经皮肾镜取石术无石状态预测机器学习对比分析数据集

数据集获取（Figshare）

• 源站标题：Table1_Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: Comparative Analysis With Guy’s Stone Score and the S.T.O.N.E Score System.xlsx
• DOI：10.3389/fmolb.2022.880291.s001
• 发布日期：2022-05-04
• 文件数：1 个
• 文件总体积：39.45 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Predicting_the_Stone-Free_Status_of_Percutaneous_Nephrolithotomy_With_the_Machine_Learning_System_Comparative_Analysis_With_Guy_s_Stone_Score_and_the_S_T_O_N_E_Score_System_xlsx/19702234

命令行下载

curl -L -o "Table1_Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: Comparative Analysis With Guy’s Stone Score .xlsx" "https://ndownloader.figshare.com/files/34993084"

文件清单

• Table1_Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: Comparative Analysis With Guy’s Stone Score .xlsx（39.45 KB）

文件总体积 39.45 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

Purpose: The aim of the study was to use machine learning methods (MLMs) to predict the stone-free status after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy’s stone score and the S.T.O.N.E score system.
Materials and Methods: Data from 222 patients (90 females, 41%) who underwent PCNL at our center were used. Twenty-six parameters, including individual variables, renal and stone factors, and surgical factors were used as input data for MLMs. We evaluated the efficacy of four different techniques: Lasso-logistic (LL), random forest (RF), support vector machine (SVM), and Naive Bayes. The model performance was evaluated using the area under the curve (AUC) and compared with that of Guy’s stone score and the S.T.O.N.E score system.
Results: The overall stone-free rate was 50% (111/222). To predict the stone-free status, all receiver operating characteristic curves of the four MLMs were above the curve for Guy’s stone score. The AUCs of LL, RF, SVM, and Naive Bayes were 0.879, 0.803, 0.818, and 0.803, respectively. These values were higher than the AUC of Guy’s score system, 0.800. The accuracies of the MLMs (0.803% to 0.818%) were also superior to the S.T.O.N.E score system (0.788%). Among the MLMs, Lasso-logistic showed the most favorable AUC.
Conclusion: Machine learning methods can predict the stone-free rate with AUCs not inferior to those of Guy’s stone score and the S.T.O.N.E score system.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmolb.2022.880291.s001（source: https://frontiersin.figshare.com/articles/dataset/Table1_Predicting_the_Stone-Free_Status_of_Percutaneous_Nephrolithotomy_With_the_Machine_Learning_System_Comparative_Analysis_With_Guy_s_Stone_Score_and_the_S_T_O_N_E_Score_System_xlsx/19702234）。