创伤患者不良结局风险预测模型的开发与验证

数据集获取（Figshare）

• 源站标题：Development and validation of risk prediction model for adverse outcomes in trauma patients
• DOI：10.6084/m9.figshare.26779780.v1
• 发布日期：2024-08-19
• 文件数：4 个
• 文件总体积：10.87 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Development_and_validation_of_risk_prediction_model_for_adverse_outcomes_in_trauma_patients/26779780

命令行下载

curl -L -o "SUPPLEMENT (7).docx" "https://ndownloader.figshare.com/files/48647725"
curl -L -o "eFigure 1.jpeg" "https://ndownloader.figshare.com/files/48647728"
curl -L -o "eFigure 2jpeg.jpeg" "https://ndownloader.figshare.com/files/48647731"
curl -L -o "eFigure 3.jpeg" "https://ndownloader.figshare.com/files/48647734"

文件清单

• SUPPLEMENT (7).docx（24.72 KB）
• eFigure 1.jpeg（3.61 MB）
• eFigure 2jpeg.jpeg（4.16 MB）
• eFigure 3.jpeg（3.07 MB）

文件总体积 10.87 MB（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

The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis.
To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China.
This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA).
Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well.
This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.26779780.v1（source: https://tandf.figshare.com/articles/dataset/Development_and_validation_of_risk_prediction_model_for_adverse_outcomes_in_trauma_patients/26779780）。