符合世界卫生组织2009年标准的重症登革热预测工具

数据集获取（Figshare）

• 源站标题：Predictive Tools for Severe Dengue Conforming to World Health Organization 2009 Criteria
• DOI：10.1371/journal.pntd.0002972
• 发布日期：2014-07-10
• 文件数：5 个
• 文件总体积：76.75 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Predictive_Tools_for_Severe_Dengue_Conforming_to_World_Health_Organization_2009_Criteria_/1099471

命令行下载

curl -L -o "Table_S1.docx" "https://ndownloader.figshare.com/files/1587727"
curl -L -o "Table_S2.docx" "https://ndownloader.figshare.com/files/1587728"
curl -L -o "Table_S3.docx" "https://ndownloader.figshare.com/files/1587729"
curl -L -o "Table_S4.docx" "https://ndownloader.figshare.com/files/1587730"
curl -L -o "Table_S5.docx" "https://ndownloader.figshare.com/files/1587731"

文件清单

• Table_S1.docx（16.10 KB）
• Table_S2.docx（14.96 KB）
• Table_S3.docx（14.97 KB）
• Table_S4.docx（14.54 KB）
• Table_S5.docx（16.17 KB）

文件总体积 76.75 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

Background
Dengue causes 50 million infections per year, posing a large disease and economic burden in tropical and subtropical regions. Only a proportion of dengue cases require hospitalization, and predictive tools to triage dengue patients at greater risk of complications may optimize usage of limited healthcare resources. For severe dengue (SD), proposed by the World Health Organization (WHO) 2009 dengue guidelines, predictive tools are lacking.
Methods
We undertook a retrospective study of adult dengue patients in Tan Tock Seng Hospital, Singapore, from 2006 to 2008. Demographic, clinical and laboratory variables at presentation from dengue polymerase chain reaction-positive and serology-positive patients were used to predict the development of SD after hospitalization using generalized linear models (GLMs).
Principal findings
Predictive tools compatible with well-resourced and resource-limited settings – not requiring laboratory measurements – performed acceptably with optimism-corrected specificities of 29% and 27% respectively for 90% sensitivity. Higher risk of severe dengue (SD) was associated with female gender, lower than normal hematocrit level, abdominal distension, vomiting and fever on admission. Lower risk of SD was associated with more years of age (in a cohort with an interquartile range of 27–47 years of age), leucopenia and fever duration on admission. Among the warning signs proposed by WHO 2009, we found support for abdominal pain or tenderness and vomiting as predictors of combined forms of SD.
Conclusions
The application of these predictive tools in the clinical setting may reduce unnecessary admissions by 19% allowing the allocation of scarce public health resources to patients according to the severity of outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pntd.0002972（source: https://plos.figshare.com/articles/dataset/_Predictive_Tools_for_Severe_Dengue_Conforming_to_World_Health_Organization_2009_Criteria_/1099471）。