ICU败血症相关性血小板减少症风险预测模型数据

数据集获取（Figshare）

• 源站标题：Table_1_Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm.DOCX
• DOI：10.3389/fmed.2022.837382.s004
• 发布日期：2022-01-27
• 文件数：1 个
• 文件总体积：28.82 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Prediction_Models_for_Sepsis-Associated_Thrombocytopenia_Risk_in_Intensive_Care_Units_Based_on_a_Machine_Learning_Algorithm_DOCX/19075325

命令行下载

curl -L -o "Table_1_Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm.DOCX" "https://ndownloader.figshare.com/files/33902027"

文件清单

• Table_1_Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm.DOCX（28.82 KB）

文件总体积 28.82 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

Sepsis-associated thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU), which significantly increases the mortality rate and leads to poor prognosis of diseases. Machine learning (ML) is widely used in disease prediction in critically ill patients. Here, we aimed to establish prediction models for platelet decrease and severe platelet decrease in ICU patients with sepsis based on four common ML algorithms and identify the best prediction model. The research subjects were 1,455 ICU sepsis patients admitted to Dongyang People’'s Hospital affiliated with Wenzhou Medical University from January 1, 2015, to October 31, 2019. Basic clinical demographic information, biochemical indicators, and clinical outcomes were recorded. The prediction models were based on four ML algorithms: random forest, neural network, gradient boosting machine, and Bayesian algorithms. Thrombocytopenia was found to occur in 732 patients (49.7%). The mechanical ventilation time and length of ICU stay were longer, and the mortality rate was higher for the thrombocytopenia group than for the non-thrombocytopenia group. The models were validated on an online international database (Medical Information Mart for Intensive Care III). The areas under the receiver operating characteristic curves (AUCs) of the four models for the prediction of thrombocytopenia were between 0.54 and 0.72. The AUCs of the models for the prediction of severe thrombocytopenia were between 0.70 and 0.77. The neural network and gradient boosting machine models effectively predicted the occurrence of SAT, and the Bayesian models had the best performance in predicting severe thrombocytopenia. Therefore, these models can be used to identify such high-risk patients at an early stage and guide individualized clinical treatment, to improve the prognosis of diseases.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.837382.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Prediction_Models_for_Sepsis-Associated_Thrombocytopenia_Risk_in_Intensive_Care_Units_Based_on_a_Machine_Learning_Algorithm_DOCX/19075325）。