An retrospective study on the effects of deep learning model-based optimization emergency nursing on treatment compliance and curative effect of patients with acute left heart failure
Dublin Core
Title
An retrospective study on the effects of deep learning model-based optimization emergency nursing on treatment compliance and curative effect of patients with acute left heart failure
Subject
Based on explainable DenseNet model, the therapeutic effects of optimization nursing on patients with acute left heart failure (ALHF) and its application values were discussed.
Description
he optimization group showed shorter durations for first aid, hospitalization, electrocardiography, vein channel establishment, and blood collection compared to the conventional group. However, their SBP, DBP, and HR were inferior. On the other hand, LVEF and FS were significantly better in the optimization group. After nursing intervention, SAS and SDS scores were lower in the optimization group. Additionally, the optimization group had higher 45-minute improvement rates, 60-minute show efficiency, rescue success, and transfer rates. They also performed better in 6-minute walking distance and ADL scores 6 months post-discharge. The optimization group had better compliance, total effective rates, and satisfaction than the conventional group.
Creator
Qian Dai, Jing Huang, Hui Huang & Lin Song
Source
https://bmcemergmed.biomedcentral.com/articles/10.1186/s12873-024-01156-x
Publisher
BMC Emergency Medicine
Date
31 DECEMBER 2024
Contributor
fAJAR BAGUS w
Format
pdf
Language
eNGLISH
Type
TEXT
Files
Collection
Citation
Qian Dai, Jing Huang, Hui Huang & Lin Song , “An retrospective study on the effects of deep learning model-based optimization emergency nursing on treatment compliance and curative effect of patients with acute left heart failure,” Repository Horizon University Indonesia, accessed June 15, 2025, https://repository.horizon.ac.id/items/show/9391.