预测慢加急性肝衰竭中急性肾损伤的机器学习模型开发与验证：一项多模型比较研究

数据集获取（Figshare）

• 源站标题：Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study
• DOI：10.6084/m9.figshare.29986123.v1
• 发布日期：2025-08-26
• 文件数：1 个
• 文件总体积：13.22 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Development_and_validation_of_machine_learning_models_for_predicting_acute_kidney_injury_in_acute-on-chronic_liver_failure_a_multimodel_comparative_study/29986123

命令行下载

curl -L -o "irnf_a_2547262_sm0774.xlsx" "https://ndownloader.figshare.com/files/57425653"

文件清单

• irnf_a_2547262_sm0774.xlsx（13.22 KB）

文件总体积 13.22 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

Acute kidney injury (AKI) is one of the serious complications in acute-on-chronic liver failure (ACLF), and the mortality rate is very high. Early identification of high-risk patients is critical. Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms.
This retrospective study enrolled 1,076 adult patients diagnosed with ACLF, with AKI defined according to the International Club of Ascites criteria. Participants were randomly allocated into training (n = 753, 70%) and test (n = 323, 30%) sets. Six ML models were developed: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and extreme gradient boosting (XGBoost). The performance of each model was assessed using the area under the receiver operating characteristic curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), the calibration curve, and decision curve analysis.
Among participants, 250 (23.2%) developed AKI during hospitalization. Multivariate LR analyses identified ten significant variables in the training set: age, hypertension, total bilirubin, blood urea nitrogen, serum creatinine, blood uric acid, international normalized ratio, hepatic encephalopathy, abdominal infection, and sepsis. The RF model performed best in the test set (AUC-ROC = 0.899; AUC-PR = 0.806).
The ML models can be reliable tools for predicting AKI in patients with ACLF. The RF model performed the best and can help medical clinicians to better identify patients with high risk of AKI in ACLF.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.29986123.v1（source: https://tandf.figshare.com/articles/dataset/Development_and_validation_of_machine_learning_models_for_predicting_acute_kidney_injury_in_acute-on-chronic_liver_failure_a_multimodel_comparative_study/29986123）。