison of mouse models of heart failure with reduced ejection fraction
by: Nabil V. Sayour, et al.
Published: (2025-02-01) -
The Role of Cardiac Imaging in Heart Failure with Reduced Ejection Fraction
by: Rebecca C Gosling, et al.
Published: (2022-06-01)