表1_机器学习算法预测社区获得性肺炎不良结局的性能.DOCX

数据集获取（Figshare）

• 源站标题：Table1_Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia.DOCX
• DOI：10.3389/fbioe.2022.903426.s001
• 发布日期：2022-06-29
• 文件数：1 个
• 文件总体积：23.17 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Performance_of_Machine_Learning_Algorithms_for_Predicting_Adverse_Outcomes_in_Community-Acquired_Pneumonia_DOCX/20181722

命令行下载

curl -L -o "Table1_Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia.DOCX" "https://ndownloader.figshare.com/files/36083621"

文件清单

• Table1_Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia.DOCX（23.17 KB）

文件总体积 23.17 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: The ability to assess adverse outcomes in patients with community-acquired pneumonia (CAP) could improve clinical decision-making to enhance clinical practice, but the studies remain insufficient, and similarly, few machine learning (ML) models have been developed.
Objective: We aimed to explore the effectiveness of predicting adverse outcomes in CAP through ML models.
Methods: A total of 2,302 adults with CAP who were prospectively recruited between January 2012 and March 2015 across three cities in South America were extracted from DryadData. After a 70:30 training set: test set split of the data, nine ML algorithms were executed and their diagnostic accuracy was measured mainly by the area under the curve (AUC). The nine ML algorithms included decision trees, random forests, extreme gradient boosting (XGBoost), support vector machines, Naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. The adverse outcomes included hospital admission, mortality, ICU admission, and one-year post-enrollment status.
Results: The XGBoost algorithm had the best performance in predicting hospital admission. Its AUC reached 0.921, and accuracy, precision, recall, and F1-score were better than those of other models. In the prediction of ICU admission, a model trained with the XGBoost algorithm showed the best performance with AUC 0.801. XGBoost algorithm also did a good job at predicting one-year post-enrollment status. The results of AUC, accuracy, precision, recall, and F1-score indicated the algorithm had high accuracy and precision. In addition, the best performance was seen by the neural network algorithm when predicting death (AUC 0.831).
Conclusions: ML algorithms, particularly the XGBoost algorithm, were feasible and effective in predicting adverse outcomes of CAP patients. The ML models based on available common clinical features had great potential to guide individual treatment and subsequent clinical decisions.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fbioe.2022.903426.s001（source: https://frontiersin.figshare.com/articles/dataset/Table1_Performance_of_Machine_Learning_Algorithms_for_Predicting_Adverse_Outcomes_in_Community-Acquired_Pneumonia_DOCX/20181722）。