PRIME评分：基于机器学习进行二尖瓣手术患者死亡风险预测

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine Learning-Based Personalized Risk Prediction Model for Mortality of Patients Undergoing Mitral Valve Surgery: The PRIME Score.pdf
• DOI：10.3389/fcvm.2022.866257.s001
• 发布日期：2022-04-01
• 文件数：1 个
• 文件总体积：865.98 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning-Based_Personalized_Risk_Prediction_Model_for_Mortality_of_Patients_Undergoing_Mitral_Valve_Surgery_The_PRIME_Score_pdf/19492322

命令行下载

curl -L -o "Data_Sheet_1_Machine Learning-Based Personalized Risk Prediction Model for Mortality of Patients Undergoing Mitral Valve Surgery: The PRIME Score.pdf" "https://ndownloader.figshare.com/files/34640063"

文件清单

• Data_Sheet_1_Machine Learning-Based Personalized Risk Prediction Model for Mortality of Patients Undergoing Mitral Valve Surgery: The PRIME Score.pdf（865.98 KB）

文件总体积 865.98 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

BackgroundMitral valve surgery (MVS) is an effective treatment for mitral valve diseases. There is a lack of reliable personalized risk prediction models for mortality in patients undergoing mitral valve surgery. Our aim was to develop a risk stratification system to predict all-cause mortality in patients after mitral valve surgery.
MethodsDifferent machine learning models for the prediction of all-cause mortality were trained on a derivation cohort of 1,883 patients undergoing mitral valve surgery [split into a training cohort (70%) and internal validation cohort (30%)] to predict all-cause mortality. Forty-five clinical variables routinely evaluated at discharge were used to train the models. The best performance model (PRIME score) was tested in an externally validated cohort of 220 patients undergoing mitral valve surgery. The model performance was evaluated according to the area under the curve (AUC). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were compared with existing risk strategies.
ResultsAfter a median follow-up of 2 years, there were 133 (7.063%) deaths in the derivation cohort and 17 (7.727%) deaths in the validation cohort. The PRIME score showed an AUC of 0.902 (95% confidence interval [CI], 0.849–0.956) in the internal validation cohort and 0.873 (95% CI: 0.769–0.977) in the external validation cohort. In the external validation cohort, the performance of the PRIME score was significantly improved compared with that of the existing EuroSCORE II (NRI = 0.550, [95% CI 0.001–1.099], P = 0.049, IDI = 0.485, [95% CI 0.230–0.741], P Machine learning-based model (the PRIME score) that integrate clinical, demographic, imaging, and laboratory features demonstrated superior performance for the prediction of mortality patients after mitral valve surgery compared with the traditional risk model EuroSCORE II.
Clinical Trial Registration[http://www.clinicaltrials.gov], identifier [NCT05141292].

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.866257.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning-Based_Personalized_Risk_Prediction_Model_for_Mortality_of_Patients_Undergoing_Mitral_Valve_Surgery_The_PRIME_Score_pdf/19492322）。