重症铜绿假单胞菌感染患者死亡风险预测与解释机器学习框架数据集

数据集获取（Figshare）

• 源站标题：Table_1_A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa.xlsx
• DOI：10.3389/fmed.2022.942356.s001
• 发布日期：2022-07-25
• 文件数：1 个
• 文件总体积：13.75 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_prediction_and_interpretation_machine_learning_framework_of_mortality_risk_among_severe_infection_patients_with_pseudomonas_aeruginosa_xlsx/20366247

命令行下载

curl -L -o "Table_1_A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa.xlsx" "https://ndownloader.figshare.com/files/36405765"

文件清单

• Table_1_A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa.xlsx（13.75 KB）

文件总体积 13.75 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

Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.942356.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_prediction_and_interpretation_machine_learning_framework_of_mortality_risk_among_severe_infection_patients_with_pseudomonas_aeruginosa_xlsx/20366247）。