表1_术后肺炎预测的可解释机器学习模型开发与验证

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of interpretable machine learning models for postoperative pneumonia prediction.docx
• DOI：10.3389/fpubh.2024.1468504.s001
• 发布日期：2024-12-11
• 文件数：1 个
• 文件总体积：17.12 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_interpretable_machine_learning_models_for_postoperative_pneumonia_prediction_docx/28005791

命令行下载

curl -L -o "Table 1_Development and validation of interpretable machine learning models for postoperative pneumonia prediction.docx" "https://ndownloader.figshare.com/files/51094454"

文件清单

• Table 1_Development and validation of interpretable machine learning models for postoperative pneumonia prediction.docx（17.12 KB）

文件总体积 17.12 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

BackgroundPostoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients’ prognosis and even their lives. This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.
ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. By evaluating the performance differences among these machine learning models, this study aims to assist clinicians in early prediction and diagnosis of POP, providing optimal interventions and treatments.
MethodsRetrospective data from electronic medical records was collected for 264 patients diagnosed with postoperative pneumonia and 264 healthy control surgical patients. Through correlation screening, chi-square tests, and feature importance ranking, 47 variables were narrowed down to 5 potential predictive factors based on the main cohort of 528 patients. Nine machine learning models, including k-nearest neighbors, support vector machine, random forest, decision tree, gradient boosting machine, adaptive boosting, naive bayes, general linear model, and linear discriminant analysis, were developed and validated to predict postoperative pneumonia. Model performance was evaluated using the area under the receiver operating curve, sensitivity, specificity, accuracy, precision, recall, and F1 score. A distribution plot of feature importance and feature interaction was obtained to interpret the machine learning models.
ResultsAmong 17,190 surgical patients, 264 (1.54%) experienced postoperative pneumonia, which resulted in adverse outcomes such as prolonged hospital stay, increased ICU admission rates, and mortality. We successfully established nine machine learning models for predicting postoperative pneumonia in surgical patients, with the general linear model demonstrating the best overall performance. The AUC of the general linear model on the testing set was 0.877, with an accuracy of 0.82, specificity of 0.89, sensitivity of 0.74, precision of 0.88, and F1 score of 0.80. Our study revealed that the duration of bed rest, unplanned re

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2024.1468504.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_interpretable_machine_learning_models_for_postoperative_pneumonia_prediction_docx/28005791）。