Data_Sheet_1_基于递归特征消除算法开发与验证慢性乙型肝炎严重急性加重患者慢加急性肝衰竭新型风险预测模型.DOCX

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients With Severe Acute Exacerbation.DOCX
• DOI：10.3389/fmed.2021.748915.s001
• 发布日期：2021-11-01
• 文件数：1 个
• 文件总体积：27.43 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_Validation_of_a_Novel_Risk_Prediction_Model_Using_Recursive_Feature_Elimination_Algorithm_for_Acute-on-Chronic_Liver_Failure_in_Chronic_Hepatitis_B_Patients_With_Severe_Acute_Exacerbation_DOCX/16911016

命令行下载

curl -L -o "Data_Sheet_1_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Fail.DOCX" "https://ndownloader.figshare.com/files/31278901"

文件清单

• Data_Sheet_1_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Fail.DOCX（27.43 KB）

文件总体积 27.43 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

Background: Patients with chronic hepatitis B (CHB) with severe acute exacerbation (SAE) are at a progression stage of acute-on-chronic liver failure (ACLF) but uniform models for predicting ACLF occurrence are lacking. We aimed to present a risk prediction model to early identify the patients at a high risk of ACLF and predict the survival of the patient.
Methods: We selected the best variable combination using a novel recursive feature elimination algorithm to develop and validate a classification regression model and also an online application on a cloud server from the training cohort with a total of 342 patients with CHB with SAE and two external cohorts with a sample size of 96 and 65 patients, respectively.
Findings: An excellent prediction model called the PATA model including four predictors, prothrombin time (PT), age, total bilirubin (Tbil), and alanine aminotransferase (ALT) could achieve an area under the receiver operating characteristic curve (AUC) of 0.959 (95% CI 0.941–0.977) in the development set, and AUC of 0.932 (95% CI 0.876–0.987) and 0.905 (95% CI 0.826–0.984) in the two external validation cohorts, respectively. The calibration curve for risk prediction probability of ACLF showed optimal agreement between prediction by PATA model and actual observation. After predictive stratification into different risk groups, the C-index of predictive 90-days mortality was 0.720 (0.675–0.765) for the PATA model, 0.549 (0.506–0.592) for the end-stage liver disease score model, and 0.648 (0.581–0.715) for Child–Turcotte–Pugh scoring system.
Interpretation: The highlypredictive risk model and easy-to-use online application can accurately predict the risk of ACLF with a poor prognosis. They may facilitate risk communication and guidetherapeutic options.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2021.748915.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_Validation_of_a_Novel_Risk_Prediction_Model_Using_Recursive_Feature_Elimination_Algorithm_for_Acute-on-Chronic_Liver_Failure_in_Chronic_Hepatitis_B_Patients_With_Severe_Acute_Exacerbation_DOCX/16911016）。