术后ICU患者耐碳青霉烯类鲍曼不动杆菌感染预测模型数据集（表1）

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of an interpretable machine learning–based model for predicting carbapenem-resistant Acinetobacter baumannii infection in postoperative ICU patients: a retrospective cohort study.docx
• DOI：10.3389/fcimb.2026.1788229.s001
• 发布日期：2026-03-25
• 文件数：1 个
• 文件总体积：1.17 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_an_interpretable_machine_learning_based_model_for_predicting_carbapenem-resistant_Acinetobacter_baumannii_infection_in_postoperative_ICU_patients_a_retrospective_cohort_study_docx/31849963

命令行下载

curl -L -o "Table 1_Development and validation of an interpretable machine learning–based model for predicting carbapenem-resistant Acinetobacter baumannii infect.docx" "https://ndownloader.figshare.com/files/63079564"

文件清单

• Table 1_Development and validation of an interpretable machine learning–based model for predicting carbapenem-resistant Acinetobacter baumannii infect.docx（1.17 MB）

文件总体积 1.17 MB（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

BackgroundCarbapenem-resistant Acinetobacter baumannii (CRAB) is a major cause of healthcare-associated infections and is associated with poor outcomes in intensive care units, particularly among postoperative patients. However, predictive tools for early identification of high-risk postoperative intensive care units (ICU) patients remain scarce.
MethodsWe conducted a retrospective cohort study including 2,195 postoperative ICU patients. Clinically available demographic, treatment-related, and laboratory variables were used to develop eight machine learning models. Feature selection was performed using Boruta, and model interpretability was enhanced using Shapley Additive Explanations (SHAP) analysis. Model performance was evaluated in an independent test set using the area under the receiver operating characteristic curve (AUC), with sensitivity analyses performed using reduced feature sets.
ResultsAmong 2,195 postoperative ICU patients, 694 (31.6%) developed CRAB infection. Patients with CRAB infection had significantly longer ICU stays, greater exposure to invasive procedures, higher antimicrobial use, and worse laboratory profiles than non-infected patients. Using 19 features selected by the Boruta algorithm, all eight machine learning models achieved good discrimination in the test set (AUC > 0.83). Gradient Boosting demonstrated the best overall performance, with an AUC of 0.867 (95% CI: 0.836–0.892), good calibration, and the highest net clinical benefit. SHAP analysis identified duration of mechanical ventilation, central venous catheterization, ICU length of stay (LOS), and carbapenem exposure as the most influential predictors. Sensitivity analyses showed that models using only the top 10 or top 5 SHAP-ranked features achieved performance comparable to the full model, supporting the feasibility of feature reduction for clinical application.
ConclusionsThis study provides an interpretable and clinically applicable framework for early risk assessment of CRAB infection in postoperative ICU patients, supporting targeted prevention strategies and more rational antimicrobial stewardship.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcimb.2026.1788229.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_an_interpretable_machine_learning_based_model_for_predicting_carbapenem-resistant_Acinetobacter_baumannii_infection_in_postoperative_ICU_patients_a_retrospective_cohort_study_docx/31849963）。