心脏瓣膜手术后死亡率预测的机器学习列线图开发与验证数据集

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of machine learning nomograms for predicting mortality after cardiac valve surgery.docx
• DOI：10.3389/fmed.2026.1779140.s002
• 发布日期：2026-03-27
• 文件数：1 个
• 文件总体积：16.13 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_machine_learning_nomograms_for_predicting_mortality_after_cardiac_valve_surgery_docx/31867957

命令行下载

curl -L -o "Table 1_Development and validation of machine learning nomograms for predicting mortality after cardiac valve surgery.docx" "https://ndownloader.figshare.com/files/63164245"

文件清单

• Table 1_Development and validation of machine learning nomograms for predicting mortality after cardiac valve surgery.docx（16.13 KB）

文件总体积 16.13 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

ObjectiveTo develop and validate machine learning (ML) models for predicting mortality after heart valve surgery and compare their performance to the conventional EuroSCORE II, with the final goal of creating clinically accessible and easy-to-use nomograms.
MethodsThis multicenter, retrospective cohort study included 935 adult patients who underwent heart valve surgery. All-cause mortality at in-hospital, 30-day, and 365-day post-operative intervals were the primary outcomes. The Boruta algorithm was employed for feature selection. Five models, Logistic Regression, XGBoost, Random Forest, Extra Trees, and EuroSCORE II (as a benchmark), were developed on a 70% training set and validated on a 30% hold-out test set. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (ROC AUC), sensitivity, and specificity. The best-performing model for each endpoint was converted into a nomogram.
ResultsMachine learning models demonstrated strong discriminative performance across all endpoints. For in-hospital mortality, the Extra Trees model achieved the highest discrimination (ROC-AUC 0.858). For 30-day mortality, Logistic Regression showed the best performance (ROC-AUC 0.800), substantially exceeding EuroSCORE II (ROC-AUC 0.610). For 365-day mortality, predictive performance was comparable across models, with EuroSCORE II demonstrating similar discrimination (ROC-AUC 0.787). Key predictive features consistently included age and biomarkers reflecting cardiac stress, renal function, and hepatic function. The derived nomograms exhibited good discrimination and calibration in both internal and external validation cohorts.
ConclusionMachine learning models, particularly ensemble approaches, improved short-term mortality prediction following valve surgery compared with EuroSCORE II. The developed nomograms provide a practical and interpretable tool for individualized perioperative risk stratification.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2026.1779140.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_machine_learning_nomograms_for_predicting_mortality_after_cardiac_valve_surgery_docx/31867957）。