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
Abstract Background 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. Method In this study, 96 patients with ALHF in the emergency department of the Affiliated Hospital of X...
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2024-12-01
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| Series: | BMC Emergency Medicine |
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| Online Access: | https://doi.org/10.1186/s12873-024-01156-x |
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| author | Qian Dai Jing Huang Hui Huang Lin Song |
| author_facet | Qian Dai Jing Huang Hui Huang Lin Song |
| author_sort | Qian Dai |
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| description | Abstract Background 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. Method In this study, 96 patients with ALHF in the emergency department of the Affiliated Hospital of Xuzhou Medical University were selected. According to different nursing methods, they were divided into conventional group and optimization group. Activity of daily living (ADL) scale was used to evaluate ADL of patients 6 months after discharge. Self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to assess patients’ psychological state. 45 min improvement rate, 60 min show efficiency, rescue success rate, and transfer rate were used to assess the effect of first aid. Likert 5-level scoring method was adopted to evaluate nursing satisfaction. Results The 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. Conclusion It was demonstrated that explainable DenseNet model had application values in the diagnosis of ALHF. Optimization emergency method could effectively shorten the duration of first aid, relieve anxiety, and other adverse emotions, and improve rescue success rate and short-term efficacy. Nursing intervention has a positive impact on the total effective efficiency and patient satisfaction. |
| format | Article |
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| institution | Kabale University |
| issn | 1471-227X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | BMC Emergency Medicine |
| spelling | doaj-art-381ffb8fd30e4e58b45e7348ee16e6712025-01-05T12:10:26ZengBMCBMC Emergency Medicine1471-227X2024-12-0124111510.1186/s12873-024-01156-xAn 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 failureQian Dai0Jing Huang1Hui Huang2Lin Song3The Affiliated Hospital of Xuzhou Medical UniversityThe Affiliated Hospital of Xuzhou Medical UniversityThe Affiliated Hospital of Xuzhou Medical UniversityThe Affiliated Hospital of Xuzhou Medical UniversityAbstract Background 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. Method In this study, 96 patients with ALHF in the emergency department of the Affiliated Hospital of Xuzhou Medical University were selected. According to different nursing methods, they were divided into conventional group and optimization group. Activity of daily living (ADL) scale was used to evaluate ADL of patients 6 months after discharge. Self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to assess patients’ psychological state. 45 min improvement rate, 60 min show efficiency, rescue success rate, and transfer rate were used to assess the effect of first aid. Likert 5-level scoring method was adopted to evaluate nursing satisfaction. Results The 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. Conclusion It was demonstrated that explainable DenseNet model had application values in the diagnosis of ALHF. Optimization emergency method could effectively shorten the duration of first aid, relieve anxiety, and other adverse emotions, and improve rescue success rate and short-term efficacy. Nursing intervention has a positive impact on the total effective efficiency and patient satisfaction.https://doi.org/10.1186/s12873-024-01156-xDeep learning modelEmergency nursingTreatment complianceAcute left heart failure |
| spellingShingle | 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 BMC Emergency Medicine Deep learning model Emergency nursing Treatment compliance Acute left heart failure |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Deep learning model Emergency nursing Treatment compliance Acute left heart failure |
| url | https://doi.org/10.1186/s12873-024-01156-x |
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