可解释机器学习用于大肠杆菌尿路感染早期检测数据集

数据集获取（Figshare）

• 源站标题：Table 2_Explainable machine learning for early detection of Escherichia coli urinary tract infections: integrating SHAP interpretation and bacterial epidemiology.docx
• DOI：10.3389/fcimb.2026.1740707.s005
• 发布日期：2026-02-13
• 文件数：1 个
• 文件总体积：12.38 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Explainable_machine_learning_for_early_detection_of_Escherichia_coli_urinary_tract_infections_integrating_SHAP_interpretation_and_bacterial_epidemiology_docx/31331611

命令行下载

curl -L -o "Table 2_Explainable machine learning for early detection of Escherichia coli urinary tract infections: integrating SHAP interpretation and bacterial e.docx" "https://ndownloader.figshare.com/files/61865500"

文件清单

• Table 2_Explainable machine learning for early detection of Escherichia coli urinary tract infections: integrating SHAP interpretation and bacterial e.docx（12.38 KB）

文件总体积 12.38 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

BackgroundEscherichia coli is the predominant uropathogen in urinary tract infections (UTIs), but culture-based identification is time-consuming. This study aimed to develop an explainable, culture-independent model to distinguish E. coli from other uropathogens using routinely collected clinical data.
MethodsWe retrospectively analyzed 308 hospitalized patients with culture-confirmed UTIs at Fuding Hospital, Fujian University of Traditional Chinese Medicine (January–December 2023), classified as E. coli (n = 158) or non–E. coli (n = 150). Species identification was performed using an automated microbiology system. Nineteen predictors (sex, urinary leukocyte grade, and 17 routine laboratory variables) were used. Associations with E. coli UTI were examined using univariate and multivariable logistic regression. A Random Forest (RF) classifier was developed with SHapley Additive exPlanations (SHAP) for interpretability. Data were split using a stratified 70/30 train–test split; 5-fold stratified cross-validation within the training set was used for hyperparameter tuning, and final performance (discrimination and calibration) was reported on the held-out test set. RF was additionally benchmarked against regularized logistic regression, calibrated linear SVM, and gradient boosting using the same protocol.
ResultsE. coli accounted for 51.3% of isolates, followed by Enterococcus spp. (18.5%) and Klebsiella spp. (7.8%). Compared with non–E. coli cases, E. coli infections were more common in females and showed higher lymphocyte counts (LYM), alanine aminotransferase (ALT), and albumin (ALB) (all P E. coli remains the predominant pathogen among hospitalized UTIs. An explainable RF model using routine laboratory variables provided moderate, reproducible discrimination of E. coli vs non–E. coli UTIs and may support earlier decision-making while awaiting culture results.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcimb.2026.1740707.s005（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Explainable_machine_learning_for_early_detection_of_Escherichia_coli_urinary_tract_infections_integrating_SHAP_interpretation_and_bacterial_epidemiology_docx/31331611）。