DataSheet_3：ICU重症感染相关免疫生物标志物人工神经网络分类模型解释结果

数据集获取（Figshare）

• 源站标题：DataSheet_3_Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.pdf
• DOI：10.3389/fimmu.2023.1137850.s003
• 发布日期：2023-03-09
• 文件数：1 个
• 文件总体积：165.04 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_3_Model-interpreted_outcomes_of_artificial_neural_networks_classifying_immune_biomarkers_associated_with_severe_infections_in_ICU_pdf/22239208

命令行下载

curl -L -o "DataSheet_3_Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.pdf" "https://ndownloader.figshare.com/files/39525655"

文件清单

• DataSheet_3_Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.pdf（165.04 KB）

文件总体积 165.04 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

IntroductionMillions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients.
MethodsIn our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.
ResultsWe isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.
DiscussionIn conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2023.1137850.s003（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_3_Model-interpreted_outcomes_of_artificial_neural_networks_classifying_immune_biomarkers_associated_with_severe_infections_in_ICU_pdf/22239208）。