表5：使用机器学习技术识别骨科创伤患者免疫抑制状态——持续性危重症——的生物标志物及预测

数据集获取（Figshare）

• 源站标题：Table_5_Biological signatures and prediction of an immunosuppressive status—persistent critical illness—among orthopedic trauma patients using machine learning techniques.docx
• DOI：10.3389/fimmu.2022.979877.s017
• 发布日期：2022-10-17
• 文件数：1 个
• 文件总体积：20.70 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_5_Biological_signatures_and_prediction_of_an_immunosuppressive_status_persistent_critical_illness_among_orthopedic_trauma_patients_using_machine_learning_techniques_docx/21346992

命令行下载

curl -L -o "Table_5_Biological signatures and prediction of an immunosuppressive status—persistent critical illness—among orthopedic trauma patients using machine.docx" "https://ndownloader.figshare.com/files/37889151"

文件清单

• Table_5_Biological signatures and prediction of an immunosuppressive status—persistent critical illness—among orthopedic trauma patients using machine.docx（20.70 KB）

文件总体积 20.70 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

BackgroundPersistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, and to construct a reliable prediction model for patients who had suffered orthopedic trauma using machine learning techniques.
MethodsThis study enrolled 1257 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Lymphocytes were tracked from ICU admission to more than 20 days following admission to examine the dynamic changes over time. Over 40 possible variables were gathered for investigation. Patients were split 80:20 at random into a training cohort (n=1035) and an internal validation cohort (n=222). Four machine learning algorithms, including random forest, gradient boosting machine, decision tree, and support vector machine, and a logistic regression technique were utilized to train and optimize models using data from the training cohort. Patients in the internal validation cohort were used to validate models, and the optimal one was chosen. Patients from two large teaching hospitals were used for external validation (n=113). The key metrics that used to assess the prediction performance of models mainly included discrimination, calibration, and clinical usefulness. To encourage clinical application based on the optimal machine learning-based model, a web-based calculator was developed.
Results16.0% (201/1257) of all patients had PerCI in the MIMIC-III database. The means of lymphocytes (%) were consistently below the normal reference range across the time among PerCI patients (around 10.0%), whereas in patients without PerCI, the number of lymphocytes continued to increase and began to be in normal range on day 10 following ICU admission. Subgroup analysis demonstrated that patients with PerCI were in a more serious health condition at admission since those patients had worse nutritional status, more electrolyte imbalance and infection-related comorbidities, and more severe illness scores. Eight variables, including albumin, serum calcium, red cell volume distributing width (R

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2022.979877.s017（source: https://frontiersin.figshare.com/articles/dataset/Table_5_Biological_signatures_and_prediction_of_an_immunosuppressive_status_persistent_critical_illness_among_orthopedic_trauma_patients_using_machine_learning_techniques_docx/21346992）。