COVID-19大流行期间远程医疗评估急性呼吸道症状患者的临床特征与转诊影响数据集

数据集获取（Figshare）

• 源站标题：Assessment of patients with acute respiratory symptoms during the COVID-19 pandemic by Telemedicine: clinical features and impact on referral
• DOI：10.6084/m9.figshare.14322402.v1
• 发布日期：2021-03-26
• 文件数：4 个
• 文件总体积：22.53 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Assessment_of_patients_with_acute_respiratory_symptoms_during_the_COVID-19_pandemic_by_Telemedicine_clinical_features_and_impact_on_referral/14322402

命令行下载

curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/27284094"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/27284097"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/27284100"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/27284103"

文件清单

• 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 22.53 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 Objective: To characterize variables associated with referral to the emergency department following Telemedicine consultation during the COVID-19 pandemic. Methods: Cross-sectional retrospective study conducted between March and May 2020, with a sample of 500 adult patients. The inclusion criterion was the manifestation of respiratory symptoms, regardless of type. Results: The mean age of patients was 34.7±10.5 years, and 59% were women. Most patients (62.6%) perceived their own health status as malaise and some (41.4%) self-diagnosed COVID-19. Cough (74.4%), rhinorrhea (65.6%), sore throat (38.6%) and sneezing (20.6%) were the most common infection-related symptoms. Overall, 29.4% and 16% of patients reported dyspnea and chest pain, respectively. The Roth score was calculated for a sizeable number of patients (67.6%) and was normal, moderately altered or severely altered in 83.5%, 10.7% and 5.6% of patients, respectively. The percentage of suspected COVID-19 cases was 67.6%. Of these, 75% were managed remotely and only one quarter referred for emergency assessment. Conclusion: Telemedicine assessment is associated with reclassification of patient’'s subjective impression, better inspection of coronavirus disease 2019 and identification of risk patients. Referral is therefore optimized to avoid inappropriate in-person assessment, and low-risk patients can be properly guided. Telemedicine should be implemented in the health care system as a cost-effective strategy for initial assessment of acute patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.14322402.v1（source: https://scielo.figshare.com/articles/dataset/Assessment_of_patients_with_acute_respiratory_symptoms_during_the_COVID-19_pandemic_by_Telemedicine_clinical_features_and_impact_on_referral/14322402）。