使用机器学习预测肺切除术后并发症：一项10年研究-41081

数据集获取（Figshare）

• 源站标题：Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study
• DOI：10.6084/m9.figshare.28743008.v1
• 发布日期：2025-04-07
• 文件数：3 个
• 文件总体积：8.49 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Predicting_postoperative_complications_after_pneumonectomy_using_machine_learning_a_10-year_study/28743008

命令行下载

curl -L -o "PCNC Graphical abstract.tif" "https://ndownloader.figshare.com/files/53470673"
curl -L -o "Supplemental_20240806.doc" "https://ndownloader.figshare.com/files/53470676"
curl -L -o "TRIPODAI_checklist.pdf" "https://ndownloader.figshare.com/files/53470679"

文件清单

• PCNC Graphical abstract.tif（6.90 MB）
• Supplemental_20240806.doc（475.14 KB）
• TRIPODAI_checklist.pdf（1.12 MB）

文件总体积 8.49 MB（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

Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.
We aimed to investigate independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer.
This study used data from a previous retrospective study of 16,368 patients with lung cancer (training set: 11,458; validation set: 4,910) with American Standards Association physical statuses I–IV who underwent surgery. Postoperative information was collected from electronic medical records to help build models based on cause-and-effect and statistical data, potentially revealing hidden dependencies between factors and diseases in a big data environment. The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). A predictive nomogram was built and receiver operating characteristics were used to assess the validity of the model. The discriminative power and clinical validity were assessed using calibration and decision-making curve analyses.
Multivariate logistic regression analysis revealed that age, surgery duration, intraoperative intercostal nerve block, postoperative patient-controlled analgesia, bronchial blocker use and sufentanil use were independent predictors of PCNC. Random forest was identified as the optimal model with an area under the curve of 0.898 in the training set and 0.752 in the validation set, confirming the excellent prediction accuracy of the nomogram. All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks.
The combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28743008.v1（source: https://tandf.figshare.com/articles/dataset/Predicting_postoperative_complications_after_pneumonectomy_using_machine_learning_a_10-year_study/28743008）。