COVID-19疾病结果预测模型数据集

数据集获取（Figshare）

• 源站标题：Prediction models for COVID-19 disease outcomes
• DOI：10.6084/m9.figshare.26042178.v1
• 发布日期：2024-06-14
• 文件数：8 个
• 文件总体积：38.75 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Prediction_models_for_COVID-19_disease_outcomes/26042178

命令行下载

curl -L -o "ViHOP_Data_Hospitalization.xlsx" "https://ndownloader.figshare.com/files/47062485"
curl -L -o "ViHOP_Data_ICU.xlsx" "https://ndownloader.figshare.com/files/47062488"
curl -L -o "ViHOP_Data_LongCOVID.xlsx" "https://ndownloader.figshare.com/files/47062491"
curl -L -o "ViHOP_Hospitalization.ipynb" "https://ndownloader.figshare.com/files/47062494"
curl -L -o "ViHOP_ICU.ipynb" "https://ndownloader.figshare.com/files/47062497"
curl -L -o "ViHOP_LongCOVID.ipynb" "https://ndownloader.figshare.com/files/47062500"
curl -L -o "temi_a_2361791_sm2863.docx" "https://ndownloader.figshare.com/files/47062503"
curl -L -o "temi_a_2361791_sm2862.xlsx" "https://ndownloader.figshare.com/files/47062506"

文件清单

• ViHOP_Data_Hospitalization.xlsx（14.66 MB）
• ViHOP_Data_ICU.xlsx（12.37 MB）
• ViHOP_Data_LongCOVID.xlsx（10.00 MB）
• ViHOP_Hospitalization.ipynb（52.92 KB）
• ViHOP_ICU.ipynb（352.65 KB）
• ViHOP_LongCOVID.ipynb（42.38 KB）
• temi_a_2361791_sm2863.docx（1.13 MB）
• temi_a_2361791_sm2862.xlsx（148.19 KB）

文件总体积 38.75 MB（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

SARS-CoV-2 has caused over 6.9 million deaths and continues to produce lasting health consequences. COVID-19 manifests broadly from no symptoms to death. In a retrospective cross-sectional study, we developed personalized risk assessment models that predict clinical outcomes for individuals with COVID-19 and inform targeted interventions. We sequenced viruses from SARS-CoV-2-positive nasopharyngeal swab samples between July 2020 and July 2022 from 4450 individuals in Missouri and retrieved associated disease courses, clinical history, and urban-rural classification. We integrated this data to develop machine learning-based predictive models to predict hospitalization, ICU admission, and long COVID.
The mean age was 38.3 years (standard deviation = 21.4) with 55.2% (N = 2453) females and 44.8% (N = 1994) males (not reported, N = 4). Our analyses revealed a comprehensive set of predictors for each outcome, encompassing human, environment, and virus genome-wide genetic markers. Immunosuppression, cardiovascular disease, older age, cardiac, gastrointestinal, and constitutional symptoms, rural residence, and specific amino acid substitutions were associated with hospitalization. ICU admission was associated with acute respiratory distress syndrome, ventilation, bacterial co-infection, rural residence, and non-wild type SARS-CoV-2 variants. Finally, long COVID was associated with hospital admission, ventilation, and female sex.
Overall, we developed risk assessment models that offer the capability to identify patients with COVID-19 necessitating enhanced monitoring or early interventions. Of importance, we demonstrate the value of including key elements of virus, host, and environmental factors to predict patient outcomes, serving as a valuable platform in the field of personalized medicine with the potential for adaptation to other infectious diseases.
Model summary and motivation. Individuals infected with SARS-CoV-2 experience a wide spectrum of clinical manifestations ranging from no symptoms to death. Using the Virus-Human Outcomes Prediction (ViHOP) algorithm, we aim to utilize the individual’s clinical characteristics, the individual’s location, and the infe

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.26042178.v1（source: https://tandf.figshare.com/articles/dataset/Prediction_models_for_COVID-19_disease_outcomes/26042178）。