AI深度学习技术对冠心病患者可能并发症及临床护理质量的影响-63756

数据集获取（Figshare）

• 源站标题：Effect of AI deep learning techniques on possible complications and clinical nursing quality of patients with coronary heart disease
• DOI：10.6084/m9.figshare.14318649.v1
• 发布日期：2021-03-26
• 文件数：6 个
• 文件总体积：186.85 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Effect_of_AI_deep_learning_techniques_on_possible_complications_and_clinical_nursing_quality_of_patients_with_coronary_heart_disease/14318649

命令行下载

curl -L -o "0101-2061-cta-fst42020-gf01.jpg" "https://ndownloader.figshare.com/files/27264216"
curl -L -o "0101-2061-cta-fst42020-gf02.jpg" "https://ndownloader.figshare.com/files/27264219"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/27264222"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/27264225"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/27264228"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/27264231"

文件清单

• 0101-2061-cta-fst42020-gf01.jpg（72.86 KB）
• 0101-2061-cta-fst42020-gf02.jpg（91.47 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）

文件总体积 186.85 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Abstract To analyze the effect of AI deep learning techniques on understanding possible complications and improving clinical nursing quality of patients with coronary heart disease. The clinical data of 182 patients with coronary heart disease who received treatment were collected, among which 80 patients received routine nursing management only during hospitalization, set as the control group; AI deep learning techniques were applied to the other 102 patients During treatment and nursing, the incidence of related complications in the control group was higher than that in the observation group, and the average hospitalization time of the patients was longer than that in the observation group. In the observation group, AI deep learning techniques were applied to predict the incidence of complications of coronary heart disease in 14 patients, with an accuracy rate of 87.50% (14/16) and an error rate of 12.50% (2/16). Both the clinical nursing quality and patients’ satisfaction score of the observation group were higher than those of the control group, and the overall nursing satisfaction rate of the patients was higher. Applied in prediction of possible complications of hospitalized patients with coronary heart disease, AI deep learning techniques were of high accuracy rate.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.14318649.v1（source: https://scielo.figshare.com/articles/dataset/Effect_of_AI_deep_learning_techniques_on_possible_complications_and_clinical_nursing_quality_of_patients_with_coronary_heart_disease/14318649）。