数据表1_基于机器学习的肺炎相关急性呼吸窘迫综合征预后预测模型.pdf

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome.pdf
• DOI：10.3389/fmed.2025.1582426.s002
• 发布日期：2025-07-03
• 文件数：1 个
• 文件总体积：239.77 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning-based_prognostic_prediction_model_of_pneumonia-associated_acute_respiratory_distress_syndrome_pdf/29468489

命令行下载

curl -L -o "Data Sheet 1_Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome.pdf" "https://ndownloader.figshare.com/files/55953107"

文件清单

• Data Sheet 1_Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome.pdf（239.77 KB）

文件总体积 239.77 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

ObjectiveThis study aimed to construct a machine learning predictive model for prognostic analysis of patients with p- ARDS.
MethodsIn this single-center retrospective study, 230 patients with p- ARDS admitted to the RICU of the second affiliated hospital of Chongqing Medical University from January 2020 to November 2024 were included. Patients were divided into survival group and death group according to the 28-day prognosis results. All patients’ clinical data were first results within 24 h of admission. 20% of the total samples were randomly selected as the test set, and the remaining samples were used as the training set for crossvalidation, and six different models were constructed, including Logistic Regression, Random Forest, NaiveBayes, SVM, XGBoost and Adaboost. The AUC value, AP value, accuracy, sensitivity, specificity, Brier score, and F 1 score were used to evaluate the performance of the models and pick the optimal model. Finally, the SHAP feature importance map was drawn to explain the optimal model.
Results10 key variables, namely LAR, Lac, pH, age, PO2/FiO2, ALB, BMI, TP, PT, DBIL were screened using the filtration method. The importance ranking of the variables showed that age was the most important variable. Among the six algorithms, the performance of the SVM algorithm is significantly better than that of other algorithms. The AUC, AP, Accuracy, Sensitivity, Specificity, Brier Score, and F1 Scores in the test set were 0.77, 0.67, 0.74, 0.60, 0.81, 0.19, and 0.60, respectively. This indicates the potential value of machine learning models in predicting the prognosis of patients with p- ARDS.
ConclusionThis study developed and visualized a machine learning model constructed based on 10 common clinical features for predicting 28-day mortality in patients with p- ARDS. The model shows good predictive performance and achieves explanatory analysis in combination with SHAP and LIME methods, providing a reliable mortality risk assessment tool for p- ARDS.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1582426.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning-based_prognostic_prediction_model_of_pneumonia-associated_acute_respiratory_distress_syndrome_pdf/29468489）。