数据挖掘技术应用于探索神经源性膀胱患者上尿路损伤预测因素

数据集获取（Figshare）

• 源站标题：Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder
• DOI：10.6084/m9.figshare.5670424.v1
• 发布日期：2017-12-05
• 文件数：6 个
• 文件总体积：74.02 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Application_of_data_mining_techniques_to_explore_predictors_of_upper_urinary_tract_damage_in_patients_with_neurogenic_bladder/5670424

命令行下载

curl -L -o "1414-431X-bjmbr-1414-431X20176638-gf01.jpg" "https://ndownloader.figshare.com/files/9911590"
curl -L -o "1414-431X-bjmbr-1414-431X20176638-gf02.jpg" "https://ndownloader.figshare.com/files/9911593"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/9911596"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/9911599"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/9911602"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/9911605"

文件清单

• 1414-431X-bjmbr-1414-431X20176638-gf01.jpg（13.88 KB）
• 1414-431X-bjmbr-1414-431X20176638-gf02.jpg（33.52 KB）
• Table_1.xls（9.73 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）

文件总体积 74.02 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

This study proposed a decision tree model to screen upper urinary tract damage (UUTD) for patients with neurogenic bladder (NGB). Thirty-four NGB patients with UUTD were recruited in the case group, while 78 without UUTD were included in the control group. A decision tree method, classification and regression tree (CART), was then applied to develop the model in which UUTD was used as a dependent variable and history of urinary tract infections, bladder management, conservative treatment, and urodynamic findings were used as independent variables. The urethra function factor was found to be the primary screening information of patients and treated as the root node of the tree; Pabd max (maximum abdominal pressure, >14 cmH2O), Pves max (maximum intravesical pressure, ≤89 cmH2O), and gender (female) were also variables associated with UUTD. The accuracy of the proposed model was 84.8%, and the area under curve was 0.901 (95%CI=0.844-0.958), suggesting that the decision tree model might provide a new and convenient way to screen UUTD for NGB patients in both undeveloped and developing areas.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.5670424.v1（source: https://scielo.figshare.com/articles/dataset/Application_of_data_mining_techniques_to_explore_predictors_of_upper_urinary_tract_damage_in_patients_with_neurogenic_bladder/5670424）。