幼年特发性关节炎甲氨蝶呤治疗反应预测表1数据集

数据集获取（Figshare）

• 源站标题：Table_1_Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.docx
• DOI：10.3389/fphar.2019.01155.s001
• 发布日期：2019-10-07
• 文件数：1 个
• 文件总体积：30.50 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Early_and_Accurate_Prediction_of_Clinical_Response_to_Methotrexate_Treatment_in_Juvenile_Idiopathic_Arthritis_Using_Machine_Learning_docx/9944837

命令行下载

curl -L -o "Table_1_Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.docx" "https://ndownloader.figshare.com/files/17915963"

文件清单

• Table_1_Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.docx（30.50 KB）

文件总体积 30.50 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

Background and Aims: Accurately predicting the response to methotrexate (MTX) in juvenile idiopathic arthritis (JIA) patients before administration is the key point to improve the treatment outcome. However, no simple and reliable prediction model has been identified. Here, we aimed to develop and validate predictive models for the MTX response to JIA using machine learning based on electronic medical record (EMR) before and after administering MTX.
Materials and Methods: Data of 362 JIA patients with MTX mono-therapy were retrospectively collected from EMR between January 2008 and October 2018. DAS44/ESR-3 simplified standard was used to evaluate the MTX response. Extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and logistic regression (LR) algorithms were applied to develop and validate models with 5-fold cross-validation on the randomly split training and test set. Data of 13 patients additionally collected were used for external validation.
Results: The XGBoost screened out the optimal 10 pre-administration features and 6 mix-variables. The XGBoost established the best model based on the 10 pre-administration variables. The performances were accuracy 91.78%, sensitivity 90.70%, specificity 93.33%, AUC 97.00%, respectively. Similarly, the XGBoost developed a better model based on the 6 mix-variables, whose performances were accuracy 94.52%, sensitivity 95.35%, specificity 93.33%, AUC 99.00%, respectively.
Conclusion: Based on common EMR data, we developed two MTX response predictive models with excellent performance in JIA using machine learning. These models can predict the MTX efficacy early and accurately, which provides powerful decision support for doctors to make or adjust therapeutic scheme before or after treatment.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphar.2019.01155.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Early_and_Accurate_Prediction_of_Clinical_Response_to_Methotrexate_Treatment_in_Juvenile_Idiopathic_Arthritis_Using_Machine_Learning_docx/9944837）。