基于非碳青霉烯类药敏谱的耐碳青霉烯肺炎克雷伯菌血流感染机器学习早期预警模型的开发与验证

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of a machine learning–based early warning model for carbapenem-resistant Klebsiella pneumoniae bloodstream infections using non-carbapenem susceptibility profiles.docx
• DOI：10.3389/fmicb.2026.1807076.s002
• 发布日期：2026-04-01
• 文件数：1 个
• 文件总体积：13.97 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_based_early_warning_model_for_carbapenem-resistant_Klebsiella_pneumoniae_bloodstream_infections_using_non-carbapenem_susceptibility_profiles_docx/31916121

命令行下载

curl -L -o "Table 1_Development and validation of a machine learning–based early warning model for carbapenem-resistant Klebsiella pneumoniae bloodstream infectio.docx" "https://ndownloader.figshare.com/files/63378363"

文件清单

• Table 1_Development and validation of a machine learning–based early warning model for carbapenem-resistant Klebsiella pneumoniae bloodstream infectio.docx（13.97 KB）

文件总体积 13.97 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

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a major cause of bloodstream infections with limited therapeutic options. Definitive carbapenem susceptibility results are often obtained late in the laboratory workflow, highlighting the need for early warning tools to support timely risk stratification. We analyzed multicenter surveillance data from the Bloodstream Infection Resistance Surveillance Consortium, including 13,072 K. pneumoniae bloodstream isolates collected from 60 hospitals in China between 2014 and 2023. Non-carbapenem antimicrobial susceptibility interpretations were used as model inputs, while carbapenem results were excluded. Data were split chronologically into training (2014–2021), validation (2022), and test (2023) sets. Logistic regression, XGBoost, and CatBoost models were developed and evaluated using discrimination, calibration, decision curve analysis (DCA), and SHAP-based interpretability. XGBoost demonstrated the best overall performance, achieving higher discrimination with a ROC-AUC of 0.993 and a PR-AUC of 0.973 on the test set, along with superior calibration as reflected by the lowest Brier score (0.018). At a sensitivity-targeted threshold (~0.95), XGBoost maintained high sensitivity (0.924), excellent specificity (0.989), and a favorable positive predictive value (0.944), while preserving a high negative predictive value (0.986). SHAP analysis identified key non-carbapenem susceptibility features contributing to CRKP risk prediction. Non-carbapenem susceptibility profiles enable early identification of CRKP bloodstream infections. A machine learning–based early warning model, particularly XGBoost, may support laboratory-based risk stratification and complement conventional susceptibility testing.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmicb.2026.1807076.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_based_early_warning_model_for_carbapenem-resistant_Klebsiella_pneumoniae_bloodstream_infections_using_non-carbapenem_susceptibility_profiles_docx/31916121）。