XGBoost在诊断脓毒症相关性血小板减少症方面优于其他机器学习模型：一项多中心回顾性研究

数据集获取（Figshare）

• 源站标题：Table 1_XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study.docx
• DOI：10.3389/fmed.2026.1715551.s002
• 发布日期：2026-02-09
• 文件数：1 个
• 文件总体积：17.15 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_XGBoost_outperforms_other_machine_learning_models_in_diagnosing_Sepsis-Associated_Thrombocytopenia_a_multicenter_retrospective_study_docx/31292305

命令行下载

curl -L -o "Table 1_XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study.docx" "https://ndownloader.figshare.com/files/61740766"

文件清单

• Table 1_XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study.docx（17.15 KB）

文件总体积 17.15 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

ObjectiveSepsis-associated thrombocytopenia is a frequent complication of sepsis and is associated with poor clinical outcomes. Accurate diagnosis remains challenging, and machine learning approaches may offer improved diagnostic performance. The objective of this study was to conduct a comparative assessment of the performance of machine learning models (Random Forest, Artificial Neural Networks, Extreme Gradient Boosting, and Naive Bayes) in diagnosing sepsis-associated thrombocytopenia and to evaluate their diagnostic performance based on predefined performance criteria.
MethodsThis retrospective cross-sectional study was conducted at two centers and utilized data from 1,447 sepsis patients extracted from electronic health records between January 2013 and December 2023. The dataset comprised demographic and clinical attributes together with laboratory test results. The data were partitioned into training and test sets in an 80:20 ratio. All models were trained on the training set, and 10-fold cross-validation was applied within the training set to assess internal performance consistency. Model performance was evaluated using accuracy, precision, F1 score, AUROC, and confusion matrix. The ten most significant variables were ranked using classifier-based feature importance and SHAP analysis.
ResultsThrombocytopenia occurred in 772 (53.4%) of the 1,447 sepsis patients. Cross-validation results indicated that XGBoost exhibited superior performance, achieving an accuracy of 91.10% and an F1 score of 92.31%. ANN and RF achieved accuracies of 90.32% and 83.75%, with F1 scores of 91.51% and 85.32%, respectively. The AUROC values for cross-validation and test sets of RF, ANN, XGBoost, and Naive Bayes models exceeded 90%.
ConclusionAmong the evaluated algorithms, the XGBoost model demonstrated superior performance, achieving an AUROC of 98.60% in cross-validation and 97.50% in the test set, indicating the strongest performance metrics within internal validation.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2026.1715551.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_XGBoost_outperforms_other_machine_learning_models_in_diagnosing_Sepsis-Associated_Thrombocytopenia_a_multicenter_retrospective_study_docx/31292305）。